added Gumbel distribution

mindspore/nn/probability/bijector/bijector.py

@@ -17,7 +17,7 @@ from mindspore import context
 from mindspore.nn.cell import Cell
 from mindspore.ops import operations as P
 from mindspore._checkparam import Validator as validator
-from ..distribution._utils.utils import CheckTensor
+from ..distribution._utils.utils import CheckTensor, cast_to_tensor
 from ..distribution import Distribution
 from ..distribution import TransformedDistribution
 
@@ -66,6 +66,8 @@ class Bijector(Cell):
         # ops needed for the base class
         self.cast_base = P.Cast()
         self.dtype_base = P.DType()
+        self.shape_base = P.Shape()
+        self.fill_base = P.Fill()
 
     @property
     def name(self):
@@ -87,6 +89,36 @@ class Bijector(Cell):
     def is_injective(self):
         return self._is_injective
 
+    def _add_parameter(self, value, name):
+        """
+        Cast `value` to a tensor and add it to `self.default_parameters`.
+        Add `name` into  and `self.parameter_names`.
+        """
+        # initialize the attributes if they do not exist yet
+        if not hasattr(self, 'default_parameters'):
+            self.default_parameters = []
+            self.parameter_names = []
+        # cast value to a tensor if it is not None
+        value_t = None if value is None else cast_to_tensor(value, self.parameter_type)
+        self.default_parameters += [value_t,]
+        self.parameter_names += [name,]
+        return value_t
+
+    def _calc_event_shape(self):
+        """
+        Calculate event_shape based on parameters.
+        """
+        broadcast_shape = None
+        for param in self.default_parameters:
+            if broadcast_shape is None:
+                broadcast_shape = self.shape_base(param)
+                broadcast_shape_tensor = self.fill_base(self.parameter_type, broadcast_shape, 0.0)
+            else:
+                broadcast_shape = self.shape_base(param + broadcast_shape_tensor)
+                broadcast_shape_tensor = self.fill_base(self.parameter_type, broadcast_shape, 0.0)
+        return broadcast_shape
+
+
     def _check_value(self, value, name):
         """
         Check availability of `value` as a Tensor.

mindspore/nn/probability/bijector/gumbel_cdf.py

@@ -14,7 +14,9 @@
 # ============================================================================
 """GumbelCDF Bijector"""
 from mindspore.common import dtype as mstype
-from ..distribution._utils.utils import cast_to_tensor, check_greater_zero, set_param_type
+from mindspore._checkparam import Validator
+from mindspore.ops import operations as P
+from ..distribution._utils.utils import check_greater_zero, set_param_type
 from ..distribution._utils.custom_ops import exp_generic, log_generic
 from .bijector import Bijector
 
@@ -33,6 +35,7 @@ class GumbelCDF(Bijector):
     Args:
         loc (int, float, list, numpy.ndarray, Tensor): The location. Default: 0..
         scale (int, float, list, numpy.ndarray, Tensor): The scale. Default: 1.0.
+        dtype (mindspore.dtype): Type of the distribution which the bijector operates on. Default: float32.
         name (str): The name of the Bijector. Default: 'Gumbel_CDF'.
 
     Examples:
@@ -58,17 +61,24 @@ class GumbelCDF(Bijector):
     def __init__(self,
                  loc=0.0,
                  scale=1.0,
+                 dtype=mstype.float32,
                  name='GumbelCDF'):
         """
         Constructor of GumbelCDF Bijector.
         """
         param = dict(locals())
-        parameter_type = set_param_type({'loc': loc, "scale": scale}, mstype.float32)
+        valid_dtype = mstype.float_type + mstype.int_type + mstype.uint_type
-        super(GumbelCDF, self).__init__(name=name, dtype=parameter_type, param=param)
+        Validator.check_type(type(self).__name__, dtype, valid_dtype)
-        self._loc = cast_to_tensor(loc, parameter_type)
+        parameter_type = set_param_type({'loc': loc, "scale": scale}, dtype)
-        self._scale = cast_to_tensor(scale, parameter_type)
+        super(GumbelCDF, self).__init__(name=name, dtype=dtype, param=param)
-        check_greater_zero(self._scale, "scale")
 
+        self._parameter_type = parameter_type
+        self._loc = self._add_parameter(loc, 'loc')
+        self._scale = self._add_parameter(scale, 'scale')
+        check_greater_zero(self._scale, "scale")
+        self._event_shape = self._calc_event_shape()
+
+        self.cast = P.Cast()
         self.exp = exp_generic
         self.log = log_generic
 
@@ -81,6 +91,14 @@ class GumbelCDF(Bijector):
     def scale(self):
         return self._scale
 
+    @property
+    def event_shape(self):
+        return self._event_shape
+
+    @property
+    def parameter_type(self):
+        return self._parameter_type
+
     def extend_repr(self):
         str_info = f'loc = {self.loc}, scale = {self.scale}'
         return str_info
@@ -90,18 +108,22 @@ class GumbelCDF(Bijector):
 
     def _forward(self, x):
         x = self._check_value(x, 'value')
+        x = self.cast(x, self.parameter_type)
         z = (x - self.loc) / self.scale
         return self.exp(-self.exp(-z))
 
     def _inverse(self, y):
         y = self._check_value(y, 'value')
+        y = self.cast(y, self.parameter_type)
         return self.loc - self.scale * self.log(-self.log(y))
 
     def _forward_log_jacobian(self, x):
         x = self._check_value(x, 'value')
+        x = self.cast(x, self.parameter_type)
         z = (x - self.loc) / self.scale
         return -z - self.exp(-z) - self.log(self.scale)
 
     def _inverse_log_jacobian(self, y):
         y = self._check_value(y, 'value')
-        return self.log(self.scale / (-y * self.log(y)))
+        y = self.cast(y, self.parameter_type)
+        return self.log(self.scale / (-1. * y * self.log(y)))

mindspore/nn/probability/bijector/invert.py

@@ -57,11 +57,19 @@ class Invert(Bijector):
                                      name=name,
                                      param=param)
         self._bijector = bijector
+        if hasattr(self._bijector, 'event_shape'):
+            self._event_shape = self.bijector.event_shape
+        else:
+            self._event_shape = ()
 
     @property
     def bijector(self):
         return self._bijector
 
+    @property
+    def event_shape(self):
+        return self._event_shape
+
     def inverse(self, y):
         return self.bijector("forward", y)
 

mindspore/nn/probability/distribution/__init__.py

@@ -26,6 +26,7 @@ from .geometric import Geometric
 from .categorical import Categorical
 from .log_normal import LogNormal
 from .logistic import Logistic
+from .gumbel import Gumbel
 
 __all__ = ['Distribution',
            'TransformedDistribution',
@@ -37,4 +38,5 @@ __all__ = ['Distribution',
            'Geometric',
            'LogNormal',
            'Logistic',
+           'Gumbel',
            ]

mindspore/nn/probability/distribution/distribution.py

@@ -132,6 +132,10 @@ class Distribution(Cell):
     def broadcast_shape(self):
         return self._broadcast_shape
 
+    def _reset_parameters(self):
+        self.default_parameters = []
+        self.parameter_names = []
+
     def _add_parameter(self, value, name):
         """
         Cast `value` to a tensor and add it to `self.default_parameters`.

mindspore/nn/probability/distribution/gumbel.py

@@ -0,0 +1,249 @@
+# 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.
+# ============================================================================
+"""Gumbel Distribution"""
+import numpy as np
+from mindspore.ops import operations as P
+from mindspore._checkparam import Validator
+from mindspore.common import dtype as mstype
+import mindspore.nn as nn
+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, raise_not_implemented_util
+from ._utils.custom_ops import exp_generic, expm1_generic, log_generic
+
+class Gumbel(TransformedDistribution):
+    """
+    Gumbel distribution.
+
+    Args:
+        loc (int, float, list, numpy.ndarray, Tensor, Parameter): The location of Gumbel distribution.
+        scale (int, float, list, numpy.ndarray, Tensor, Parameter): The scale of Gumbel distribution.
+        seed (int): the seed used in sampling. The global seed is used if it is None. Default: None.
+        dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
+        name (str): the name of the distribution. Default: 'Gumbel'.
+
+    Note:
+        `scale` must be greater than zero.
+        `dist_spec_args` are `loc` and `scale`.
+        `dtype` must be a float type because Gumbel distributions are continuous.
+
+    Examples:
+        >>> # To initialize a Gumbel distribution of `loc` 3.0 and `scale` 4.0.
+        >>> gum = msd.Gumbel(3.0, 4.0, dtype=mstype.float32)
+        >>>
+        >>> # The following creates two independent Gumbel distributions.
+        >>> gum = msd.Gumbel([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
+        >>>
+        >>> # To use a Gumbel distribution in a network.
+        >>> class net(Cell):
+        >>>     def __init__(self):
+        >>>         super(net, self).__init__():
+        >>>         self.g1 = msd.Gumbel(0.0, 1.0, dtype=mstype.float32)
+        >>>
+        >>>     # The following calls are valid in construct.
+        >>>     def construct(self, value, loc_b, scale_b):
+        >>>
+        >>>         # Private interfaces of probability functions corresponding to public interfaces, including
+        >>>         # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same
+        >>>         # arguments as follows.
+        >>>         # Args:
+        >>>         #     value (Tensor): the value to be evaluated.
+        >>>
+        >>>         # Examples of `prob`.
+        >>>         # Similar calls can be made to other probability functions
+        >>>         # by replacing 'prob' by the name of the function.
+        >>>         ans = self.g1.prob(value)
+        >>>
+        >>>         # Functions `mean`, `mode`, sd`, `var`, and `entropy` do not take in any argument.
+        >>>         ans = self.g1.mean()
+        >>>         ans = self.g1.mode()
+        >>>         ans = self.g1.sd()
+        >>>         ans = self.g1.entropy()
+        >>>         ans = self.g1.var()
+        >>>
+        >>>         # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
+        >>>         # Args:
+        >>>         #     dist (str): the type of the distributions. Only "Gumbel" is supported.
+        >>>         #     loc_b (Tensor): the loc of distribution b.
+        >>>         #     scale_b (Tensor): the scale distribution b.
+        >>>
+        >>>         # Examples of `kl_loss`. `cross_entropy` is similar.
+        >>>         ans = self.g1.kl_loss('Gumbel', loc_b, scale_b)
+        >>>         ans = self.g1.cross_entropy('Gumbel', loc_b, scale_b)
+        >>>
+        >>>         # Examples of `sample`.
+        >>>         # Args:
+        >>>         #     shape (tuple): the shape of the sample. Default: ()
+        >>>
+        >>>         ans = self.g1.sample()
+        >>>         ans = self.g1.sample((2,3))
+    """
+
+    def __init__(self,
+                 loc,
+                 scale,
+                 seed=0,
+                 dtype=mstype.float32,
+                 name="Gumbel"):
+        """
+        Constructor of Gumbel distribution.
+        """
+        valid_dtype = mstype.float_type
+        Validator.check_type(type(self).__name__, dtype, valid_dtype)
+        gumbel_cdf = msb.GumbelCDF(loc, scale, dtype)
+        super(Gumbel, self).__init__(
+            distribution=msd.Uniform(0.0, 1.0, dtype=dtype),
+            bijector=msb.Invert(gumbel_cdf),
+            seed=seed, name=name)
+
+        self._parameter_type = gumbel_cdf.parameter_type
+        self._broadcast_shape = gumbel_cdf.event_shape
+        if self._broadcast_shape != ():
+            self._is_scalar_batch = False
+
+        # overwrite default_parameters and parameter_names
+        self._reset_parameters()
+        self._loc = self._add_parameter(loc, 'loc')
+        self._scale = self._add_parameter(scale, 'scale')
+        self._gumbel_bijector = gumbel_cdf
+
+        # ops needed for the class
+        self.cast = P.Cast()
+        self.const = P.ScalarToArray()
+        self.exp = exp_generic
+        self.expm1 = expm1_generic
+        self.fill = P.Fill()
+        self.lgamma = nn.LGamma()
+        self.log = log_generic
+        self.shape = P.Shape()
+        self.sqrt = P.Sqrt()
+
+    @property
+    def loc(self):
+        return self._loc
+
+    @property
+    def scale(self):
+        return self._scale
+
+    def extend_repr(self):
+        if self.is_scalar_batch:
+            str_info = f'loc = {self._loc}, scale = {self._scale}'
+        else:
+            str_info = f'batch_shape = {self._broadcast_shape}'
+        return str_info
+
+    def _mean(self):
+        r"""
+        The mean of the distribution.
+
+        .. math::
+            MEAN(X) = loc + scale * Euler-Mascheroni_constant
+        """
+        return self.loc + self.scale * np.euler_gamma
+
+    def _mode(self):
+        """
+        The mode of the distribution.
+        """
+        return self.loc * self.fill(self.parameter_type, self.shape(self.scale), 1.0)
+
+    def _sd(self):
+        r"""
+        The standard deviation of the distribution.
+
+        .. math::
+            STD(X) = \frac{\pi}{\sqrt(6)} * scale
+        """
+        scale = self.scale * self.fill(self.parameter_type, self.broadcast_shape, 1.0)
+        return scale * np.pi / self.sqrt(self.const(6.))
+
+    def _entropy(self):
+        r"""
+        Evaluate entropy.
+
+        .. math::
+            H(X) = 1. + \log(scale) + Euler-Mascheroni_constant
+        """
+        scale = self.scale * self.fill(self.parameter_type, self.broadcast_shape, 1.0)
+        return 1. + self.log(scale) + np.euler_gamma
+
+    def _log_prob(self, value):
+        r"""
+        .. math::
+            log_pdf(X) = -(z + \exp(-z)) - \log(scale)
+                where z = \frac{x - loc}{scale}
+        """
+        value = self._check_value(value, 'value')
+        z = (value - self.loc) / self.scale
+        return -(z + self.exp(-z)) - self.log(self.scale)
+
+    def _cdf(self, value):
+        r"""
+        .. math::
+            cdf_pdf(X) = \exp(-\exp(-\frac{x - loc}{scale})
+        """
+        return self._gumbel_bijector("forward", value)
+
+    def _cross_entropy(self, dist, loc_b, scale_b):
+        r"""
+        Evaluate cross entropy between Gumbel distributions.
+
+        Args:
+            dist (str): The type of the distributions. Should be "Gumbel" in this case.
+            loc_b (Tensor): The loc of distribution b.
+            scale_b (Tensor): The scale of distribution b.
+        """
+        if self.device_target == 'GPU':
+            raise_not_implemented_util('On GPU backend, cross_entropy', self.name)
+        check_distribution_name(dist, 'Gumbel')
+        return self._entropy() + self._kl_loss(dist, loc_b, scale_b)
+
+    def _kl_loss(self, dist, loc_b, scale_b):
+        r"""
+        Evaluate Gumbel-Gumbel kl divergence, i.e. KL(a||b).
+
+        Args:
+            dist (str): The type of the distributions. Should be "Gumbel" in this case.
+            loc_b (Tensor): The loc of distribution b.
+            scale_b (Tensor): The scale of distribution b.
+
+        .. math::
+            KL(a||b) = \log(scale_b / scale_a) + Euler-Mascheroni_constant * (scale_a / scale_b - 1.) +
+                       \exp(\frac{(loc_b - loc_a)}{scale_b}) * \Gamma(scale_a / scale_b + 1.) - 1.
+        """
+        if self.device_target == 'GPU':
+            raise_not_implemented_util('On GPU backend, kl_loss', self.name)
+        check_distribution_name(dist, 'Gumbel')
+        loc_b = self._check_value(loc_b, 'loc_b')
+        scale_b = self._check_value(scale_b, 'scale_b')
+        loc_b = self.cast(loc_b, self.parameter_type)
+        scale_b = self.cast(scale_b, self.parameter_type)
+        return self.log(scale_b) - self.log(self.scale) +\
+               np.euler_gamma * (self.scale / scale_b - 1.) +\
+               self.expm1((loc_b - self.loc) / scale_b + self.lgamma(self.scale / scale_b + 1.))
+
+    def _sample(self, shape=()):
+        origin_shape = shape + self._broadcast_shape
+        if origin_shape == ():
+            sample_shape = (1,)
+        else:
+            sample_shape = origin_shape
+        org_sample = self.distribution("sample", sample_shape)
+        value = self.bijector("forward", org_sample)
+        if origin_shape == ():
+            value = self.squeeze(value)
+        return value

mindspore/nn/probability/distribution/transformed_distribution.py

@@ -82,11 +82,21 @@ class TransformedDistribution(Distribution):
         self._is_linear_transformation = bijector.is_constant_jacobian
         self.default_parameters = distribution.default_parameters
         self.parameter_names = distribution.parameter_names
+
         self.exp = exp_generic
         self.log = log_generic
         self.isnan = P.IsNan()
         self.equal_base = P.Equal()
         self.select_base = P.Select()
+        self.fill = P.Fill()
+
+        # check if batch shape of the distribution and event shape is broadcastable
+        if hasattr(self.bijector, 'event_shape'):
+            event_shape_tensor = self.fill(self.dtype, self.bijector.event_shape, 0.0)
+            broadcast_shape_tensor = self.fill(self.dtype, self.broadcast_shape, 0.0)
+            self._batch_event = (event_shape_tensor + broadcast_shape_tensor).shape
+        else:
+            self._batch_event = self.broadcast_shape
 
     @property
     def bijector(self):

tests/st/probability/distribution/test_gumbel.py

@@ -0,0 +1,303 @@
+# Copyright 2019 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.
+# ============================================================================
+"""test cases for Gumbel distribution"""
+import numpy as np
+from scipy import stats
+from scipy import special
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
+from mindspore import Tensor
+from mindspore import dtype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Prob(nn.Cell):
+    """
+    Test class: probability of Gumbel distribution.
+    """
+    def __init__(self):
+        super(Prob, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.gum.prob(x_)
+
+def test_pdf():
+    """
+    Test pdf.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    value = np.array([1.0, 2.0]).astype(np.float32)
+    expect_pdf = gumbel_benchmark.pdf(value).astype(np.float32)
+    pdf = Prob()
+    output = pdf(Tensor(value, dtype=dtype.float32))
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
+
+class LogProb(nn.Cell):
+    """
+    Test class: log probability of Gumbel distribution.
+    """
+    def __init__(self):
+        super(LogProb, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.gum.log_prob(x_)
+
+def test_log_likelihood():
+    """
+    Test log_pdf.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    expect_logpdf = gumbel_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
+    logprob = LogProb()
+    output = logprob(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
+
+class KL(nn.Cell):
+    """
+    Test class: kl_loss of Gumbel distribution.
+    """
+    def __init__(self):
+        super(KL, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([1.0, 2.0]), dtype=dtype.float32)
+
+    def construct(self, loc_b, scale_b):
+        return self.gum.kl_loss('Gumbel', loc_b, scale_b)
+
+def test_kl_loss():
+    """
+    Test kl_loss.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([1.0, 2.0]).astype(np.float32)
+
+    loc_b = np.array([1.0]).astype(np.float32)
+    scale_b = np.array([1.0, 2.0]).astype(np.float32)
+
+    expect_kl_loss = np.log(scale_b) - np.log(scale) +\
+               np.euler_gamma * (scale / scale_b - 1.) +\
+               np.expm1((loc_b - loc) / scale_b + special.loggamma(scale / scale_b + 1.))
+
+    kl_loss = KL()
+    loc_b = Tensor(loc_b, dtype=dtype.float32)
+    scale_b = Tensor(scale_b, dtype=dtype.float32)
+    output = kl_loss(loc_b, scale_b)
+    tol = 1e-5
+    assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
+
+class Basics(nn.Cell):
+    """
+    Test class: mean/sd/mode of Gumbel distribution.
+    """
+    def __init__(self):
+        super(Basics, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self):
+        return self.gum.mean(), self.gum.sd(), self.gum.mode()
+
+def test_basics():
+    """
+    Test mean/standard deviation/mode.
+    """
+    basics = Basics()
+    mean, sd, mode = basics()
+
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    expect_mean = gumbel_benchmark.mean().astype(np.float32)
+    expect_sd = gumbel_benchmark.std().astype(np.float32)
+    expect_mode = np.array([[0.0], [0.0]]).astype(np.float32)
+    tol = 1e-6
+    assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
+    assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
+    assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
+
+class Sampling(nn.Cell):
+    """
+    Test class: sample of Gumbel distribution.
+    """
+    def __init__(self, shape, seed=0):
+        super(Sampling, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([1.0, 2.0, 3.0]), dtype=dtype.float32, seed=seed)
+        self.shape = shape
+
+    def construct(self):
+        return self.gum.sample(self.shape)
+
+def test_sample():
+    """
+    Test sample.
+    """
+    shape = (2, 3)
+    seed = 10
+    sample = Sampling(shape, seed=seed)
+    output = sample()
+    assert output.shape == (2, 3, 3)
+
+class CDF(nn.Cell):
+    """
+    Test class: cdf of Gumbel distribution.
+    """
+    def __init__(self):
+        super(CDF, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.gum.cdf(x_)
+
+def test_cdf():
+    """
+    Test cdf.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    expect_cdf = gumbel_benchmark.cdf([1.0, 2.0]).astype(np.float32)
+    cdf = CDF()
+    output = cdf(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 2e-5
+    assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
+
+class LogCDF(nn.Cell):
+    """
+    Test class: log_cdf of Gumbel distribution.
+    """
+    def __init__(self):
+        super(LogCDF, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.gum.log_cdf(x_)
+
+def test_log_cdf():
+    """
+    Test log cdf.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    expect_logcdf = gumbel_benchmark.logcdf([1.0, 2.0]).astype(np.float32)
+    logcdf = LogCDF()
+    output = logcdf(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 1e-4
+    assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
+
+class SF(nn.Cell):
+    """
+    Test class: survival function of Gumbel distribution.
+    """
+    def __init__(self):
+        super(SF, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.gum.survival_function(x_)
+
+def test_survival():
+    """
+    Test log_survival.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    expect_survival = gumbel_benchmark.sf([1.0, 2.0]).astype(np.float32)
+    survival_function = SF()
+    output = survival_function(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 2e-5
+    assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
+
+class LogSF(nn.Cell):
+    """
+    Test class: log survival function of Gumbel distribution.
+    """
+    def __init__(self):
+        super(LogSF, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.gum.log_survival(x_)
+
+def test_log_survival():
+    """
+    Test log_survival.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    expect_log_survival = gumbel_benchmark.logsf([1.0, 2.0]).astype(np.float32)
+    log_survival = LogSF()
+    output = log_survival(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 5e-4
+    assert (np.abs(output.asnumpy() - expect_log_survival) < tol).all()
+
+class EntropyH(nn.Cell):
+    """
+    Test class: entropy of Gumbel distribution.
+    """
+    def __init__(self):
+        super(EntropyH, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self):
+        return self.gum.entropy()
+
+def test_entropy():
+    """
+    Test entropy.
+    """
+    loc = np.array([0.0]).astype(np.float32)
+    scale = np.array([[1.0], [2.0]]).astype(np.float32)
+    gumbel_benchmark = stats.gumbel_r(loc, scale)
+    expect_entropy = gumbel_benchmark.entropy().astype(np.float32)
+    entropy = EntropyH()
+    output = entropy()
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
+
+class CrossEntropy(nn.Cell):
+    """
+    Test class: cross entropy between Gumbel distributions.
+    """
+    def __init__(self):
+        super(CrossEntropy, self).__init__()
+        self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+
+    def construct(self, x_, y_):
+        entropy = self.gum.entropy()
+        kl_loss = self.gum.kl_loss('Gumbel', x_, y_)
+        h_sum_kl = entropy + kl_loss
+        cross_entropy = self.gum.cross_entropy('Gumbel', x_, y_)
+        return h_sum_kl - cross_entropy
+
+def test_cross_entropy():
+    """
+    Test cross_entropy.
+    """
+    cross_entropy = CrossEntropy()
+    loc = Tensor([1.0], dtype=dtype.float32)
+    scale = Tensor([1.0], dtype=dtype.float32)
+    diff = cross_entropy(loc, scale)
+    tol = 1e-6
+    assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()

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

@@ -0,0 +1,153 @@
+# 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.
+# ============================================================================
+"""
+Test nn.probability.distribution.gumbel.
+"""
+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
+
+def test_gumbel_shape_errpr():
+    """
+    Invalid shapes.
+    """
+    with pytest.raises(ValueError):
+        msd.Gumbel([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
+
+def test_type():
+    with pytest.raises(TypeError):
+        msd.Gumbel(0., 1., dtype=dtype.int32)
+
+def test_name():
+    with pytest.raises(TypeError):
+        msd.Gumbel(0., 1., name=1.0)
+
+def test_seed():
+    with pytest.raises(TypeError):
+        msd.Gumbel(0., 1., seed='seed')
+
+def test_scale():
+    with pytest.raises(ValueError):
+        msd.Gumbel(0., 0.)
+    with pytest.raises(ValueError):
+        msd.Gumbel(0., -1.)
+
+def test_arguments():
+    """
+    args passing during initialization.
+    """
+    l = msd.Gumbel([3.0], [4.0], dtype=dtype.float32)
+    assert isinstance(l, msd.Distribution)
+
+
+class GumbelProb(nn.Cell):
+    """
+    Gumbel distribution: initialize with loc/scale.
+    """
+    def __init__(self):
+        super(GumbelProb, self).__init__()
+        self.gumbel = msd.Gumbel(3.0, 4.0, dtype=dtype.float32)
+
+    def construct(self, value):
+        prob = self.gumbel.prob(value)
+        log_prob = self.gumbel.log_prob(value)
+        cdf = self.gumbel.cdf(value)
+        log_cdf = self.gumbel.log_cdf(value)
+        sf = self.gumbel.survival_function(value)
+        log_sf = self.gumbel.log_survival(value)
+        return prob + log_prob + cdf + log_cdf + sf + log_sf
+
+def test_gumbel_prob():
+    """
+    Test probability functions: passing value through construct.
+    """
+    net = GumbelProb()
+    value = Tensor([0.5, 1.0], dtype=dtype.float32)
+    ans = net(value)
+    assert isinstance(ans, Tensor)
+
+class KL(nn.Cell):
+    """
+    Test kl_loss.
+    """
+    def __init__(self):
+        super(KL, self).__init__()
+        self.gumbel = msd.Gumbel(3.0, 4.0)
+
+    def construct(self, mu, s):
+        kl = self.gumbel.kl_loss('Gumbel', mu, s)
+        cross_entropy = self.gumbel.cross_entropy('Gumbel', mu, s)
+        return kl + cross_entropy
+
+def test_kl_cross_entropy():
+    """
+    Test kl_loss and cross_entropy.
+    """
+    net = KL()
+    loc_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
+    scale_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
+    ans = net(loc_b, scale_b)
+    assert isinstance(ans, Tensor)
+
+
+class GumbelBasics(nn.Cell):
+    """
+    Test class: basic loc/scale function.
+    """
+    def __init__(self):
+        super(GumbelBasics, self).__init__()
+        self.gumbel = msd.Gumbel(3.0, 4.0, dtype=dtype.float32)
+
+    def construct(self):
+        mean = self.gumbel.mean()
+        sd = self.gumbel.sd()
+        mode = self.gumbel.mode()
+        entropy = self.gumbel.entropy()
+        return mean + sd + mode + entropy
+
+def test_bascis():
+    """
+    Test mean/sd/mode/entropy functionality of Gumbel.
+    """
+    net = GumbelBasics()
+    ans = net()
+    assert isinstance(ans, Tensor)
+
+
+class GumbelConstruct(nn.Cell):
+    """
+    Gumbel distribution: going through construct.
+    """
+    def __init__(self):
+        super(GumbelConstruct, self).__init__()
+        self.gumbel = msd.Gumbel(3.0, 4.0)
+
+
+    def construct(self, value):
+        prob = self.gumbel('prob', value)
+        prob1 = self.gumbel.prob(value)
+        return prob + prob1
+
+def test_gumbel_construct():
+    """
+    Test probability function going through construct.
+    """
+    net = GumbelConstruct()
+    value = Tensor([0.5, 1.0], dtype=dtype.float32)
+    ans = net(value)
+    assert isinstance(ans, Tensor)