!2699 add ScatterMax ScatterMin ScatterSub vm

Merge pull request !2699 from zhaozhenlong/op/scatter-max-vm

mindspore/ops/_op_impl/tbe/__init__.py

@@ -273,3 +273,6 @@ from .matrix_diag_part import _matrix_diag_part_tbe
 from .matrix_set_diag import _matrix_set_diag_tbe
 from .lrn import _lrn_tbe
 from .lrn_grad import _lrn_grad_tbe
+from .scatter_max import _scatter_max_tbe
+from .scatter_min import _scatter_min_tbe
+from .scatter_sub import _scatter_sub_tbe

mindspore/ops/_op_impl/tbe/scatter_max.py

+# 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.
+# ============================================================================
+
+"""ScatterMax op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+scatter_max_op_info = TBERegOp("ScatterMax") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_max.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_max") \
+    .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.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(scatter_max_op_info)
+def _scatter_max_tbe():
+    """ScatterMax TBE register"""
+    return

mindspore/ops/_op_impl/tbe/scatter_min.py

+# 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.
+# ============================================================================
+
+"""ScatterMin op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+scatter_min_op_info = TBERegOp("ScatterMin") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_min.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_min") \
+    .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.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(scatter_min_op_info)
+def _scatter_min_tbe():
+    """ScatterMin TBE register"""
+    return

mindspore/ops/_op_impl/tbe/scatter_sub.py

+# 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.
+# ============================================================================
+
+"""ScatterSub op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+scatter_sub_op_info = TBERegOp("ScatterSub") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_sub.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_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.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_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) \
+    .get_op_info()
+
+
+@op_info_register(scatter_sub_op_info)
+def _scatter_sub_tbe():
+    """ScatterSub TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -25,7 +25,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Fill, GatherNd, GatherV2, SparseGatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue,
-                        SameTypeShape, ScatterAdd, ScatterMax, ScatterUpdate,
+                        SameTypeShape, ScatterAdd, ScatterSub, ScatterMax, ScatterMin, ScatterUpdate,
                         ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
                         Shape, Size, Slice, Split,
                         Squeeze, StridedSlice, Tile, TensorScatterUpdate,
@@ -214,8 +214,10 @@ __all__ = [
     'BoundingBoxDecode',
     'L2Normalize',
     'ScatterAdd',
+    'ScatterSub',
     'ScatterNd',
     'ScatterMax',
+    'ScatterMin',
     'ResizeNearestNeighbor',
     'HistogramFixedWidth',
     'Pad',

mindspore/ops/operations/array_ops.py

@@ -2272,6 +2272,51 @@ class ScatterMax(PrimitiveWithInfer):
         return x_dtype
 
 
+class ScatterMin(PrimitiveWithInfer):
+    """
+    Update the value of the input tensor through the min operation.
+
+    Using given values to update tensor value through the min 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 min operation whose data type should be mindspore.int32.
+        - **updates** (Tensor) - The tensor doing the min operation with `input_x`,
+          the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
+
+    Outputs:
+        Parameter, the updated `input_x`.
+
+    Examples:
+        >>> input_x = Parameter(Tensor(np.array([[0.0, 1.0, 2.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="input_x")
+        >>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
+        >>> update = Tensor(np.ones([2, 2, 3]), mindspore.float32)
+        >>> scatter_min = P.ScatterMin()
+        >>> output = scatter_min(input_x, indices, update)
+        [[0.0, 1.0, 1.0], [0.0, 0.0, 0.0]]
+    """
+
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        """Init ScatterMin"""
+        self.init_prim_io_names(inputs=['x', 'indices', 'updates'], outputs=['y'])
+        validator.check_value_type('use_locking', use_locking, (bool,), self.name)
+
+    def infer_shape(self, x_shape, indices_shape, updates_shape):
+        _check_scatter_shape(x_shape, indices_shape, updates_shape, self.name)
+        return x_shape
+
+    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.number_type, self.name)
+        return x_dtype
+
+
 class ScatterAdd(PrimitiveWithInfer):
     """
     Update the value of the input tensor through the add operation.
@@ -2316,6 +2361,50 @@ class ScatterAdd(PrimitiveWithInfer):
         return x_dtype
 
 
+class ScatterSub(PrimitiveWithInfer):
+    """
+    Update the value of the input tensor through the sub operation.
+
+    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 sub 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 + x_shape[1:]`.
+
+    Outputs:
+        Parameter, the updated `input_x`.
+
+    Examples:
+        >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]), mindspore.float32), name="x")
+        >>> indices = Tensor(np.array([[0, 1]]), mindspore.int32)
+        >>> updates = Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32)
+        >>> scatter_sub = P.ScatterSub()
+        >>> output = scatter_sub(input_x, indices, updates)
+        [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1.0]]
+    """
+
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        """Init ScatterSub"""
+        validator.check_value_type('use_locking', use_locking, (bool,), self.name)
+
+    def infer_shape(self, x_shape, indices_shape, updates_shape):
+        _check_scatter_shape(x_shape, indices_shape, updates_shape, self.name)
+        return x_shape
+
+    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.number_type, self.name)
+        return x_dtype
+
+
 class SpaceToDepth(PrimitiveWithInfer):
     r"""
     Rearrange blocks of spatial data into depth.

tests/ut/python/ops/test_ops.py

@@ -207,22 +207,35 @@ class HistogramSummaryNet(nn.Cell):
 class ScatterMax(nn.Cell):
     """ScatterMax net definition"""
 
-    def __init__(self):
+    def __init__(self, dtype=np.float32, use_locking=False):
         super(ScatterMax, self).__init__()
-        self.scatter_max = P.ScatterMax()
-        self.ref = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], np.float32)), name="ref")
+        self.scatter_max = P.ScatterMax(use_locking)
+        self.ref = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype)), name="ref")
 
     def construct(self, indices, updates):
         out = self.scatter_max(self.ref, indices, updates)
         return out
 
 
+class ScatterMin(nn.Cell):
+    """ScatterMin net definition"""
+
+    def __init__(self, dtype=np.float32, use_locking=False):
+        super(ScatterMin, self).__init__()
+        self.scatter_min = P.ScatterMin(use_locking)
+        self.ref = Parameter(Tensor(np.array([[-1.0, 2.0, 3.0], [-4.0, 1.0, 6.0]], dtype)), name="ref")
+
+    def construct(self, indices, updates):
+        out = self.scatter_min(self.ref, indices, updates)
+        return out
+
+
 class ScatterAdd(nn.Cell):
     """ScatterAdd net definition"""
 
-    def __init__(self, ref_shape, dtype=np.float32):
+    def __init__(self, ref_shape, dtype=np.float32, use_locking=False):
         super(ScatterAdd, self).__init__()
-        self.scatter_add = P.ScatterAdd()
+        self.scatter_add = P.ScatterAdd(use_locking)
         self.ref = Parameter(Tensor(np.ones(ref_shape, dtype)), name="ref")
 
     def construct(self, indices, updates):
@@ -230,6 +243,19 @@ class ScatterAdd(nn.Cell):
         return out
 
 
+class ScatterSub(nn.Cell):
+    """ScatterSub net definition"""
+
+    def __init__(self, ref_shape, dtype=np.float32, use_locking=False):
+        super(ScatterSub, self).__init__()
+        self.scatter_sub = P.ScatterSub(use_locking)
+        self.ref = Parameter(Tensor(np.ones(ref_shape, dtype)), name="ref")
+
+    def construct(self, indices, updates):
+        out = self.scatter_sub(self.ref, indices, updates)
+        return out
+
+
 class ApplyFtrlNet(nn.Cell):
     def __init__(self):
         super(ApplyFtrlNet, self).__init__()
@@ -1741,11 +1767,61 @@ test_case_other_ops = [
                         Tensor(np.array([[0, 1], [1, 2]], np.int32)),
                         Tensor(np.ones([2, 5], np.float32) * 99)),
         'desc_bprop': [([3, 4, 5], {'dtype': np.float32})]}),
-    ('ScatterMax', {
+    ('ScatterMaxUseLocking', {
+        'block': ScatterMax(use_locking=True),
+        'desc_inputs': (Tensor(np.array([1, 0], np.int32)),
+                        Tensor(np.array([[5.0, 5.0, 5.0], [4.0, 4.0, 4.0]], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterMax1d', {
+        'block': ScatterMax(),
+        'desc_inputs': (Tensor(np.array([1, 0], np.int32)),
+                        Tensor(np.array([[5.0, 5.0, 5.0], [4.0, 4.0, 4.0]], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterMaxF32', {
         'block': ScatterMax(),
         'desc_inputs': (Tensor(np.array([[0, 0], [1, 1]], np.int32)),
                         Tensor(np.ones([2, 2, 3], np.float32) * 99)),
         'skip': ['backward']}),
+    ('ScatterMaxF16', {
+        'block': ScatterMax(np.float16),
+        'desc_inputs': (Tensor(np.array([[0, 0], [1, 1]], np.int32)),
+                        Tensor(np.ones([2, 2, 3], np.float16) * 99)),
+        'skip': ['backward']}),
+    ('ScatterMaxI32', {
+        'block': ScatterMax(np.int32),
+        'desc_inputs': (Tensor(np.array([[0, 0], [1, 1]], np.int32)),
+                        Tensor(np.ones([2, 2, 3], np.int32) * 99)),
+        'skip': ['backward']}),
+    ('ScatterMinUseLocking', {
+        'block': ScatterMin(use_locking=True),
+        'desc_inputs': (Tensor(np.array([1, 0], np.int32)),
+                        Tensor(np.ones([2, 3], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterMin1d', {
+        'block': ScatterMin(),
+        'desc_inputs': (Tensor(np.array([1, 0], np.int32)),
+                        Tensor(np.ones([2, 3], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterMinF32', {
+        'block': ScatterMin(),
+        'desc_inputs': (Tensor(np.array([[0, 0], [1, 1]], np.int32)),
+                        Tensor(np.ones([2, 2, 3], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterMinF16', {
+        'block': ScatterMin(np.float16),
+        'desc_inputs': (Tensor(np.array([[0, 0], [1, 1]], np.int32)),
+                        Tensor(np.ones([2, 2, 3], np.float16))),
+        'skip': ['backward']}),
+    ('ScatterMinI32', {
+        'block': ScatterMin(np.int32),
+        'desc_inputs': (Tensor(np.array([[0, 0], [1, 1]], np.int32)),
+                        Tensor(np.ones([2, 2, 3], np.int32))),
+        'skip': ['backward']}),
+    ('ScatterAddUseLocking', {
+        'block': ScatterAdd((6,), use_locking=True),
+        'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
+                        Tensor(np.array([2.0, 3.0, 4.0], np.float32))),
+        'skip': ['backward']}),
     ('ScatterAdd', {
         'block': ScatterAdd((6,)),
         'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
@@ -1782,6 +1858,42 @@ test_case_other_ops = [
         'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
                         Tensor(np.array([2, 3, 4], np.uint8))),
         'skip': ['backward']}),
+    ('ScatterSubUseLocking', {
+        'block': ScatterSub((6,), use_locking=True),
+        'desc_inputs': (Tensor(np.array([2], np.int32)),
+                        Tensor(np.array([2.0], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterSubScalar', {
+        'block': ScatterSub((6,)),
+        'desc_inputs': (Tensor(np.array([2], np.int32)),
+                        Tensor(np.array([2.0], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterSub2d', {
+        'block': ScatterSub((3, 4)),
+        'desc_inputs': (Tensor(np.array([[0, 1], [1, 2]], np.int32)),
+                        Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2]],
+                                         [[3, 3, 3, 3], [4, 4, 4, 4]]], np.float32))),
+        'skip': ['backward']}),
+    ('ScatterSubF16', {
+        'block': ScatterSub((6,), np.float16),
+        'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
+                        Tensor(np.array([2.0, 3.0, 4.0], np.float16))),
+        'skip': ['backward']}),
+    ('ScatterSubI32', {
+        'block': ScatterSub((6,), np.int32),
+        'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
+                        Tensor(np.array([2, 3, 4], np.int32))),
+        'skip': ['backward']}),
+    ('ScatterSubI8', {
+        'block': ScatterSub((6,), np.int8),
+        'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
+                        Tensor(np.array([2, 3, 4], np.int8))),
+        'skip': ['backward']}),
+    ('ScatterSubU8', {
+        'block': ScatterSub((6,), np.uint8),
+        'desc_inputs': (Tensor(np.array([2, 0, 5], np.int32)),
+                        Tensor(np.array([1, 1, 0], np.uint8))),
+        'skip': ['backward']}),
     ('SmoothL1Loss', {
         'block': P.SmoothL1Loss(),
         'desc_inputs': [[256, 4], [256, 4]],