add 5 vm op

mindspore/ccsrc/backend/kernel_compiler/tbe/tbe_adapter.cc

@@ -37,6 +37,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"re_lu6", "relu6"},
   {"re_lu6_grad", "relu6_grad"},
   {"re_lu", "relu"},
+  {"reverse_v2", "reverse_v2_d"},
   {"re_luv2", "relu_v2"},
   {"p_re_lu", "prelu"},
   {"p_re_lu_grad", "prelu_grad"},

mindspore/ops/_grad/grad_array_ops.py

@@ -377,6 +377,18 @@ def get_bprop_pack(self):
     return bprop
 
 
+@bprop_getters.register(P.ReverseV2)
+def get_bprop_reverse_v2(self):
+    """Generate bprop for ReverseV2"""
+    axis = self.axis
+
+    def bprop(x, out, dout):
+        reverse_grad = P.ReverseV2(axis)
+        dx = reverse_grad(dout)
+        return (dx,)
+
+    return  bprop
+
 @bprop_getters.register(P.Unpack)
 def get_bprop_unpack(self):
     """Generate bprop for Unpack"""
@@ -495,6 +507,16 @@ def get_bprop_scatter_nd_update(self):
     return bprop
 
 
+@bprop_getters.register(P.ScatterNonAliasingAdd)
+def get_bprop_scatter_non_aliasing_add_update(self):
+    """Generate bprop for ScatterNonAliasingAdd"""
+    op = P.GatherNd()
+
+    def bprop(x, indices, update, out, dout):
+        return dout, zeros_like(indices), op(dout, indices)
+
+    return bprop
+
 @bprop_getters.register(P.TensorScatterUpdate)
 def get_bprop_tensor_scatter_update(self):
     """Generate bprop for TensorScatterUpdate"""
@@ -509,6 +531,7 @@ def get_bprop_tensor_scatter_update(self):
     return bprop
 
 
+
 @bprop_getters.register(P.ScatterMax)
 def get_bprop_scatter_max(self):
     """Generate bprop for ScatterMax"""

mindspore/ops/_op_impl/tbe/__init__.py

@@ -81,6 +81,9 @@ from .sub import _sub_tbe
 from .reduce_mean_d import _reduce_mean_d_tbe
 from .scatter_nd import _scatter_nd_tbe
 from .scatter_nd_d import _scatter_nd_d_tbe
+from .scatter_nd_add import _scatter_nd_add_tbe
+from .scatter_nd_sub import _scatter_nd_sub_tbe
+from .scatter_non_aliasing_add import _scatter_non_aliasing_add_tbe
 from .reduce_mean import _reduce_mean_tbe
 from .tile import _tile_tbe
 from .atomic_addr_clean import _atomic_addr_clean_tbe
@@ -93,6 +96,8 @@ from .bn_training_update_grad import _bn_training_update_grad_tbe
 from .bn_infer import _bn_infer_tbe
 from .bn_infer_grad import _bn_infer_grad_tbe
 from .reciprocal import _reciprocal_tbe
+from .reverse_v2_d import _reverse_v2_d_tbe
+from .rint import _rint_tbe
 from .strided_slice_d import _strided_slice_d_tbe
 from .strided_slice_grad_d import _strided_slice_grad_d_tbe
 from .split_d import _split_d_tbe

mindspore/ops/_op_impl/tbe/reverse_v2_d.py

@@ -0,0 +1,37 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ReverseV2D op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+reverse_v2_d_op_info = TBERegOp("ReverseV2") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("reverse_v2_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reverse_v2_d") \
+    .partial_flag(True) \
+    .op_pattern("dynamicFormat") \
+    .attr("axis", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.None_None, DataType.None_None) \
+    .get_op_info()
+
+
+@op_info_register(reverse_v2_d_op_info)
+def _reverse_v2_d_tbe():
+    """ReverseV2D TBE register"""
+    return

mindspore/ops/_op_impl/tbe/rint.py

@@ -0,0 +1,37 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Rint op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+rint_op_info = TBERegOp("Rint") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("rint.so") \
+    .compute_cost(10) \
+    .kernel_name("rint") \
+    .partial_flag(True) \
+    .op_pattern("formatAgnostic") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_None, DataType.F16_None) \
+    .dtype_format(DataType.F32_None, DataType.F32_None) \
+    .get_op_info()
+
+
+@op_info_register(rint_op_info)
+def _rint_tbe():
+    """Rint TBE register"""
+    return

mindspore/ops/_op_impl/tbe/scatter_nd_add.py

@@ -0,0 +1,42 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ScatterNdAdd op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+scatter_nd_add_op_info = TBERegOp("ScatterNdAdd") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_nd_add.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_nd_add") \
+    .partial_flag(True) \
+    .attr("use_locking", "optional", "bool", "all") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "indices", False, "required", "all") \
+    .input(2, "updates", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(scatter_nd_add_op_info)
+def _scatter_nd_add_tbe():
+    """ScatterNdAdd TBE register"""
+    return

mindspore/ops/_op_impl/tbe/scatter_nd_sub.py

@@ -0,0 +1,42 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ScatterNdSub op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+scatter_nd_sub_op_info = TBERegOp("ScatterNdSub") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_nd_sub.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_nd_sub") \
+    .partial_flag(True) \
+    .attr("use_locking", "optional", "bool", "all") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "indices", False, "required", "all") \
+    .input(2, "updates", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(scatter_nd_sub_op_info)
+def _scatter_nd_sub_tbe():
+    """ScatterNdSub TBE register"""
+    return

mindspore/ops/_op_impl/tbe/scatter_non_aliasing_add.py

@@ -0,0 +1,39 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ScatterNonAliasingAdd op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+scatter_non_aliasing_add_op_info = TBERegOp("ScatterNonAliasingAdd") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_non_aliasing_add.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_non_aliasing_add") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "indices", False, "required", "all") \
+    .input(2, "updates", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(scatter_non_aliasing_add_op_info)
+def _scatter_non_aliasing_add_tbe():
+    """ScatterNonAliasingAdd TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -28,6 +28,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         SameTypeShape, ScatterAdd, ScatterSub, ScatterMul, ScatterDiv, ScatterMax, ScatterMin,
                         ScatterUpdate, ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
                         Shape, Size, Slice, Split, TransShape, ParallelConcat,
+                        ScatterNdAdd, ScatterNdSub, ScatterNonAliasingAdd, ReverseV2, Rint,
                         Squeeze, StridedSlice, Tile, TensorScatterUpdate,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin, UnsortedSegmentProd,
                         UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
@@ -233,6 +234,11 @@ __all__ = [
     'ScatterNd',
     'ScatterMax',
     'ScatterMin',
+    'ScatterNdAdd',
+    'ScatterNdSub',
+    'ScatterNonAliasingAdd',
+    'ReverseV2',
+    'Rint',
     'ResizeNearestNeighbor',
     'HistogramFixedWidth',
     'Pad',

mindspore/ops/operations/array_ops.py

@@ -47,8 +47,8 @@ class _ScatterOp(PrimitiveWithInfer):
         ('indices', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T1),
         ('updates', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T)
     )
-    @staticmethod
-    def _check_scatter_shape(x_shape, indices_shape, updates_shape, prim_name):
+
+    def _check_scatter_shape(self, x_shape, indices_shape, updates_shape, prim_name):
         if updates_shape and updates_shape != indices_shape + x_shape[1:]:
             raise ValueError(f"For '{prim_name}', the shape of updates should be [] or "
                              f"updates_shape = indices_shape + x_shape[1:], but got x_shape: {x_shape}, "
@@ -61,7 +61,7 @@ class _ScatterOp(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['x', 'indices', 'updates'], outputs=['y'])
 
     def infer_shape(self, x_shape, indices_shape, updates_shape):
-        _ScatterOp._check_scatter_shape(x_shape, indices_shape, updates_shape, self.name)
+        self._check_scatter_shape(x_shape, indices_shape, updates_shape, self.name)
         return x_shape
 
     def infer_dtype(self, x_dtype, indices_dtype, updates_dtype):
@@ -71,6 +71,19 @@ class _ScatterOp(PrimitiveWithInfer):
         return x_dtype
 
 
+class _ScatterNdOp(_ScatterOp):
+    """
+    Define _ScatterNd operators
+    """
+    def _check_scatter_shape(self, x_shape, indices_shape, updates_shape, prim_name):
+        validator.check('the dimension of x', len(x_shape),
+                        'the dimension of indices', indices_shape[-1], Rel.GE)
+        if indices_shape[:-1] + x_shape[indices_shape[-1]:] != updates_shape:
+            raise ValueError(f"For '{prim_name}', the shape of updates should be [] or updates_shape = "
+                             f"indices_shape[:-1] + x_shape[indices_shape[-1]:], but got x_shape: {x_shape}, "
+                             f"indices_shape: {indices_shape}, updates_shape: {updates_shape}.")
+
+
 def _check_infer_attr_reduce(axis, keep_dims, prim_name):
     validator.check_value_type('keep_dims', keep_dims, [bool], prim_name)
     validator.check_value_type('axis', axis, [int, tuple], prim_name)
@@ -1759,6 +1772,75 @@ class Slice(PrimitiveWithInfer):
                 'value': None}
 
 
+class ReverseV2(PrimitiveWithInfer):
+    """
+    Reverse specific dimensions of a tensor.
+
+    Args:
+        axis (Union[tuple(int), list(int)): The indices of the dimensions to reverse.
+
+    Inputs:
+        - **input_x** (Tensor) - The target tensor.
+
+    Outputs:
+        Tensor, has the same shape and type as `input_x`.
+
+    Examples:
+        >>> input_x = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]), mindspore.int32)
+        >>> op = P.ReverseV2(axis=[1])
+        >>> output = op(input_x)
+        [[4, 3, 2, 1], [8, 7, 6, 5]]
+    """
+
+    @prim_attr_register
+    def __init__(self, axis):
+        validator.check_value_type('axis', axis, [list, tuple], self.name)
+        for i, each in enumerate(axis):
+            validator.check_value_type(f'axis[{i}]', each, [int], self.name)
+        self.axis = axis
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        dim = len(x_shape)
+        for i, each in enumerate(self.axis):
+            validator.check_int_range(f'axis[{i}]', each, -dim, dim, Rel.INC_LEFT, self.name)
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_tensor_type_same({'x': x_dtype}, (mstype.bool_,) + mstype.number_type, self.name)
+        return x_dtype
+
+
+class Rint(PrimitiveWithInfer):
+    """
+    Return element-wise integer closest to x.
+
+    Inputs:
+        - **input_x** (Tensor) - The target tensor, which must be one of the following types:
+          float16, float32.
+
+    Outputs:
+        Tensor, has the same shape and type as `input_x`.
+
+    Examples:
+        >>> input_x = Tensor(np.array([-1.6, -0.1, 1.5, 2.0]), mindspore.float32)
+        >>> op = P.Rint()
+        >>> output = op(input_x)
+        [-2., 0., 2., 2.]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_tensor_type_same({'x': x_dtype}, [mstype.float16, mstype.float32], self.name)
+        return x_dtype
+
+
 class Select(PrimitiveWithInfer):
     r"""
 
@@ -2404,7 +2486,7 @@ class ScatterUpdate(_ScatterOp):
         return x_dtype
 
 
-class ScatterNdUpdate(PrimitiveWithInfer):
+class ScatterNdUpdate(_ScatterNdOp):
     """
     Update tensor value by using input indices and value.
 
@@ -2429,11 +2511,7 @@ class ScatterNdUpdate(PrimitiveWithInfer):
         >>> op = P.ScatterNdUpdate()
         >>> output = op(input_x, indices, update)
     """
-    __mindspore_signature__ = (
-        ('x', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
-        ('indices', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T1),
-        ('value', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T)
-    )
+
 
     @prim_attr_register
     def __init__(self, use_locking=True):
@@ -2441,13 +2519,6 @@ class ScatterNdUpdate(PrimitiveWithInfer):
         validator.check_value_type('use_locking', use_locking, [bool], self.name)
         self.init_prim_io_names(inputs=['x', 'indices', 'value'], outputs=['y'])
 
-    def infer_shape(self, x_shape, indices_shape, value_shape):
-        validator.check('the dimension of x', len(x_shape),
-                        'the dimension of indices', indices_shape[-1], Rel.GE)
-        if indices_shape[:-1] + x_shape[indices_shape[-1]:] != value_shape:
-            raise ValueError("For 'ScatterNdUpdate', input value are not match with input indices.")
-        return x_shape
-
     def infer_dtype(self, x_dtype, indices_dtype, value_dtype):
         validator.check_tensor_type_same({'indices': indices_dtype}, [mstype.int32], self.name)
         args = {"x": x_dtype, "value": value_dtype}
@@ -2635,6 +2706,101 @@ class ScatterDiv(_ScatterOp):
     """
 
 
+class ScatterNdAdd(_ScatterNdOp):
+    """
+    Applies sparse addition to individual values or slices in a Tensor.
+
+    Using given values to update tensor value through the add operation, along with the input indices.
+    This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
+
+    Args:
+        use_locking (bool): Whether protect the assignment by a lock. Default: False.
+
+    Inputs:
+        - **input_x** (Parameter) - The target parameter.
+        - **indices** (Tensor) - The index to do add operation whose data type should be mindspore.int32.
+        - **updates** (Tensor) - The tensor doing the add operation with `input_x`,
+          the data type is same as `input_x`, the shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
+
+    Outputs:
+        Parameter, the updated `input_x`.
+
+    Examples:
+        >>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
+        >>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
+        >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
+        >>> scatter_nd_add = P.ScatterNdAdd()
+        >>> output = scatter_nd_add(input_x, indices, updates)
+        [1, 10, 9, 4, 12, 6, 7, 17]
+    """
+
+
+class ScatterNdSub(_ScatterNdOp):
+    """
+    Applies sparse subtraction to individual values or slices in a Tensor.
+
+    Using given values to update tensor value through the sub operation, along with the input indices.
+    This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
+
+    Args:
+        use_locking (bool): Whether protect the assignment by a lock. Default: False.
+
+    Inputs:
+        - **input_x** (Parameter) - The target parameter.
+        - **indices** (Tensor) - The index to do add operation whose data type should be mindspore.int32.
+        - **updates** (Tensor) - The tensor doing the sub operation with `input_x`,
+          the data type is same as `input_x`, the shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
+
+    Outputs:
+        Parameter, the updated `input_x`.
+
+    Examples:
+        >>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
+        >>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
+        >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
+        >>> scatter_nd_sub = P.ScatterNdSub()
+        >>> output = scatter_nd_sub(input_x, indices, updates)
+        [1, -6, -3, 4, -2, 6, 7, -1]
+    """
+
+
+class ScatterNonAliasingAdd(_ScatterNdOp):
+    """
+    Applies sparse addition to input using individual values or slices.
+
+    Using given values to update tensor value through the add operation, along with the input indices.
+    This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
+
+    Inputs:
+        - **input_x** (Parameter) - The target parameter.
+        - **indices** (Tensor) - The index to do add operation whose data type should be mindspore.int32.
+        - **updates** (Tensor) - The tensor doing the add operation with `input_x`,
+          the data type is same as `input_x`, the shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
+
+    Outputs:
+        Parameter, the updated `input_x`.
+
+    Examples:
+        >>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
+        >>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
+        >>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
+        >>> scatter_non_aliasing_add = P.ScatterNonAliasingAdd()
+        >>> output = scatter_non_aliasing_add(input_x, indices, updates)
+        [1, 10, 9, 4, 12, 6, 7, 17]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """Init ScatterNonAliasingAdd"""
+        self.init_prim_io_names(inputs=['x', 'indices', 'updates'], outputs=['y'])
+
+    def infer_dtype(self, x_dtype, indices_dtype, updates_dtype):
+        validator.check_tensor_type_same({'indices': indices_dtype}, [mstype.int32], self.name)
+        args = {"x": x_dtype, "updates": updates_dtype}
+        validator.check_tensor_type_same(args, [mstype.float16, mstype.float32, mstype.int32], self.name)
+        return x_dtype
+
+
 class SpaceToDepth(PrimitiveWithInfer):
     r"""
     Rearrange blocks of spatial data into depth.

tests/ut/python/ops/test_ops.py

@@ -237,6 +237,44 @@ class ScatterAdd(nn.Cell):
         return out
 
 
+class ScatterNonAliasingAdd(nn.Cell):
+    """ScatterNonAliasingAdd net definition"""
+
+    def __init__(self, ref_shape, dtype=np.float32):
+        super(ScatterNonAliasingAdd, self).__init__()
+        self.scatter_no_aliasing_add = P.ScatterNonAliasingAdd()
+        self.ref = Parameter(Tensor(np.ones(ref_shape, dtype)), name="ref")
+
+    def construct(self, indices, updates):
+        out = self.scatter_no_aliasing_add(self.ref, indices, updates)
+        return out
+
+
+class ScatterNdSub(nn.Cell):
+    """ScatterNdSub net definition"""
+
+    def __init__(self, ref_shape, dtype=np.float32):
+        super(ScatterNdSub, self).__init__()
+        self.scatter_nd_sub = P.ScatterNdSub()
+        self.ref = Parameter(Tensor(np.ones(ref_shape, dtype)), name="ref")
+
+    def construct(self, indices, updates):
+        out = self.scatter_nd_sub(self.ref, indices, updates)
+        return out
+
+class ScatterNdAdd(nn.Cell):
+    """ScatterNdAdd net definition"""
+
+    def __init__(self, ref_shape, dtype=np.float32):
+        super(ScatterNdAdd, self).__init__()
+        self.scatter_nd_add = P.ScatterNdAdd()
+        self.ref = Parameter(Tensor(np.ones(ref_shape, dtype)), name="ref")
+
+    def construct(self, indices, updates):
+        out = self.scatter_nd_add(self.ref, indices, updates)
+        return out
+
+
 class ScatterSub(nn.Cell):
     """ScatterSub net definition"""
 
@@ -1811,6 +1849,14 @@ test_case_array_ops = [
         'desc_const': [(2, 1, 1, 2)],
         'desc_inputs': [[2, 2, 2]],
         'desc_bprop': [[2, 2, 2, 4]]}),
+    ('ReverseV2', {
+        'block': P.ReverseV2(axis=[1]),
+        'desc_inputs': [(Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]).astype(np.float32)))],
+        'desc_bprop': [(Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]).astype(np.float32)))]}),
+    ('Rint', {
+        'block': P.Rint(),
+        'desc_inputs': [(Tensor(np.array([-1.6, -0.1, 1.5, 2.0]).astype(np.float32)))],
+        'skip': ['backward']}),
     ('ConcatV2_0', {
         'block': P.Concat(),
         'desc_inputs': [
@@ -2074,6 +2120,21 @@ test_case_other_ops = [
         'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
                         Tensor(np.array([2.0, 3.0, 4.0], np.float32))),
         'skip': ['backward']}),
+    ('ScatterNonAliasingAdd_1d', {
+        'block': ScatterNonAliasingAdd((8,)),
+        'desc_inputs': (Tensor(np.array([[2], [3], [4], [5]], np.int32)),
+                        Tensor(np.array([2.0, 3.0, 4.0, 8.0], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterNdAdd', {
+        'block': ScatterNdAdd((8,)),
+        'desc_inputs': (Tensor(np.array([[2], [3], [4], [5]], np.int32)),
+                        Tensor(np.array([2.0, 3.0, 4.0, 8.0], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterNdSub', {
+        'block': ScatterNdAdd((8,)),
+        'desc_inputs': (Tensor(np.array([[2], [3], [4], [5]], np.int32)),
+                        Tensor(np.array([2.0, 3.0, 4.0, 8.0], np.float32))),
+        'skip': ['backward']}),
     ('ScatterAdd', {
         'block': ScatterAdd((6,)),
         'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),