add vm support for Expm1

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."""

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

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

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',

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.

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))],