!8234 Remove expm1_generic and log1p_generic from PP utils

From: @peixu_ren
Reviewed-by: 
Signed-off-by:

mindspore/nn/probability/bijector/power_transform.py

@@ -15,7 +15,7 @@
 """Power Bijector"""
 from mindspore.ops import operations as P
 from ..distribution._utils.utils import check_greater_equal_zero
-from ..distribution._utils.custom_ops import exp_generic, expm1_generic, log_generic, log1p_generic
+from ..distribution._utils.custom_ops import exp_generic, log_generic
 from .bijector import Bijector
 
 
@@ -73,9 +73,9 @@ class PowerTransform(Bijector):
         self.dtypeop = P.DType()
         self.cast = P.Cast()
         self.exp = exp_generic
-        self.expm1 = expm1_generic
+        self.expm1 = P.Expm1()
         self.log = log_generic
-        self.log1p = log1p_generic
+        self.log1p = P.Log1p()
 
     @property
     def power(self):

mindspore/nn/probability/bijector/softplus.py

@@ -16,7 +16,7 @@
 import numpy as np
 from mindspore.ops import operations as P
 from mindspore.nn.layer.activation import LogSigmoid
-from ..distribution._utils.custom_ops import exp_generic, expm1_generic, log_generic
+from ..distribution._utils.custom_ops import exp_generic, log_generic
 from .bijector import Bijector
 
 
@@ -65,7 +65,7 @@ class Softplus(Bijector):
 
         self.exp = exp_generic
         self.log = log_generic
-        self.expm1 = expm1_generic
+        self.expm1 = P.Expm1()
         self.abs = P.Abs()
         self.dtypeop = P.DType()
         self.cast = P.Cast()

mindspore/nn/probability/distribution/_utils/__init__.py

@@ -25,9 +25,7 @@ __all__ = [
     'check_greater_zero',
     'check_prob',
     'exp_generic',
-    'expm1_generic',
     'log_generic',
-    'log1p_generic',
     'broadcast_to',
     'set_param_type',
     'CheckTensor',

mindspore/nn/probability/distribution/_utils/custom_ops.py

@@ -32,13 +32,6 @@ def exp_generic(input_x):
     return exp(input_x)
 
 
-def expm1_generic(input_x):
-    """
-    Expm1 ops under GPU context.
-    """
-    return exp_generic(input_x) - 1.0
-
-
 def log_generic(input_x):
     """
     Log op on Ascend is calculated as log(abs(x)).

mindspore/nn/probability/distribution/gumbel.py

@@ -22,7 +22,7 @@ import mindspore.nn.probability.bijector as msb
 import mindspore.nn.probability.distribution as msd
 from .transformed_distribution import TransformedDistribution
 from ._utils.utils import check_distribution_name
-from ._utils.custom_ops import exp_generic, expm1_generic, log_generic
+from ._utils.custom_ops import exp_generic, log_generic
 
 class Gumbel(TransformedDistribution):
     """
@@ -120,7 +120,7 @@ class Gumbel(TransformedDistribution):
         self.cast = P.Cast()
         self.const = P.ScalarToArray()
         self.exp = exp_generic
-        self.expm1 = expm1_generic
+        self.expm1 = P.Expm1()
         self.fill = P.Fill()
         self.lgamma = nn.LGamma()
         self.log = log_generic

mindspore/nn/probability/distribution/log_normal.py

@@ -19,7 +19,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn.probability.bijector as msb
 import mindspore.nn.probability.distribution as msd
 from ._utils.utils import check_distribution_name
-from ._utils.custom_ops import exp_generic, expm1_generic, log_generic
+from ._utils.custom_ops import exp_generic, log_generic
 
 class LogNormal(msd.TransformedDistribution):
     """
@@ -146,7 +146,7 @@ class LogNormal(msd.TransformedDistribution):
 
         #ops needed for the class
         self.exp = exp_generic
-        self.expm1 = expm1_generic
+        self.expm1 = P.Expm1()
         self.log = log_generic
         self.const = P.ScalarToArray()
         self.erf = P.Erf()

mindspore/nn/probability/distribution/logistic.py

@@ -20,7 +20,7 @@ from mindspore._checkparam import Validator
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import check_greater_zero
-from ._utils.custom_ops import exp_generic, expm1_generic, log_generic, log1p_generic
+from ._utils.custom_ops import exp_generic, log_generic
 
 
 class Logistic(Distribution):
@@ -124,11 +124,11 @@ class Logistic(Distribution):
         self.const = P.ScalarToArray()
         self.dtypeop = P.DType()
         self.exp = exp_generic
-        self.expm1 = expm1_generic
+        self.expm1 = P.Expm1()
         self.fill = P.Fill()
         self.less = P.Less()
         self.log = log_generic
-        self.log1p = log1p_generic
+        self.log1p = P.Log1p()
         self.logicalor = P.LogicalOr()
         self.erf = P.Erf()
         self.greater = P.Greater()

mindspore/nn/probability/distribution/normal.py

@@ -20,7 +20,7 @@ from mindspore._checkparam import Validator
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import check_greater_zero, check_distribution_name
-from ._utils.custom_ops import exp_generic, expm1_generic, log_generic
+from ._utils.custom_ops import exp_generic, log_generic
 
 
 class Normal(Distribution):
@@ -137,7 +137,7 @@ class Normal(Distribution):
 
         # ops needed for the class
         self.exp = exp_generic
-        self.expm1 = expm1_generic
+        self.expm1 = P.Expm1()
         self.log = log_generic
         self.erf = P.Erf()
         self.squeeze = P.Squeeze(0)

tests/ut/python/nn/probability/distribution/test_lognormal.py

@@ -175,15 +175,16 @@ class LogNormalBasics(nn.Cell):
 
     def construct(self):
         mean = self.n.mean()
-        sd = self.n.sd()
         mode = self.n.mode()
         entropy = self.n.entropy()
-        return mean + sd + mode + entropy
+        return mean + mode + entropy
 
 def test_bascis():
     """
     Test mean/sd/mode/entropy functionality of LogNormal.
     """
+    from mindspore import context
+    context.set_context(device_target="Ascend")
     net = LogNormalBasics()
     ans = net()
     assert isinstance(ans, Tensor)