!1508 add op BitwiseAnd BitwiseOr BitwiseXor vm

Merge pull request !1508 from zhaozhenlong/op/bitwise-and-or-xor

mindspore/common/dtype.py

@@ -111,6 +111,7 @@ number_type = (int8,
                float64,)
 
 int_type = (int8, int16, int32, int64,)
+uint_type = (uint8, uint16, uint32, uint64)
 float_type = (float16, float32, float64,)
 
 _simple_types = {

mindspore/ops/_op_impl/tbe/__init__.py

@@ -202,3 +202,6 @@ from .scatter_add import _scatter_add_tbe
 from .atan2 import _atan2_tbe
 from .batch_to_space_nd import _batch_to_space_nd_tbe
 from .space_to_batch_nd import _space_to_batch_nd_tbe
+from .bitwise_and import bitwise_and_op_info
+from .bitwise_or import bitwise_or_op_info
+from .bitwise_xor import bitwise_xor_op_info

mindspore/ops/_op_impl/tbe/bitwise_and.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.
+# ============================================================================
+
+"""BitwiseAnd op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+bitwise_and_op_info = TBERegOp("BitwiseAnd") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bitwise_and.so") \
+    .compute_cost(10) \
+    .kernel_name("bitwise_and") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default, DataType.U16_Default) \
+    .get_op_info()
+
+
+@op_info_register(bitwise_and_op_info)
+def _bitwise_and_tbe():
+    """BitwiseAnd TBE register"""
+    return

mindspore/ops/_op_impl/tbe/bitwise_or.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.
+# ============================================================================
+
+"""BitwiseOr op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+bitwise_or_op_info = TBERegOp("BitwiseOr") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bitwise_or.so") \
+    .compute_cost(10) \
+    .kernel_name("bitwise_or") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default, DataType.U16_Default) \
+    .get_op_info()
+
+
+@op_info_register(bitwise_or_op_info)
+def _bitwise_or_tbe():
+    """BitwiseOr TBE register"""
+    return

mindspore/ops/_op_impl/tbe/bitwise_xor.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.
+# ============================================================================
+
+"""BitwiseXor op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+bitwise_xor_op_info = TBERegOp("BitwiseXor") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bitwise_xor.so") \
+    .compute_cost(10) \
+    .kernel_name("bitwise_xor") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default, DataType.U16_Default) \
+    .get_op_info()
+
+
+@op_info_register(bitwise_xor_op_info)
+def _bitwise_xor_tbe():
+    """BitwiseXor TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -39,7 +39,7 @@ from .debug_ops import (ImageSummary, InsertGradientOf, HookBackward, ScalarSumm
                         TensorSummary, HistogramSummary, Print)
 from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
-from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
+from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul, BitwiseAnd, BitwiseOr, BitwiseXor,
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
                        Cos, Div, Equal, EqualCount, Exp, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh,
                        Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd,
@@ -267,7 +267,10 @@ __all__ = [
     "ApplyCenteredRMSProp",
     "SpaceToBatchND",
     "BatchToSpaceND",
-    "SquareSumAll"
+    "SquareSumAll",
+    "BitwiseAnd",
+    "BitwiseOr",
+    "BitwiseXor"
 ]
 
 __all__.extend(_quant_ops.__all__)

mindspore/ops/operations/math_ops.py

@@ -71,7 +71,7 @@ class _BinaryOp(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self):
-        """init _MathBinaryOp"""
+        """init _BinaryOp"""
         self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
 
     def infer_shape(self, x_shape, y_shape):
@@ -93,6 +93,27 @@ class _MathBinaryOp(_BinaryOp):
         return _MathBinaryOp.do_infer_dtype(x_dtype, y_dtype, mstype.number_type, self.name)
 
 
+class _BitwiseBinaryOp(_MathBinaryOp):
+    """
+    Define bitwise binary operators.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init _BitwiseBinaryOp"""
+        self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
+
+    @staticmethod
+    def _check_bitwise_op_input_type(x1_type, x2_type, prim):
+        args = {'x1': x1_type, 'x2': x2_type}
+        valid_types = mstype.int_type + mstype.uint_type
+        validator.check_tensor_type_same(args, valid_types, prim)
+        return x1_type
+
+    def infer_dtype(self, x1_type, x2_type):
+        return _BitwiseBinaryOp._check_bitwise_op_input_type(x1_type, x2_type, self.name)
+
+
 class TensorAdd(_MathBinaryOp):
     """
     Adds two input tensors element-wise.
@@ -2183,3 +2204,63 @@ class SquareSumAll(PrimitiveWithInfer):
         validator.check_tensor_type_same({'x1_type': x_type}, [mstype.float16, mstype.float32], self.name)
         validator.check_tensor_type_same({'x2_type': y_type}, [mstype.float16, mstype.float32], self.name)
         return x_type, y_type
+
+
+class BitwiseAnd(_BitwiseBinaryOp):
+    """
+    Returns bitwise `and` of two tensors element-wise.
+
+    Inputs:
+        - **input_x1** (Tensor) - The input tensor with int or uint type.
+        - **input_x2** (Tensor) - The input tensor with same type as the `input_x1`.
+
+    Outputs:
+        - **y** (Tensor) - The same type as the `input_x1`.
+
+    Examples:
+         >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16)
+         >>> input_x2 = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mstype.int16)
+         >>> bitwise_and = P.BitwiseAnd()
+         >>> bitwise_and(input_x1, input_x2)
+         [0, 0, 1, -1, 1, 0, 1]
+    """
+
+
+class BitwiseOr(_BitwiseBinaryOp):
+    """
+    Returns bitwise `or` of two tensors element-wise.
+
+    Inputs:
+        - **input_x1** (Tensor) - The input tensor with int or uint type.
+        - **input_x2** (Tensor) - The input tensor with same type as the `input_x1`.
+
+    Outputs:
+        - **y** (Tensor) - The same type as the `input_x1`.
+
+    Examples:
+         >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16)
+         >>> input_x2 = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mstype.int16)
+         >>> bitwise_or = P.BitwiseOr()
+         >>> bitwise_or(input_x1, input_x2)
+         [0, 1, 1, -1, -1, 3, 3]
+    """
+
+
+class BitwiseXor(_BitwiseBinaryOp):
+    """
+    Returns bitwise `xor` of two tensors element-wise.
+
+    Inputs:
+        - **input_x1** (Tensor) - The input tensor with int or uint type.
+        - **input_x2** (Tensor) - The input tensor with same type as the `input_x1`.
+
+    Outputs:
+        - **y** (Tensor) - The same type as the `input_x1`.
+
+    Examples:
+         >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16)
+         >>> input_x2 = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mstype.int16)
+         >>> bitwise_xor = P.BitwiseXor()
+         >>> bitwise_xor(input_x1, input_x2)
+         [0, 1, 0, 0, -2, 3, 2]
+    """

tests/ut/python/ops/test_ops.py

@@ -242,6 +242,36 @@ class ApplyRMSNet(nn.Cell):
         return out
 
 test_case_math_ops = [
+    ('BitwiseAnd', {
+        'block': P.BitwiseAnd(),
+        'desc_inputs': [Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16),
+                        Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mstype.int16)],
+        'skip': ['backward']}),
+    ('BitwiseAnd_1', {
+        'block': P.BitwiseAnd(),
+        'desc_inputs': [Tensor(np.array([[1, 2, 3], [-1, -2, -3]]), mstype.int16),
+                        Tensor(np.array([1, 1, 1]), mstype.int16)],
+        'skip': ['backward']}),
+    ('BitwiseOr', {
+        'block': P.BitwiseOr(),
+        'desc_inputs': [Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16),
+                        Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mstype.int16)],
+        'skip': ['backward']}),
+    ('BitwiseOr_1', {
+        'block': P.BitwiseOr(),
+        'desc_inputs': [Tensor(np.array([[1, 2, 3], [-1, -2, -3]]), mstype.int16),
+                        Tensor(np.array([1, 1, 1]), mstype.int16)],
+        'skip': ['backward']}),
+    ('BitwiseXor', {
+        'block': P.BitwiseXor(),
+        'desc_inputs': [Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16),
+                        Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mstype.int16)],
+        'skip': ['backward']}),
+    ('BitwiseXor_1', {
+        'block': P.BitwiseXor(),
+        'desc_inputs': [Tensor(np.array([[1, 2, 3], [-1, -2, -3]]), mstype.int16),
+                        Tensor(np.array([1, 1, 1]), mstype.int16)],
+        'skip': ['backward']}),
     ('Neg', {
         'block': P.Neg(),
         'desc_inputs': [[1, 3, 4, 4]],