support vm for log1p

mindspore/ops/_grad/grad_math_ops.py

@@ -331,6 +331,19 @@ def get_bprop_log(self):
     return bprop
 
 
+@bprop_getters.register(P.Log1p)
+def get_bprop_log1p(self):
+    """Grad definition for `Log1p` operation."""
+    reciprocal = P.Reciprocal()
+
+    def bprop(x, out, dout):
+        x_1p = x + 1
+        g = reciprocal(x_1p)
+        dx = g * dout
+        return dx, 0
+    return bprop
+
+
 @bprop_getters.register(P.Erf)
 def get_bprop_erf(self):
     """Grad definition for `Erf` operation."""

mindspore/ops/_op_impl/tbe/__init__.py

@@ -159,3 +159,4 @@ from .ones_like import _ones_like_tbe
 from .batch_to_space import _batch_to_space_tbe
 from .space_to_batch import _space_to_batch_tbe
 from .floor import _floor_tbe
+from .log1p import _log1p_tbe

mindspore/ops/_op_impl/tbe/log1p.py

+# 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.
+# ============================================================================
+
+"""Log1p op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+log1p_op_info = TBERegOp("Log1p") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("log1p.so") \
+    .compute_cost(10) \
+    .kernel_name("log1p") \
+    .partial_flag(True) \
+    .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(log1p_op_info)
+def _log1p_tbe():
+    """Log1p TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -40,7 +40,7 @@ from .inner_ops import ScalarCast
 from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
                        Cos, Div, Equal, EqualCount, Exp, Erf, Floor, FloorDiv, FloorMod, Acosh,
-                       Greater, GreaterEqual, Less, LessEqual, Log, LogicalAnd,
+                       Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd,
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
                        NPUAllocFloatStatus, NPUClearFloatStatus,

mindspore/ops/operations/math_ops.py

@@ -1007,6 +1007,35 @@ class Log(PrimitiveWithInfer):
         return x
 
 
+class Log1p(PrimitiveWithInfer):
+    """
+    Returns the natural logarithm of one plus the input 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([1.0, 2.0, 4.0]), mindspore.float32)
+        >>> log1p = P.Log1p()
+        >>> log1p(input_x)
+        [0.6931472, 1.0986123, 1.609438]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=['x'], outputs=['y'])
+
+    def infer_shape(self, x):
+        return x
+
+    def infer_dtype(self, x):
+        validator.check_subclass("x", x, mstype.tensor, self.name)
+        return x
+
+
 class Erf(PrimitiveWithInfer):
     r"""
     Computes the Gauss error function of `input_x` element-wise.

tests/ut/python/ops/test_math_ops.py

@@ -359,6 +359,14 @@ class FloorNet(nn.Cell):
     def construct(self, x):
         return self.floor(x)
 
+class Log1pNet(nn.Cell):
+    def __init__(self):
+        super(Log1pNet, self).__init__()
+        self.log1p = P.Log1p()
+
+    def construct(self, x):
+        return self.log1p(x)
+
 
 test_case_math_ops = [
     ('MatMulGrad', {
@@ -405,6 +413,11 @@ test_case_math_ops = [
         'desc_inputs': [Tensor(np.array([[1., 0., -2.]], np.float32))],
         'desc_bprop': [Tensor(np.array([[1., 0., -2.]], np.float32))],
         'skip': ['backward']}),
+    ('Log1p', {
+        'block': Log1pNet(),
+        'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
+        'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
+        'skip': ['backward']}),
 ]
 
 test_case_lists = [test_case_math_ops]