remove import probability from nn/__init__.py

mindspore/nn/__init__.py

@@ -24,7 +24,6 @@ from .loss import *
 from .optim import *
 from .metrics import *
 from .wrap import *
-from .probability import *
 
 
 __all__ = ["Cell", "GraphKernel"]
@@ -33,7 +32,7 @@ __all__.extend(loss.__all__)
 __all__.extend(optim.__all__)
 __all__.extend(metrics.__all__)
 __all__.extend(wrap.__all__)
-__all__.extend(probability.__all__)
+
 
 
 __all__.sort()

mindspore/nn/probability/__init__.py

@@ -15,10 +15,7 @@
 """
 Probability.
 
-The high-level components(Distributions) used to construct the probabilistic network.
+The high-level components used to construct the probabilistic network.
 """
 
-from .distribution import *
-
-__all__ = []
-__all__.extend(distribution.__all__)
+from . import distribution

tests/st/ops/ascend/test_distribution/test_bernoulli.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""test cases for bernoulli distribution"""
+"""test cases for Bernoulli distribution"""
 import numpy as np
 from scipy import stats
 import mindspore.context as context
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore import dtype
@@ -29,7 +30,7 @@ class Prob(nn.Cell):
     """
     def __init__(self):
         super(Prob, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -54,7 +55,7 @@ class LogProb(nn.Cell):
     """
     def __init__(self):
         super(LogProb, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -78,7 +79,7 @@ class KL(nn.Cell):
     """
     def __init__(self):
         super(KL, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -104,7 +105,7 @@ class Basics(nn.Cell):
     """
     def __init__(self):
         super(Basics, self).__init__()
-        self.b = nn.Bernoulli([0.3, 0.5, 0.7], dtype=dtype.int32)
+        self.b = msd.Bernoulli([0.3, 0.5, 0.7], dtype=dtype.int32)
 
     @ms_function
     def construct(self):
@@ -130,7 +131,7 @@ class Sampling(nn.Cell):
     """
     def __init__(self, shape, seed=0):
         super(Sampling, self).__init__()
-        self.b = nn.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
+        self.b = msd.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
         self.shape = shape
 
     @ms_function
@@ -152,7 +153,7 @@ class CDF(nn.Cell):
     """
     def __init__(self):
         super(CDF, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -177,7 +178,7 @@ class LogCDF(nn.Cell):
     """
     def __init__(self):
         super(LogCDF, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -202,7 +203,7 @@ class SF(nn.Cell):
     """
     def __init__(self):
         super(SF, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -227,7 +228,7 @@ class LogSF(nn.Cell):
     """
     def __init__(self):
         super(LogSF, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -251,7 +252,7 @@ class EntropyH(nn.Cell):
     """
     def __init__(self):
         super(EntropyH, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self):
@@ -274,7 +275,7 @@ class CrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(CrossEntropy, self).__init__()
-        self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):

tests/st/ops/ascend/test_distribution/test_exponential.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""test cases for exponential distribution"""
+"""test cases for Exponential distribution"""
 import numpy as np
 from scipy import stats
 import mindspore.context as context
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore import dtype
@@ -29,7 +30,7 @@ class Prob(nn.Cell):
     """
     def __init__(self):
         super(Prob, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -53,7 +54,7 @@ class LogProb(nn.Cell):
     """
     def __init__(self):
         super(LogProb, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -77,7 +78,7 @@ class KL(nn.Cell):
     """
     def __init__(self):
         super(KL, self).__init__()
-        self.e = nn.Exponential([1.5], dtype=dtype.float32)
+        self.e = msd.Exponential([1.5], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -101,7 +102,7 @@ class Basics(nn.Cell):
     """
     def __init__(self):
         super(Basics, self).__init__()
-        self.e = nn.Exponential([0.5], dtype=dtype.float32)
+        self.e = msd.Exponential([0.5], dtype=dtype.float32)
 
     @ms_function
     def construct(self):
@@ -127,7 +128,7 @@ class Sampling(nn.Cell):
     """
     def __init__(self, shape, seed=0):
         super(Sampling, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], seed=seed, dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], seed=seed, dtype=dtype.float32)
         self.shape = shape
 
     @ms_function
@@ -151,7 +152,7 @@ class CDF(nn.Cell):
     """
     def __init__(self):
         super(CDF, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -175,7 +176,7 @@ class LogCDF(nn.Cell):
     """
     def __init__(self):
         super(LogCDF, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -199,7 +200,7 @@ class SF(nn.Cell):
     """
     def __init__(self):
         super(SF, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -223,7 +224,7 @@ class LogSF(nn.Cell):
     """
     def __init__(self):
         super(LogSF, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -247,7 +248,7 @@ class EntropyH(nn.Cell):
     """
     def __init__(self):
         super(EntropyH, self).__init__()
-        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
+        self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
     @ms_function
     def construct(self):
@@ -270,7 +271,7 @@ class CrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(CrossEntropy, self).__init__()
-        self.e = nn.Exponential([1.0], dtype=dtype.float32)
+        self.e = msd.Exponential([1.0], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):

tests/st/ops/ascend/test_distribution/test_geometric.py

@@ -17,6 +17,7 @@ import numpy as np
 from scipy import stats
 import mindspore.context as context
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore import dtype
@@ -29,7 +30,7 @@ class Prob(nn.Cell):
     """
     def __init__(self):
         super(Prob, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -53,7 +54,7 @@ class LogProb(nn.Cell):
     """
     def __init__(self):
         super(LogProb, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -77,7 +78,7 @@ class KL(nn.Cell):
     """
     def __init__(self):
         super(KL, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -103,7 +104,7 @@ class Basics(nn.Cell):
     """
     def __init__(self):
         super(Basics, self).__init__()
-        self.g = nn.Geometric([0.5, 0.5], dtype=dtype.int32)
+        self.g = msd.Geometric([0.5, 0.5], dtype=dtype.int32)
 
     @ms_function
     def construct(self):
@@ -129,7 +130,7 @@ class Sampling(nn.Cell):
     """
     def __init__(self, shape, seed=0):
         super(Sampling, self).__init__()
-        self.g = nn.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
+        self.g = msd.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
         self.shape = shape
 
     @ms_function
@@ -151,7 +152,7 @@ class CDF(nn.Cell):
     """
     def __init__(self):
         super(CDF, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -175,7 +176,7 @@ class LogCDF(nn.Cell):
     """
     def __init__(self):
         super(LogCDF, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -199,7 +200,7 @@ class SF(nn.Cell):
     """
     def __init__(self):
         super(SF, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -223,7 +224,7 @@ class LogSF(nn.Cell):
     """
     def __init__(self):
         super(LogSF, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):
@@ -247,7 +248,7 @@ class EntropyH(nn.Cell):
     """
     def __init__(self):
         super(EntropyH, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self):
@@ -270,7 +271,7 @@ class CrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(CrossEntropy, self).__init__()
-        self.g = nn.Geometric(0.7, dtype=dtype.int32)
+        self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
     @ms_function
     def construct(self, x_):

tests/st/ops/ascend/test_distribution/test_normal.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""test cases for normal distribution"""
+"""test cases for Normal distribution"""
 import numpy as np
 from scipy import stats
 import mindspore.context as context
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore import dtype
@@ -29,7 +30,7 @@ class Prob(nn.Cell):
     """
     def __init__(self):
         super(Prob, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -52,7 +53,7 @@ class LogProb(nn.Cell):
     """
     def __init__(self):
         super(LogProb, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -76,7 +77,7 @@ class KL(nn.Cell):
     """
     def __init__(self):
         super(KL, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_, y_):
@@ -110,7 +111,7 @@ class Basics(nn.Cell):
     """
     def __init__(self):
         super(Basics, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
 
     @ms_function
     def construct(self):
@@ -135,7 +136,7 @@ class Sampling(nn.Cell):
     """
     def __init__(self, shape, seed=0):
         super(Sampling, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
         self.shape = shape
 
     @ms_function
@@ -160,7 +161,7 @@ class CDF(nn.Cell):
     """
     def __init__(self):
         super(CDF, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -184,7 +185,7 @@ class LogCDF(nn.Cell):
     """
     def __init__(self):
         super(LogCDF, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -207,7 +208,7 @@ class SF(nn.Cell):
     """
     def __init__(self):
         super(SF, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -230,7 +231,7 @@ class LogSF(nn.Cell):
     """
     def __init__(self):
         super(LogSF, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -253,7 +254,7 @@ class EntropyH(nn.Cell):
     """
     def __init__(self):
         super(EntropyH, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
     @ms_function
     def construct(self):
@@ -276,7 +277,7 @@ class CrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(CrossEntropy, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.n = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_, y_):

tests/st/ops/ascend/test_distribution/test_normal_new_api.py

@@ -16,6 +16,7 @@
 import numpy as np
 from scipy import stats
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 import mindspore.context as context
@@ -30,7 +31,7 @@ class Net(nn.Cell):
 
     def __init__(self):
         super(Net, self).__init__()
-        self.normal = nn.Normal(0., 1., dtype=dtype.float32)
+        self.normal = msd.Normal(0., 1., dtype=dtype.float32)
 
     def construct(self, x_, y_):
         kl = self.normal.kl_loss('kl_loss', 'Normal', x_, y_)

tests/st/ops/ascend/test_distribution/test_uniform.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""test cases for uniform distribution"""
+"""test cases for Uniform distribution"""
 import numpy as np
 from scipy import stats
 import mindspore.context as context
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore import dtype
@@ -29,7 +30,7 @@ class Prob(nn.Cell):
     """
     def __init__(self):
         super(Prob, self).__init__()
-        self.u = nn.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -53,7 +54,7 @@ class LogProb(nn.Cell):
     """
     def __init__(self):
         super(LogProb, self).__init__()
-        self.u = nn.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -77,7 +78,7 @@ class KL(nn.Cell):
     """
     def __init__(self):
         super(KL, self).__init__()
-        self.u = nn.Uniform([0.0], [1.5], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_, y_):
@@ -103,7 +104,7 @@ class Basics(nn.Cell):
     """
     def __init__(self):
         super(Basics, self).__init__()
-        self.u = nn.Uniform([0.0], [3.0], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [3.0], dtype=dtype.float32)
 
     @ms_function
     def construct(self):
@@ -127,7 +128,7 @@ class Sampling(nn.Cell):
     """
     def __init__(self, shape, seed=0):
         super(Sampling, self).__init__()
-        self.u = nn.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
         self.shape = shape
 
     @ms_function
@@ -152,7 +153,7 @@ class CDF(nn.Cell):
     """
     def __init__(self):
         super(CDF, self).__init__()
-        self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -176,7 +177,7 @@ class LogCDF(nn.Cell):
     """
     def __init__(self):
         super(LogCDF, self).__init__()
-        self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -188,7 +189,7 @@ class SF(nn.Cell):
     """
     def __init__(self):
         super(SF, self).__init__()
-        self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -200,7 +201,7 @@ class LogSF(nn.Cell):
     """
     def __init__(self):
         super(LogSF, self).__init__()
-        self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_):
@@ -212,7 +213,7 @@ class EntropyH(nn.Cell):
     """
     def __init__(self):
         super(EntropyH, self).__init__()
-        self.u = nn.Uniform([0.0], [1.0, 2.0], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [1.0, 2.0], dtype=dtype.float32)
 
     @ms_function
     def construct(self):
@@ -235,7 +236,7 @@ class CrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(CrossEntropy, self).__init__()
-        self.u = nn.Uniform([0.0], [1.5], dtype=dtype.float32)
+        self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
 
     @ms_function
     def construct(self, x_, y_):

tests/ut/python/nn/distribution/test_bernoulli.py

@@ -13,11 +13,12 @@
 # limitations under the License.
 # ============================================================================
 """
-Test nn.Distribution.Bernoulli.
+Test nn.probability.distribution.Bernoulli.
 """
 import pytest
 
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 
@@ -25,19 +26,19 @@ def test_arguments():
     """
     Args passing during initialization.
     """
-    b = nn.Bernoulli()
-    assert isinstance(b, nn.Distribution)
-    b = nn.Bernoulli([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
-    assert isinstance(b, nn.Distribution)
+    b = msd.Bernoulli()
+    assert isinstance(b, msd.Distribution)
+    b = msd.Bernoulli([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
+    assert isinstance(b, msd.Distribution)
 
 def test_prob():
     """
     Invalid probability.
     """
     with pytest.raises(ValueError):
-        nn.Bernoulli([-0.1], dtype=dtype.int32)
+        msd.Bernoulli([-0.1], dtype=dtype.int32)
     with pytest.raises(ValueError):
-        nn.Bernoulli([1.1], dtype=dtype.int32)
+        msd.Bernoulli([1.1], dtype=dtype.int32)
 
 class BernoulliProb(nn.Cell):
     """
@@ -45,7 +46,7 @@ class BernoulliProb(nn.Cell):
     """
     def __init__(self):
         super(BernoulliProb, self).__init__()
-        self.b = nn.Bernoulli(0.5, dtype=dtype.int32)
+        self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
 
     def construct(self, value):
         prob = self.b('prob', value)
@@ -71,7 +72,7 @@ class BernoulliProb1(nn.Cell):
     """
     def __init__(self):
         super(BernoulliProb1, self).__init__()
-        self.b = nn.Bernoulli(dtype=dtype.int32)
+        self.b = msd.Bernoulli(dtype=dtype.int32)
 
     def construct(self, value, probs):
         prob = self.b('prob', value, probs)
@@ -98,8 +99,8 @@ class BernoulliKl(nn.Cell):
     """
     def __init__(self):
         super(BernoulliKl, self).__init__()
-        self.b1 = nn.Bernoulli(0.7, dtype=dtype.int32)
-        self.b2 = nn.Bernoulli(dtype=dtype.int32)
+        self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
+        self.b2 = msd.Bernoulli(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
         kl1 = self.b1('kl_loss', 'Bernoulli', probs_b)
@@ -122,8 +123,8 @@ class BernoulliCrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(BernoulliCrossEntropy, self).__init__()
-        self.b1 = nn.Bernoulli(0.7, dtype=dtype.int32)
-        self.b2 = nn.Bernoulli(dtype=dtype.int32)
+        self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
+        self.b2 = msd.Bernoulli(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
         h1 = self.b1('cross_entropy', 'Bernoulli', probs_b)
@@ -146,7 +147,7 @@ class BernoulliBasics(nn.Cell):
     """
     def __init__(self):
         super(BernoulliBasics, self).__init__()
-        self.b = nn.Bernoulli([0.3, 0.5], dtype=dtype.int32)
+        self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
 
     def construct(self):
         mean = self.b('mean')

tests/ut/python/nn/distribution/test_exponential.py

@@ -13,11 +13,12 @@
 # limitations under the License.
 # ============================================================================
 """
-Test nn.Distribution.Exponential.
+Test nn.probability.distribution.Exponential.
 """
 import pytest
 
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 
@@ -26,19 +27,19 @@ def test_arguments():
     """
     Args passing during initialization.
     """
-    e = nn.Exponential()
-    assert isinstance(e, nn.Distribution)
-    e = nn.Exponential([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
-    assert isinstance(e, nn.Distribution)
+    e = msd.Exponential()
+    assert isinstance(e, msd.Distribution)
+    e = msd.Exponential([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
+    assert isinstance(e, msd.Distribution)
 
 def test_rate():
     """
     Invalid rate.
     """
     with pytest.raises(ValueError):
-        nn.Exponential([-0.1], dtype=dtype.float32)
+        msd.Exponential([-0.1], dtype=dtype.float32)
     with pytest.raises(ValueError):
-        nn.Exponential([0.0], dtype=dtype.float32)
+        msd.Exponential([0.0], dtype=dtype.float32)
 
 class ExponentialProb(nn.Cell):
     """
@@ -46,7 +47,7 @@ class ExponentialProb(nn.Cell):
     """
     def __init__(self):
         super(ExponentialProb, self).__init__()
-        self.e = nn.Exponential(0.5, dtype=dtype.float32)
+        self.e = msd.Exponential(0.5, dtype=dtype.float32)
 
     def construct(self, value):
         prob = self.e('prob', value)
@@ -72,7 +73,7 @@ class ExponentialProb1(nn.Cell):
     """
     def __init__(self):
         super(ExponentialProb1, self).__init__()
-        self.e = nn.Exponential(dtype=dtype.float32)
+        self.e = msd.Exponential(dtype=dtype.float32)
 
     def construct(self, value, rate):
         prob = self.e('prob', value, rate)
@@ -99,8 +100,8 @@ class ExponentialKl(nn.Cell):
     """
     def __init__(self):
         super(ExponentialKl, self).__init__()
-        self.e1 = nn.Exponential(0.7, dtype=dtype.float32)
-        self.e2 = nn.Exponential(dtype=dtype.float32)
+        self.e1 = msd.Exponential(0.7, dtype=dtype.float32)
+        self.e2 = msd.Exponential(dtype=dtype.float32)
 
     def construct(self, rate_b, rate_a):
         kl1 = self.e1('kl_loss', 'Exponential', rate_b)
@@ -123,8 +124,8 @@ class ExponentialCrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(ExponentialCrossEntropy, self).__init__()
-        self.e1 = nn.Exponential(0.3, dtype=dtype.float32)
-        self.e2 = nn.Exponential(dtype=dtype.float32)
+        self.e1 = msd.Exponential(0.3, dtype=dtype.float32)
+        self.e2 = msd.Exponential(dtype=dtype.float32)
 
     def construct(self, rate_b, rate_a):
         h1 = self.e1('cross_entropy', 'Exponential', rate_b)
@@ -147,7 +148,7 @@ class ExponentialBasics(nn.Cell):
     """
     def __init__(self):
         super(ExponentialBasics, self).__init__()
-        self.e = nn.Exponential([0.3, 0.5], dtype=dtype.float32)
+        self.e = msd.Exponential([0.3, 0.5], dtype=dtype.float32)
 
     def construct(self):
         mean = self.e('mean')

tests/ut/python/nn/distribution/test_geometric.py

@@ -13,11 +13,12 @@
 # limitations under the License.
 # ============================================================================
 """
-Test nn.Distribution.Geometric.
+Test nn.probability.distribution.Geometric.
 """
 import pytest
 
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 
@@ -26,19 +27,19 @@ def test_arguments():
     """
     Args passing during initialization.
     """
-    g = nn.Geometric()
-    assert isinstance(g, nn.Distribution)
-    g = nn.Geometric([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
-    assert isinstance(g, nn.Distribution)
+    g = msd.Geometric()
+    assert isinstance(g, msd.Distribution)
+    g = msd.Geometric([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
+    assert isinstance(g, msd.Distribution)
 
 def test_prob():
     """
     Invalid probability.
     """
     with pytest.raises(ValueError):
-        nn.Geometric([-0.1], dtype=dtype.int32)
+        msd.Geometric([-0.1], dtype=dtype.int32)
     with pytest.raises(ValueError):
-        nn.Geometric([1.1], dtype=dtype.int32)
+        msd.Geometric([1.1], dtype=dtype.int32)
 
 class GeometricProb(nn.Cell):
     """
@@ -46,7 +47,7 @@ class GeometricProb(nn.Cell):
     """
     def __init__(self):
         super(GeometricProb, self).__init__()
-        self.g = nn.Geometric(0.5, dtype=dtype.int32)
+        self.g = msd.Geometric(0.5, dtype=dtype.int32)
 
     def construct(self, value):
         prob = self.g('prob', value)
@@ -72,7 +73,7 @@ class GeometricProb1(nn.Cell):
     """
     def __init__(self):
         super(GeometricProb1, self).__init__()
-        self.g = nn.Geometric(dtype=dtype.int32)
+        self.g = msd.Geometric(dtype=dtype.int32)
 
     def construct(self, value, probs):
         prob = self.g('prob', value, probs)
@@ -100,8 +101,8 @@ class GeometricKl(nn.Cell):
     """
     def __init__(self):
         super(GeometricKl, self).__init__()
-        self.g1 = nn.Geometric(0.7, dtype=dtype.int32)
-        self.g2 = nn.Geometric(dtype=dtype.int32)
+        self.g1 = msd.Geometric(0.7, dtype=dtype.int32)
+        self.g2 = msd.Geometric(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
         kl1 = self.g1('kl_loss', 'Geometric', probs_b)
@@ -124,8 +125,8 @@ class GeometricCrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(GeometricCrossEntropy, self).__init__()
-        self.g1 = nn.Geometric(0.3, dtype=dtype.int32)
-        self.g2 = nn.Geometric(dtype=dtype.int32)
+        self.g1 = msd.Geometric(0.3, dtype=dtype.int32)
+        self.g2 = msd.Geometric(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
         h1 = self.g1('cross_entropy', 'Geometric', probs_b)
@@ -148,7 +149,7 @@ class GeometricBasics(nn.Cell):
     """
     def __init__(self):
         super(GeometricBasics, self).__init__()
-        self.g = nn.Geometric([0.3, 0.5], dtype=dtype.int32)
+        self.g = msd.Geometric([0.3, 0.5], dtype=dtype.int32)
 
     def construct(self):
         mean = self.g('mean')

tests/ut/python/nn/distribution/test_normal.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 # ============================================================================
 """
-Test nn.Distribution.Normal.
+Test nn.probability.distribution.Normal.
 """
 import numpy as np
 import pytest
 
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 
@@ -27,17 +28,17 @@ def test_normal_shape_errpr():
     Invalid shapes.
     """
     with pytest.raises(ValueError):
-        nn.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
+        msd.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
 
 
 def test_arguments():
     """
     args passing during initialization.
     """
-    n = nn.Normal()
-    assert isinstance(n, nn.Distribution)
-    n = nn.Normal([3.0], [4.0], dtype=dtype.float32)
-    assert isinstance(n, nn.Distribution)
+    n = msd.Normal()
+    assert isinstance(n, msd.Distribution)
+    n = msd.Normal([3.0], [4.0], dtype=dtype.float32)
+    assert isinstance(n, msd.Distribution)
 
 
 class NormalProb(nn.Cell):
@@ -46,7 +47,7 @@ class NormalProb(nn.Cell):
     """
     def __init__(self):
         super(NormalProb, self).__init__()
-        self.normal = nn.Normal(3.0, 4.0, dtype=dtype.float32)
+        self.normal = msd.Normal(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self, value):
         prob = self.normal('prob', value)
@@ -73,7 +74,7 @@ class NormalProb1(nn.Cell):
     """
     def __init__(self):
         super(NormalProb1, self).__init__()
-        self.normal = nn.Normal()
+        self.normal = msd.Normal()
 
     def construct(self, value, mean, sd):
         prob = self.normal('prob', value, mean, sd)
@@ -101,8 +102,8 @@ class NormalKl(nn.Cell):
     """
     def __init__(self):
         super(NormalKl, self).__init__()
-        self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
-        self.n2 = nn.Normal(dtype=dtype.float32)
+        self.n1 = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.n2 = msd.Normal(dtype=dtype.float32)
 
     def construct(self, mean_b, sd_b, mean_a, sd_a):
         kl1 = self.n1('kl_loss', 'Normal', mean_b, sd_b)
@@ -127,8 +128,8 @@ class NormalCrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(NormalCrossEntropy, self).__init__()
-        self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
-        self.n2 = nn.Normal(dtype=dtype.float32)
+        self.n1 = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.n2 = msd.Normal(dtype=dtype.float32)
 
     def construct(self, mean_b, sd_b, mean_a, sd_a):
         h1 = self.n1('cross_entropy', 'Normal', mean_b, sd_b)
@@ -153,7 +154,7 @@ class NormalBasics(nn.Cell):
     """
     def __init__(self):
         super(NormalBasics, self).__init__()
-        self.n = nn.Normal(3.0, 4.0, dtype=dtype.float32)
+        self.n = msd.Normal(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self):
         mean = self.n('mean')

tests/ut/python/nn/distribution/test_uniform.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 # ============================================================================
 """
-Test nn.Distribution.Uniform.
+Test nn.probability.distribution.Uniform.
 """
 import numpy as np
 import pytest
 
 import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 
@@ -27,17 +28,17 @@ def test_uniform_shape_errpr():
     Invalid shapes.
     """
     with pytest.raises(ValueError):
-        nn.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
+        msd.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
 
 
 def test_arguments():
     """
     Args passing during initialization.
     """
-    u = nn.Uniform()
-    assert isinstance(u, nn.Distribution)
-    u = nn.Uniform([3.0], [4.0], dtype=dtype.float32)
-    assert isinstance(u, nn.Distribution)
+    u = msd.Uniform()
+    assert isinstance(u, msd.Distribution)
+    u = msd.Uniform([3.0], [4.0], dtype=dtype.float32)
+    assert isinstance(u, msd.Distribution)
 
 
 def test_invalid_range():
@@ -45,9 +46,9 @@ def test_invalid_range():
     Test range of uniform distribution.
     """
     with pytest.raises(ValueError):
-        nn.Uniform(0.0, 0.0, dtype=dtype.float32)
+        msd.Uniform(0.0, 0.0, dtype=dtype.float32)
     with pytest.raises(ValueError):
-        nn.Uniform(1.0, 0.0, dtype=dtype.float32)
+        msd.Uniform(1.0, 0.0, dtype=dtype.float32)
 
 
 class UniformProb(nn.Cell):
@@ -56,7 +57,7 @@ class UniformProb(nn.Cell):
     """
     def __init__(self):
         super(UniformProb, self).__init__()
-        self.u = nn.Uniform(3.0, 4.0, dtype=dtype.float32)
+        self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self, value):
         prob = self.u('prob', value)
@@ -82,7 +83,7 @@ class UniformProb1(nn.Cell):
     """
     def __init__(self):
         super(UniformProb1, self).__init__()
-        self.u = nn.Uniform(dtype=dtype.float32)
+        self.u = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, value, low, high):
         prob = self.u('prob', value, low, high)
@@ -110,8 +111,8 @@ class UniformKl(nn.Cell):
     """
     def __init__(self):
         super(UniformKl, self).__init__()
-        self.u1 = nn.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
-        self.u2 = nn.Uniform(dtype=dtype.float32)
+        self.u1 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.u2 = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, low_b, high_b, low_a, high_a):
         kl1 = self.u1('kl_loss', 'Uniform', low_b, high_b)
@@ -136,8 +137,8 @@ class UniformCrossEntropy(nn.Cell):
     """
     def __init__(self):
         super(UniformCrossEntropy, self).__init__()
-        self.u1 = nn.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
-        self.u2 = nn.Uniform(dtype=dtype.float32)
+        self.u1 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
+        self.u2 = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, low_b, high_b, low_a, high_a):
         h1 = self.u1('cross_entropy', 'Uniform', low_b, high_b)
@@ -162,7 +163,7 @@ class UniformBasics(nn.Cell):
     """
     def __init__(self):
         super(UniformBasics, self).__init__()
-        self.u = nn.Uniform(3.0, 4.0, dtype=dtype.float32)
+        self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self):
         mean = self.u('mean')