add vm for cosh and sinh

mindspore/ops/_grad/grad_math_ops.py

@@ -793,6 +793,18 @@ def get_bprop_asinh(self):
     return bprop
 
 
+@bprop_getters.register(P.Sinh)
+def get_bprop_sinh(self):
+    """Grad definition for `Sinh` operation."""
+    cosh = P.Cosh()
+
+    def bprop(x, out, dout):
+        dx = cosh(x) * dout
+        return (dx,)
+
+    return bprop
+
+
 @bprop_getters.register(P.Cos)
 def get_bprop_cos(self):
     """Grad definition for `Cos` operation."""
@@ -830,6 +842,18 @@ def get_bprop_acosh(self):
     return bprop
 
 
+@bprop_getters.register(P.Cosh)
+def get_bprop_cosh(self):
+    """Grad definition for `Cosh` operation."""
+    sinh = P.Sinh()
+
+    def bprop(x, out, dout):
+        dx = sinh(x) * dout
+        return (dx,)
+
+    return bprop
+
+
 @bprop_getters.register(P.Abs)
 def get_bprop_abs(self):
     """Grad definition for `Abs` operation."""

mindspore/ops/_op_impl/tbe/__init__.py

@@ -227,3 +227,5 @@ from .asinh_grad import _asinh_grad_tbe
 from .atan import _atan_tbe
 from .atan_grad import _atan_grad_tbe
 from .atanh import _atanh_tbe
+from .cosh import _cosh_tbe
+from .sinh import _sinh_tbe

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

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

mindspore/ops/operations/__init__.py

@@ -40,7 +40,8 @@ from .debug_ops import (ImageSummary, InsertGradientOf, HookBackward, ScalarSumm
 from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
 
-from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul, BitwiseAnd, BitwiseOr, BitwiseXor,
+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, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh,
                        Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd,
@@ -50,7 +51,8 @@ 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, Atan, Atanh)
+                       Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh)
+
 from .random_ops import (RandomChoiceWithMask)
 from .nn_ops import (LSTM, SGD, Adam, ApplyMomentum, BatchNorm,
                      BiasAdd, Conv2D,
@@ -245,6 +247,7 @@ __all__ = [
     'Asinh',
     "PReLU",
     "Cos",
+    "Cosh",
     "ACos",
     "Diag",
     "DiagPart",
@@ -253,6 +256,7 @@ __all__ = [
     'AssignAdd',
     'AssignSub',
     "Sin",
+    "Sinh",
     "Asin",
     "LSTM",
     "Abs",

mindspore/ops/operations/math_ops.py

@@ -1359,6 +1359,35 @@ class Acosh(PrimitiveWithInfer):
         return x_dtype
 
 
+class Cosh(PrimitiveWithInfer):
+    """
+    Computes hyperbolic cosine of input element-wise.
+
+    Inputs:
+        - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
+
+    Outputs:
+        Tensor, has the same shape as `input_x`.
+
+    Examples:
+        >>> cosh = P.Cosh()
+        >>> input_x = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32)
+        >>> output = cosh(input_x)
+        [1.0289385 1.364684 1.048436 1.4228927]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init Cosh"""
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_tensor_type_same({'x': x_dtype}, mstype.number_type, self.name)
+        return x_dtype
+
+
 class Asinh(PrimitiveWithInfer):
     """
     Compute inverse hyperbolic cosine of x element-wise.
@@ -1376,7 +1405,6 @@ class Asinh(PrimitiveWithInfer):
         [-2.3212, 1.1976, 1.8184, 5.2983]
     """
 
-
     @prim_attr_register
     def __init__(self):
         """init Asinh"""
@@ -1389,6 +1417,35 @@ class Asinh(PrimitiveWithInfer):
         return x_dtype
 
 
+class Sinh(PrimitiveWithInfer):
+    """
+    Computes hyperbolic sine of input element-wise.
+
+    Inputs:
+        - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
+
+    Outputs:
+        Tensor, has the same shape as `input_x`.
+
+    Examples:
+        >>> sinh = P.Sinh()
+        >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), mindspore.float32)
+        >>> output = sinh(input_x)
+        [0.6604918 0.28367308 0.44337422 0.6604918]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init Sinh"""
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_tensor_type_same({'x': x_dtype}, mstype.number_type, self.name)
+        return x_dtype
+
+
 class _LogicBinaryOp(_BinaryOp):
     """
     Define logic binary operators.

tests/ut/python/ops/test_ops.py

@@ -128,7 +128,7 @@ class NetForFlattenComposed(nn.Cell):
         self.flatten = P.Flatten()
 
     def construct(self, x, y):
-        return self.flatten(x+x) + y
+        return self.flatten(x + x) + y
 
 
 class ArgmaxNet(nn.Cell):
@@ -281,6 +281,7 @@ class ApplyRMSNet(nn.Cell):
         out = self.apply_rms(self.var, self.ms, self.moment, self.lr, grad, self.rho, self.momentum, self.epsilon)
         return out
 
+
 test_case_math_ops = [
     ('BitwiseAnd', {
         'block': P.BitwiseAnd(),
@@ -732,6 +733,14 @@ test_case_math_ops = [
         'block': P.Atanh(),
         'desc_inputs': [[2, 3]],
         'desc_bprop': [[2, 3]]}),
+    ('Cosh', {
+        'block': P.Cosh(),
+        'desc_inputs': [[3, 4, 5]],
+        'desc_bprop': [[3, 4, 5]]}),
+    ('Sinh', {
+        'block': P.Sinh(),
+        'desc_inputs': [[3, 4, 5]],
+        'desc_bprop': [[3, 4, 5]]}),
 ]
 
 test_case_nn_ops = [
@@ -1301,7 +1310,7 @@ test_case_array_ops = [
         'desc_inputs': [(Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)))],
-        'desc_bprop': [[3,]]}),
+        'desc_bprop': [[3, ]]}),
     ('Pack_0', {
         'block': NetForPackInput(P.Pack()),
         'desc_inputs': [[2, 2], [2, 2], [2, 2]],
@@ -1464,7 +1473,7 @@ test_case = functools.reduce(lambda x, y: x + y, test_case_lists)
 test_exec_case = test_case
 
 test_backward_exec_case = filter(lambda x: 'skip' not in x[1] or
-                                 'backward' not in x[1]['skip'], test_case)
+                                           'backward' not in x[1]['skip'], test_case)
 
 
 @non_graph_engine