!1518 add register info for Atan and AtanGrad and Atanh

Merge pull request !1518 from zhouneng/add_vm_support_Atan_AtanGrad_Atanh

mindspore/ops/_grad/grad_math_ops.py

@@ -912,6 +912,17 @@ def get_bprop_bessel_i0e(self):
     return bprop
 
 
+@bprop_getters.register(P.Atan)
+def get_bprop_atan(self):
+    """Grad definition for `Atan` operation."""
+    input_grad = G.AtanGrad()
+
+    def bprop(x, out, dout):
+        dx = input_grad(x, dout)
+        return (dx,)
+    return bprop
+
+
 @bprop_getters.register(P.BesselI1e)
 def get_bprop_bessel_i1e(self):
     """Generate bprop for BesselI1e"""
@@ -934,3 +945,16 @@ def get_bprop_bessel_i1e(self):
         dx = select(x_is_valid, tmp, 0.5 + zeros)
         return (dx,)
     return bprop
+
+
+@bprop_getters.register(P.Atanh)
+def get_bprop_atanh(self):
+    """Grad definition for `Atanh` operation."""
+    power = P.Pow()
+    div = P.Div()
+
+    def bprop(x, out, dout):
+        tmp = 1 - power(x, 2)
+        dx = div(1, tmp) * dout
+        return (dx,)
+    return bprop

mindspore/ops/_op_impl/tbe/__init__.py

@@ -221,3 +221,6 @@ 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 .atan import _atan_tbe
+from .atan_grad import _atan_grad_tbe
+from .atanh import _atanh_tbe

mindspore/ops/_op_impl/tbe/atan.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.
+# ============================================================================
+
+"""Atan op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+atan_op_info = TBERegOp("Atan") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("atan.so") \
+    .compute_cost(10) \
+    .kernel_name("atan") \
+    .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.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(atan_op_info)
+def _atan_tbe():
+    """Atan TBE register"""
+    return

mindspore/ops/_op_impl/tbe/atan_grad.py

@@ -0,0 +1,43 @@
+# 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.
+# ============================================================================
+
+"""AtanGrad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+atan_grad_op_info = TBERegOp("AtanGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("atan_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("atan_grad") \
+    .partial_flag(True) \
+    .input(0, "y", False, "required", "all") \
+    .input(1, "dy", False, "required", "all") \
+    .output(0, "z", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracNZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracNZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(atan_grad_op_info)
+def _atan_grad_tbe():
+    """AtanGrad TBE register"""
+    return

mindspore/ops/_op_impl/tbe/atanh.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.
+# ============================================================================
+
+"""Atanh op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+atanh_op_info = TBERegOp("Atanh") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("atanh.so") \
+    .compute_cost(10) \
+    .kernel_name("atanh") \
+    .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.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(atanh_op_info)
+def _atanh_tbe():
+    """Atanh TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -50,7 +50,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AssignAdd, AssignSub, Atan2
                        NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
                        Reciprocal, CumSum,
                        Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e,
-                       Square, Sub, TensorAdd, Sign, Round, SquareSumAll)
+                       Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh)
 from .random_ops import (RandomChoiceWithMask)
 from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      BiasAdd, Conv2D,
@@ -277,6 +277,8 @@ __all__ = [
     "BitwiseXor",
     "BesselI0e",
     "BesselI1e",
+    "Atan",
+    "Atanh"
 ]
 
 __all__.extend(_quant_ops.__all__)

mindspore/ops/operations/_grad_ops.py

@@ -1151,3 +1151,25 @@ class RefToEmbed(Primitive):
     @prim_attr_register
     def __init__(self):
         pass
+
+
+class AtanGrad(PrimitiveWithInfer):
+    """
+    Computes AtanGrad of input element-wise.
+
+    Returns:
+        Tensor, has the same type as input.
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init AtanGrad"""
+
+    def infer_shape(self, x, dout):
+        validator.check("x shape", x, "dout shape", dout, Rel.EQ, self.name)
+        return x
+
+    def infer_dtype(self, x, dout):
+        args = {"x": x, "dout": dout}
+        validator.check_tensor_type_same(args, mstype.number_type, self.name)
+        return x

mindspore/ops/operations/math_ops.py

@@ -2207,6 +2207,66 @@ class Round(PrimitiveWithInfer):
         return x_type
 
 
+class Atan(PrimitiveWithInfer):
+    """
+    Computes the trignometric inverse tangent of x element-wise.
+
+    Inputs:
+        - **input_x** (Tensor): The input tensor.
+
+    Outputs:
+        A Tensor. Has the same type as x.
+
+    Examples:
+        >>> input_x = Tensor(np.array([1.047, 0.785]), mindspore.float32)
+        >>> tan = P.Tan()
+        >>> output_y = tan(input_x)
+        >>> atan = P.Atan()
+        >>> atan(output_y)
+        [[1.047, 07850001]]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        pass
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_type):
+        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.name)
+        return x_type
+
+
+class Atanh(PrimitiveWithInfer):
+    """
+    Computes inverse hyperbolic tangent of x element-wise.
+
+    Inputs:
+        - **input_x** (Tensor): The input tensor.
+
+    Outputs:
+        A Tensor. Has the same type as x.
+
+    Examples:
+        >>> input_x = Tensor(np.array([1.047, 0.785]), mindspore.float32)
+        >>> atanh = P.Atanh()
+        >>> atanh(input_x)
+        [[1.8869909 1.058268]]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        pass
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_type):
+        validator.check_tensor_type_same({'x': x_type}, mstype.number_type, self.name)
+        return x_type
+
+
 class Atan2(_MathBinaryOp):
     r"""
     Returns arctangent of input_x/input_y element-wise.

tests/ut/python/ops/test_ops.py

@@ -680,6 +680,18 @@ test_case_math_ops = [
         'block': P.BesselI1e(),
         'desc_inputs': [[2, 3]],
         'desc_bprop': [[2, 3]]}),
+    ('Atan', {
+        'block': P.Atan(),
+        'desc_inputs': [[2, 3]],
+        'desc_bprop': [[2, 3]]}),
+    ('AtanGrad', {
+        'block': G.AtanGrad(),
+        'desc_inputs': [[2, 3], [2, 3]],
+        'skip': ['backward']}),
+    ('Atanh', {
+        'block': P.Atanh(),
+        'desc_inputs': [[2, 3]],
+        'desc_bprop': [[2, 3]]}),
 ]
 
 test_case_nn_ops = [