add DivNoNan vm ops.

mindspore/ops/_grad/grad_math_ops.py

@@ -232,6 +232,21 @@ def get_bprop_div(self):
     return bprop
 
 
+@bprop_getters.register(P.DivNoNan)
+def get_bprop_div_no_nan(self):
+    """Grad definition for `DivNoNan` operation."""
+    div_no_nan_op = P.DivNoNan()
+    neg = P.Neg()
+    mul_op = P.Mul()
+
+    def bprop(x, y, out, dout):
+        bc_x = div_no_nan_op(dout, y)
+        bc_y = neg(mul_op(bc_x, out))
+        return binop_grad_common(x, y, bc_x, bc_y)
+
+    return bprop
+
+
 @bprop_getters.register(P.Floor)
 def get_bprop_floor(self):
     """Grad definition for `floor` operation."""

mindspore/ops/_op_impl/tbe/__init__.py

@@ -225,6 +225,7 @@ from .asin import _asin_tbe
 from .asin_grad import _asin_grad_tbe
 from .asinh import _asinh_tbe
 from .asinh_grad import _asinh_grad_tbe
+from .div_no_nan import _div_no_nan_tbe
 from .atan import _atan_tbe
 from .atan_grad import _atan_grad_tbe
 from .atanh import _atanh_tbe

mindspore/ops/_op_impl/tbe/div_no_nan.py

@@ -0,0 +1,45 @@
+# 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.
+# ============================================================================
+
+"""DivNoNan op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+div_no_nan_op_info = TBERegOp("DivNoNan") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("div_no_nan.so") \
+    .compute_cost(10) \
+    .kernel_name("div_no_nan") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(div_no_nan_op_info)
+def _div_no_nan_tbe():
+    """DivNoNan TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -43,7 +43,7 @@ from .inner_ops import ScalarCast
 from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul, BitwiseAnd, BitwiseOr,
                        BitwiseXor,
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
-                       Cos, Div, Equal, EqualCount, Exp, Expm1, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh,
+                       Cos, Div, DivNoNan, Equal, EqualCount, Exp, Expm1, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh,
                        Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd,
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
@@ -141,6 +141,7 @@ __all__ = [
     'LessEqual',
     'RealDiv',
     'Div',
+    'DivNoNan',
     'TruncatedNormal',
     'Fill',
     'OnesLike',

mindspore/ops/operations/math_ops.py

@@ -1217,8 +1217,8 @@ class RealDiv(_MathBinaryOp):
     and the type of the scalar is the same as the data type of the tensor.
 
     Inputs:
-        - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
+        - **input_x** (Union[Tensor, Number]) - The first input is a number or a tensor whose data type is number.
-        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is the same as 'input_x' or
           a number.
 
     Outputs:
@@ -1253,12 +1253,12 @@ class Div(_MathBinaryOp):
     and the type of the scalar is the same as the data type of the tensor.
 
     Inputs:
-        - **input_x** (Union[Tensor, Number]) - The first input is a tensor whose data type is number or a number.
+        - **input_x** (Union[Tensor, Number]) - The first input is a number or a tensor whose data type is number.
-        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is same as 'input_x' or
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is the same as 'input_x' or
           a number.
 
     Outputs:
-        Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
+        Tensor, the shape is same as the shape after broadcasting, and the data type is the same as 'input_x'.
 
     Raises:
         ValueError: When `input_x` and `input_y` are not the same dtype.
@@ -1278,6 +1278,46 @@ class Div(_MathBinaryOp):
         return None
 
 
+class DivNoNan(_MathBinaryOp):
+    """
+    Computes a safe divide which returns 0 if the y is zero.
+
+    The inputs must be two tensors or one tensor and one scalar.
+    When the inputs are two tensors, the shapes of them could be broadcast,
+    and the data types of them should be same.
+    When the inputs are one tensor and one scalar, the scalar cannot be a parameter, only can be a constant,
+    and the type of the scalar is the same as the data type of the tensor.
+
+    Inputs:
+        - **input_x** (Union[Tensor, Number]) - The first input is a number or a tensor whose data type is number.
+        - **input_y** (Union[Tensor, Number]) - The second input is a tensor whose data type is the same as 'input_x' or
+          a number.
+
+    Outputs:
+        Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
+
+    Raises:
+        ValueError: When `input_x` and `input_y` are not the same dtype.
+
+    Examples:
+        >>> input_x = Tensor(np.array([-1.0, 0., 1.0, 5.0, 6.0]), mindspore.float32)
+        >>> input_y = Tensor(np.array([0., 0., 0., 2.0, 3.0]), mindspore.float32)
+        >>> div_no_nan = P.DivNoNan()
+        >>> div_no_nan(input_x, input_y)
+        [0., 0., 0., 2.5, 2.0]
+    """
+
+    def infer_value(self, x, y):
+        if x is not None and y is not None:
+            x = x.asnumpy()
+            y = y.asnumpy()
+            with np.errstate(divide='ignore', invalid='ignore'):
+                out = np.true_divide(x, y)
+                out[~np.isfinite(out)] = 0
+            return out
+        return None
+
+
 class FloorDiv(_MathBinaryOp):
     """
     Divide the first input tensor by the second input tensor element-wise and rounds down to the closest integer.