diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py index ffd79e49b9..89df76b683 100755 --- a/mindspore/ops/_grad/grad_math_ops.py +++ b/mindspore/ops/_grad/grad_math_ops.py @@ -422,6 +422,19 @@ def get_bprop_exp(self): return bprop +@bprop_getters.register(P.Expm1) +def get_bprop_expm1(self): + """Grad definition for `Expm1` operation.""" + exp_ = P.Exp() + + def bprop(x, out, dout): + g = exp_(x) + dx = g * dout + return (dx,) + + return bprop + + @bprop_getters.register(P.Minimum) def get_bprop_minimum(self): """Grad definition for `Minimum` operation.""" diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py index 6d11ba3752..e8162bfdbb 100644 --- a/mindspore/ops/_op_impl/tbe/__init__.py +++ b/mindspore/ops/_op_impl/tbe/__init__.py @@ -83,6 +83,7 @@ from .strided_slice_d import _strided_slice_d_tbe from .strided_slice_grad_d import _strided_slice_grad_d_tbe from .split_d import _split_d_tbe from .exp import _exp_tbe +from .expm1 import _expm1_tbe from .elu import _elu_tbe from .elu_grad import _elu_grad_tbe from .div import _div_tbe diff --git a/mindspore/ops/_op_impl/tbe/expm1.py b/mindspore/ops/_op_impl/tbe/expm1.py new file mode 100644 index 0000000000..2f62c33a3e --- /dev/null +++ b/mindspore/ops/_op_impl/tbe/expm1.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. +# ============================================================================ + +"""Expm1 op""" +from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType + +expm1_op_info = TBERegOp("Expm1") \ + .fusion_type("ELEMWISE") \ + .async_flag(False) \ + .binfile_name("expm1.so") \ + .compute_cost(10) \ + .kernel_name("expm1") \ + .partial_flag(True) \ + .op_pattern("formatAgnostic") \ + .input(0, "x", False, "required", "all") \ + .output(0, "y", False, "required", "all") \ + .dtype_format(DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default) \ + .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \ + .get_op_info() + + +@op_info_register(expm1_op_info) +def _expm1_tbe(): + """Expm1 TBE register""" + return diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 39cd97e5c0..5691a9ba80 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -42,7 +42,7 @@ 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, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh, + Cos, Div, 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, @@ -89,6 +89,7 @@ __all__ = [ 'Mul', 'Pow', 'Exp', + 'Expm1', 'Rsqrt', 'Sqrt', 'Square', diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 3481ef9efc..a7cc1a43d7 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1004,6 +1004,36 @@ class Exp(PrimitiveWithInfer): return x_type +class Expm1(PrimitiveWithInfer): + """ + Returns exponential then minus 1 of a tensor element-wise. + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape as the `input_x`. + + Examples: + >>> input_x = Tensor(np.array([0.0, 1.0, 2.0, 4.0]), mindspore.float32) + >>> expm1 = P.Expm1() + >>> expm1(input_x) + [ 0., 1.71828183, 6.3890561 , 53.59815003] + """ + + @prim_attr_register + def __init__(self): + """init Exp""" + 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_subclass("x", x_type, mstype.tensor, self.name) + return x_type + + class Log(PrimitiveWithInfer): """ Returns the natural logarithm of a tensor element-wise. diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 2a915aafb7..594114f24a 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -348,6 +348,10 @@ test_case_math_ops = [ 'block': P.Exp(), 'desc_inputs': [[2, 3]], 'desc_bprop': [[2, 3]]}), + ('Expm1', { + 'block': P.Expm1(), + 'desc_inputs': [[2, 3]], + 'desc_bprop': [[2, 3]]}), ('Erf', { 'block': P.Erf(), 'desc_inputs': [Tensor(np.array([-2, -1, 0, 1, 2]).astype(np.float16))],