【Hackathon No.8】 add gumbel distribution api

python/paddle/distribution/__init__.py

@@ -17,6 +17,7 @@
 from paddle.distribution.categorical import Categorical
 from paddle.distribution.dirichlet import Dirichlet
 from paddle.distribution.distribution import Distribution
+from paddle.distribution.gumbel import Gumbel
 from paddle.distribution.exponential_family import ExponentialFamily
 from paddle.distribution.independent import Independent
 from paddle.distribution.kl import kl_divergence, register_kl
@@ -31,7 +32,7 @@
 __all__ = [  # noqa
     'Beta', 'Categorical', 'Dirichlet', 'Distribution', 'ExponentialFamily',
     'Multinomial', 'Normal', 'Uniform', 'kl_divergence', 'register_kl',
-    'Independent', 'TransformedDistribution', 'Laplace'
+    'Independent', 'TransformedDistribution', 'Laplace', 'Gumbel'
 ]
 
 __all__.extend(transform.__all__)

python/paddle/distribution/gumbel.py

@@ -0,0 +1,289 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import paddle
+import numbers
+import math
+import numpy as np
+
+from paddle.distribution.uniform import Uniform
+from paddle.distribution.transformed_distribution import TransformedDistribution
+from paddle.distribution.transform import AffineTransform, ExpTransform
+
+try:
+    from collections.abc import Iterable
+except:
+    from collections import Iterable
+
+
+class Gumbel(TransformedDistribution):
+    r"""The Gumbel distribution with location `loc` and `scale` parameters.
+
+    Mathematical details
+
+    The probability density function (pdf) is
+
+    .. math::
+
+        pdf(x; mu, sigma) = exp(-(x - mu) / sigma - exp(-(x - mu) / sigma)) / sigma
+
+
+    In the above equation:
+
+    * :math:`loc = \mu`: is the mean.
+    * :math:`scale = \sigma`: is the std.
+
+    Args:
+        loc(int|float|tensor): The mean of gumbel distribution.The data type is int, float, tensor.
+        scale(int|float|tensor): The std of gumbel distribution.The data type is int, float, tensor.
+
+    Examples:
+
+        >>> example = Gumbel(paddle.to_tensor([0.0]), paddle.to_tensor([1.0]))
+        >>> example.sample()
+        Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+       [4.14814520])
+
+    """
+
+    def __init__(self, loc, scale):
+
+        self.batch_size_unknown = False
+        self.all_arg_is_float = False
+        self.loc = paddle.to_tensor(loc, dtype='float32')
+        self.scale = paddle.to_tensor(scale, dtype='float32')
+
+        finfo = np.finfo(dtype='float32')
+        if isinstance(loc, numbers.Number) and isinstance(
+                scale, numbers.Number):
+            self.base_dist = Uniform(float(finfo.tiny), float(1 - finfo.eps))
+        else:
+            self.base_dist = Uniform(
+                paddle.full_like(self.loc, float(finfo.tiny)),
+                paddle.full_like(self.loc, float(1 - finfo.eps)))
+
+        self.transforms = (ExpTransform(),
+                           AffineTransform(loc=paddle.to_tensor(
+                               0, dtype='float32'),
+                                           scale=-paddle.ones_like(self.scale)),
+                           ExpTransform(),
+                           AffineTransform(loc=self.loc, scale=-self.scale))
+
+        super(Gumbel, self).__init__(self.base_dist, self.transforms)
+
+    @property
+    def mean(self):
+        """Mean of distribution
+
+        The mean is
+
+        .. math::
+
+            mean = \mu + \sigma * γ
+
+        In the above equation:
+
+        * :math:`loc = \mu`: is the location parameter.
+        * :math:`scale = \sigma`: is the scale parameter.
+        * :math:`γ`: is the euler's constant.
+
+        Returns:
+            Tensor: mean value.
+
+        """
+        return self.loc + self.scale * np.euler_gamma
+
+    @property
+    def variance(self):
+        """Variance of distribution.
+
+        The variance is
+
+        .. math::
+
+            variance = \sigma^2 * \pi^2 / 6
+
+        In the above equation:
+
+        * :math:`scale = \sigma`: is the scale parameter.
+
+        Returns:
+            Tensor: The variance value.
+
+        """
+        temp = paddle.to_tensor(math.pi * math.pi, dtype='float32')
+
+        return paddle.pow(self.scale, 2) * temp / 6
+
+    @property
+    def stddev(self):
+        """Standard deviation of distribution
+
+        The standard deviation is
+
+        .. math::
+
+            stddev = \sqrt{\sigma^2 * \pi^2 / 6}
+
+        In the above equation:
+        * :math:`scale = \sigma`: is the scale parameter.
+
+        Returns:
+            Tensor: std value
+        """
+        return paddle.sqrt(self.variance)
+
+    def prob(self, value):
+        """Probability density/mass function
+
+        Args:
+          value (Tensor): The input tensor.
+
+        Returns:
+          Tensor: probability.The data type is same with value.
+
+        Examples:
+
+            >>> example = Gumbel(paddle.to_tensor([0.0]), paddle.to_tensor([1.0]))
+            >>> value = paddle.to_tensor([0.5])
+            >>> example.prob(value)
+            Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            [0.33070430])
+
+        """
+        if type(value) != type(self.loc):
+            raise TypeError('value type must be Tensor')
+
+        y = (self.loc - value) / self.scale
+        return paddle.exp(y - paddle.exp(y)) / self.scale
+
+    def log_prob(self, value):
+        """Log probability density/mass function.
+
+        Args:
+          value (Tensor): The input tensor.
+
+        Returns:
+          Tensor: log probability.The data type is same with value.
+
+        Examples:
+
+        >>> example = Gumbel(paddle.to_tensor([0.0]), paddle.to_tensor([1.0]))
+        >>> value = paddle.to_tensor([0.5])
+        >>> example.prob(value
+        Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+        [-1.10653067])
+
+        """
+        return paddle.log(self.prob(value))
+
+    def cdf(self, value):
+        """Cumulative distribution function.
+        Args:
+            value (Tensor): value to be evaluated.
+
+        Returns:
+            Tensor: cumulative probability of value.
+
+        Examples:
+
+        >>> example = Gumbel(paddle.to_tensor([0.0]), paddle.to_tensor([1.0]))
+        >>> value = paddle.to_tensor([0.5])
+        >>> example.cdf(value)
+        Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+       [0.54523915])
+
+        """
+        if value.dtype != self.loc.dtype:
+            value = paddle.cast(value, self.loc.dtype)
+
+        return paddle.exp(-paddle.exp(-(value - self.loc) / self.scale))
+
+    def entropy(self):
+        """Entropy of Gumbel distribution.
+
+        Returns:
+            Entropy of distribution.
+
+        Examples:
+
+        >>> example = Gumbel(paddle.to_tensor([0.0]), paddle.to_tensor([1.0]))
+        >>> example.entropy()
+        Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+       [1.57721567])
+
+        """
+        return paddle.log(self.scale) + 1 + np.euler_gamma
+
+    def sample(self, shape):
+        """Sample from ``TransformedDistribution``.
+
+        Args:
+            shape (list, optional): The sample shape. Defaults to ().
+
+        Returns:
+             Tensor: A tensor with prepended dimensions shape.The data type is float32.
+
+        Examples:
+
+        >>> example = Gumbel(paddle.to_tensor([0.0]), paddle.to_tensor([1.0]))
+        >>> m.sample()
+        Tensor(shape=[2, 1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+       [[0.39180365],
+        [2.69657302]])
+
+        """
+        if not isinstance(shape, Iterable):
+            raise TypeError('sample shape must be Iterable object.')
+
+        with paddle.no_grad():
+            return self.rsample(shape)
+
+    def rsample(self, shape):
+        """reparameterized sample
+        Args:
+          shape (list): 1D `int32`. Shape of the generated samples.
+          seed (int): Python integer number.
+
+        Returns:
+          Tensor: A tensor with prepended dimensions shape.The data type is float32.
+
+        Examples:
+
+        >>> example = Gumbel(paddle.to_tensor([0.0]), paddle.to_tensor([1.0]))
+        >>> example.rsample([2])
+       Tensor(shape=[2, 1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+       [[0.80463481],
+        [0.91893655]])
+
+        """
+        with paddle.no_grad():
+            x = self._base.sample(shape)
+
+            expTransform = paddle.distribution.ExpTransform()
+            affineTf1 = paddle.distribution.AffineTransform(
+                paddle.to_tensor(0, dtype='float32'),
+                -paddle.ones_like(self.scale))
+            affineTf2 = paddle.distribution.AffineTransform(
+                self.loc, -self.scale)
+
+            x = expTransform.inverse(x)
+
+            x = affineTf1.forward(x)
+
+            x = expTransform.inverse(x)
+
+            x = affineTf2.forward(x)
+
+            return x

python/paddle/distribution/uniform.py

@@ -130,6 +130,8 @@ def __init__(self, low, high, name=None):
                 self.low = tensor.cast(self.low, dtype=self.dtype)
                 self.high = tensor.cast(self.high, dtype=self.dtype)
 
+        super(Uniform, self).__init__(self.low.shape)
+
     def sample(self, shape, seed=0):
         """Generate samples of the specified shape.