vm for erfc

mindspore/ops/_grad/grad_math_ops.py

@@ -361,6 +361,23 @@ def get_bprop_erf(self):
     return bprop
 
 
+@bprop_getters.register(P.Erfc)
+def get_bprop_erfc(self):
+    """Grad definition for `Erfc` operation."""
+    exp = P.Exp()
+    square = P.Square()
+    sqrt = P.Sqrt()
+    cast = P.Cast()
+    dtype = P.DType()
+
+    def bprop(x, out, dout):
+        half_root_pi = cast(2 / sqrt(F.scalar_to_tensor(np.pi)), dtype(x))
+        x_square = square(x)
+        dx = dout * (-half_root_pi * exp(-x_square))
+        return (dx,)
+    return bprop
+
+
 @bprop_getters.register(P.Pow)
 def get_bprop_pow(self):
     """Grad definition for `Pow` operation."""

mindspore/ops/_op_impl/tbe/__init__.py

@@ -152,6 +152,7 @@ from .fused_mul_add_n import _fused_mul_add_n_tbe
 from .fused_mul_apply_momentum import _fused_mul_apply_momentum_tbe
 from .fill import _fill_op_tbe
 from .erf import _erf_op_tbe
+from .erfc import _erfc_op_tbe
 from .depthwise_conv2d import _depthwise_conv2d_tbe
 from .depthwise_conv2d_backprop_filter import _depthwise_conv2d_backprop_filter_tbe
 from .depthwise_conv2d_backprop_input import _depthwise_conv2d_backprop_input_tbe

mindspore/ops/_op_impl/tbe/erfc.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.
+# ============================================================================
+
+"""Erfc op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+erfc_op_info = TBERegOp("Erfc") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("erfc.so") \
+    .compute_cost(10) \
+    .kernel_name("erfc") \
+    .partial_flag(True) \
+    .op_pattern("formatAgnostic") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(erfc_op_info)
+def _erfc_op_tbe():
+    """Erfc TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -39,7 +39,7 @@ from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
 from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
-                       Cos, Div, Equal, EqualCount, Exp, Erf, Floor, FloorDiv, FloorMod, Acosh,
+                       Cos, Div, Equal, EqualCount, Exp, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh,
                        Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd,
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,

mindspore/ops/operations/math_ops.py

@@ -1067,6 +1067,36 @@ class Erf(PrimitiveWithInfer):
         return x_type
 
 
+class Erfc(PrimitiveWithInfer):
+    r"""
+    Computes the complementary error function of `input_x` element-wise.
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, has the same shape and dtype as the `input_x`.
+
+    Examples:
+        >>> input_x = Tensor(np.array([-1, 0, 1, 2, 3]), mindspore.float32)
+        >>> erfc = P.Erfc()
+        >>> erfc(input_x)
+        [1.8427168, 0., 0.1572832, 0.00469124, 0.00002235]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init Erfc"""
+        self.init_prim_io_names(inputs=['x'], outputs=['y'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_type):
+        validator.check_tensor_type_same({"x": x_type}, [mstype.float16, mstype.float32], self.name)
+        return x_type
+
+
 class Minimum(_MathBinaryOp):
     """
     Computes the element-wise minimum of input tensors.

tests/ut/python/ops/test_math_ops.py

@@ -372,6 +372,15 @@ class Log1pNet(nn.Cell):
         return self.log1p(x)
 
 
+class ErfcNet(nn.Cell):
+    def __init__(self):
+        super(ErfcNet, self).__init__()
+        self.erfc = P.Erfc()
+
+    def construct(self, x):
+        return self.erfc(x)
+
+
 test_case_math_ops = [
     ('MatMulGrad', {
         'block': GradWrap(NetWithLoss(MatMulNet())),
@@ -422,6 +431,11 @@ test_case_math_ops = [
         'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
         'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
         'skip': ['backward']}),
+    ('Erfc', {
+        'block': ErfcNet(),
+        'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
+        'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
+    }),
 ]
 
 test_case_lists = [test_case_math_ops]