move distribution.py into distribution package and split into different file...

move distribution.py into distribution package and split into different file for better scalability (#38047)

python/paddle/distribution/__init__.py

+# 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.
+
+from .categorical import Categorical
+from .distribution import Distribution
+from .normal import Normal
+from .uniform import Uniform
+
+__all__ = ['Categorical', 'Distribution', 'Normal', 'Uniform']

python/paddle/distribution/distribution.py

+#   Copyright (c) 2020 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.
+
+# TODO: define the distribution functions
+# __all__ = ['Categorical',
+#            'MultivariateNormalDiag',
+#            'Normal',
+#            'sampling_id',
+#            'Uniform']
+
+from __future__ import print_function
+
+import math
+import warnings
+
+import numpy as np
+from paddle import _C_ops
+
+from ..fluid import core
+from ..fluid.data_feeder import (check_dtype, check_type,
+                                 check_variable_and_dtype, convert_dtype)
+from ..fluid.framework import in_dygraph_mode
+from ..fluid.layers import (control_flow, elementwise_add, elementwise_div,
+                            elementwise_mul, elementwise_sub, nn, ops, tensor)
+from ..tensor import arange, concat, gather_nd, multinomial
+
+
+class Distribution(object):
+    """
+    The abstract base class for probability distributions. Functions are 
+    implemented in specific distributions.
+    """
+
+    def __init__(self):
+        super(Distribution, self).__init__()
+
+    def sample(self):
+        """Sampling from the distribution."""
+        raise NotImplementedError
+
+    def entropy(self):
+        """The entropy of the distribution."""
+        raise NotImplementedError
+
+    def kl_divergence(self, other):
+        """The KL-divergence between self distributions and other."""
+        raise NotImplementedError
+
+    def log_prob(self, value):
+        """Log probability density/mass function."""
+        raise NotImplementedError
+
+    def probs(self, value):
+        """Probability density/mass function."""
+        raise NotImplementedError
+
+    def _validate_args(self, *args):
+        """
+        Argument validation for distribution args
+        Args:
+            value (float, list, numpy.ndarray, Tensor)
+        Raises
+            ValueError: if one argument is Tensor, all arguments should be Tensor
+        """
+        is_variable = False
+        is_number = False
+        for arg in args:
+            if isinstance(arg, tensor.Variable):
+                is_variable = True
+            else:
+                is_number = True
+
+        if is_variable and is_number:
+            raise ValueError(
+                'if one argument is Tensor, all arguments should be Tensor')
+
+        return is_variable
+
+    def _to_tensor(self, *args):
+        """
+        Argument convert args to Tensor
+
+        Args:
+            value (float, list, numpy.ndarray, Tensor)
+        Returns:
+            Tensor of args.
+        """
+        numpy_args = []
+        variable_args = []
+        tmp = 0.
+
+        for arg in args:
+            if isinstance(arg, float):
+                arg = [arg]
+            if not isinstance(arg, (list, tuple, np.ndarray, tensor.Variable)):
+                raise TypeError(
+                    "Type of input args must be float, list, numpy.ndarray or Tensor, but received type {}".
+                    format(type(arg)))
+
+            arg_np = np.array(arg)
+            arg_dtype = arg_np.dtype
+            if str(arg_dtype) != 'float32':
+                if str(arg_dtype) != 'float64':
+                    # "assign" op doesn't support float64. if dtype is float64, float32 variable will be generated
+                    #  and converted to float64 later using "cast".
+                    warnings.warn(
+                        "data type of argument only support float32 and float64, your argument will be convert to float32."
+                    )
+                arg_np = arg_np.astype('float32')
+            # tmp is used to support broadcast, it summarizes shapes of all the args and get the mixed shape.
+            tmp = tmp + arg_np
+            numpy_args.append(arg_np)
+
+        dtype = tmp.dtype
+        for arg in numpy_args:
+            arg_broadcasted, _ = np.broadcast_arrays(arg, tmp)
+            arg_variable = tensor.create_tensor(dtype=dtype)
+            tensor.assign(arg_broadcasted, arg_variable)
+            variable_args.append(arg_variable)
+
+        return tuple(variable_args)
+
+    def _check_values_dtype_in_probs(self, param, value):
+        """
+        Log_prob and probs methods have input ``value``, if value's dtype is different from param,
+        convert value's dtype to be consistent with param's dtype.
+
+        Args:
+            param (Tensor): low and high in Uniform class, loc and scale in Normal class.
+            value (Tensor): The input tensor.
+
+        Returns:
+            value (Tensor): Change value's dtype if value's dtype is different from param.
+        """
+        if in_dygraph_mode():
+            if value.dtype != param.dtype and convert_dtype(
+                    value.dtype) in ['float32', 'float64']:
+                warnings.warn(
+                    "dtype of input 'value' needs to be the same as parameters of distribution class. dtype of 'value' will be converted."
+                )
+                return _C_ops.cast(value, 'in_dtype', value.dtype, 'out_dtype',
+                                   param.dtype)
+            return value
+
+        check_variable_and_dtype(value, 'value', ['float32', 'float64'],
+                                 'log_prob')
+        if value.dtype != param.dtype:
+            warnings.warn(
+                "dtype of input 'value' needs to be the same as parameters of distribution class. dtype of 'value' will be converted."
+            )
+            return tensor.cast(value, dtype=param.dtype)
+        return value

python/paddle/distribution/normal.py

+# 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 math
+import warnings
+
+import numpy as np
+from paddle import _C_ops
+
+from ..fluid import core
+from ..fluid.data_feeder import (check_dtype, check_type,
+                                 check_variable_and_dtype, convert_dtype)
+from ..fluid.framework import in_dygraph_mode
+from ..fluid.layers import (control_flow, elementwise_add, elementwise_div,
+                            elementwise_mul, elementwise_sub, nn, ops, tensor)
+from ..tensor import arange, concat, gather_nd, multinomial
+from .distribution import Distribution
+
+
+class Normal(Distribution):
+    r"""The Normal distribution with location `loc` and `scale` parameters.
+
+    Mathematical details
+
+    The probability density function (pdf) is
+
+    .. math::
+
+        pdf(x; \mu, \sigma) = \\frac{1}{Z}e^{\\frac {-0.5 (x - \mu)^2}  {\sigma^2} }
+
+    .. math::
+
+        Z = (2 \pi \sigma^2)^{0.5}
+
+    In the above equation:
+
+    * :math:`loc = \mu`: is the mean.
+    * :math:`scale = \sigma`: is the std.
+    * :math:`Z`: is the normalization constant.
+
+    Args:
+        loc(int|float|list|tuple|numpy.ndarray|Tensor): The mean of normal distribution.The data type is int, float, list, numpy.ndarray or Tensor.
+        scale(int|float|list|tuple|numpy.ndarray|Tensor): The std of normal distribution.The data type is int, float, list, numpy.ndarray or Tensor.
+        name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Examples:
+        .. code-block:: python
+          
+          import paddle
+          from paddle.distribution import Normal
+
+          # Define a single scalar Normal distribution.
+          dist = Normal(loc=0., scale=3.)
+          # Define a batch of two scalar valued Normals.
+          # The first has mean 1 and standard deviation 11, the second 2 and 22.
+          dist = Normal(loc=[1., 2.], scale=[11., 22.])
+          # Get 3 samples, returning a 3 x 2 tensor.
+          dist.sample([3])
+
+          # Define a batch of two scalar valued Normals.
+          # Both have mean 1, but different standard deviations.
+          dist = Normal(loc=1., scale=[11., 22.])
+
+          # Complete example
+          value_tensor = paddle.to_tensor([0.8], dtype="float32")
+
+          normal_a = Normal([0.], [1.])
+          normal_b = Normal([0.5], [2.])
+          sample = normal_a.sample([2])
+          # a random tensor created by normal distribution with shape: [2, 1]
+          entropy = normal_a.entropy()
+          # [1.4189385] with shape: [1]
+          lp = normal_a.log_prob(value_tensor)
+          # [-1.2389386] with shape: [1]
+          p = normal_a.probs(value_tensor)
+          # [0.28969154] with shape: [1]
+          kl = normal_a.kl_divergence(normal_b)
+          # [0.34939718] with shape: [1]
+    """
+
+    def __init__(self, loc, scale, name=None):
+        if not in_dygraph_mode():
+            check_type(loc, 'loc',
+                       (int, float, np.ndarray, tensor.Variable, list, tuple),
+                       'Normal')
+            check_type(scale, 'scale',
+                       (int, float, np.ndarray, tensor.Variable, list, tuple),
+                       'Normal')
+
+        self.batch_size_unknown = False
+        self.all_arg_is_float = False
+        self.name = name if name is not None else 'Normal'
+        self.dtype = 'float32'
+
+        if isinstance(loc, int):
+            loc = float(loc)
+        if isinstance(scale, int):
+            scale = float(scale)
+
+        if self._validate_args(loc, scale):
+            self.batch_size_unknown = True
+            self.loc = loc
+            self.scale = scale
+            self.dtype = convert_dtype(loc.dtype)
+        else:
+            if isinstance(loc, float) and isinstance(scale, float):
+                self.all_arg_is_float = True
+            if isinstance(
+                    loc,
+                    np.ndarray) and str(loc.dtype) in ['float32', 'float64']:
+                self.dtype = loc.dtype
+            elif isinstance(
+                    scale,
+                    np.ndarray) and str(scale.dtype) in ['float32', 'float64']:
+                self.dtype = scale.dtype
+            # pylint: disable=unbalanced-tuple-unpacking
+            self.loc, self.scale = self._to_tensor(loc, scale)
+            if self.dtype != convert_dtype(self.loc.dtype):
+                self.loc = tensor.cast(self.loc, dtype=self.dtype)
+                self.scale = tensor.cast(self.scale, dtype=self.dtype)
+
+    def sample(self, shape, seed=0):
+        """Generate samples of the specified shape.
+
+        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.
+
+        """
+        if not in_dygraph_mode():
+            check_type(shape, 'shape', (list), 'sample')
+            check_type(seed, 'seed', (int), 'sample')
+
+        batch_shape = list((self.loc + self.scale).shape)
+        name = self.name + '_sample'
+
+        if self.batch_size_unknown:
+            output_shape = shape + batch_shape
+            zero_tmp = tensor.fill_constant_batch_size_like(
+                self.loc + self.scale, batch_shape + shape, self.dtype, 0.)
+            zero_tmp_reshape = nn.reshape(zero_tmp, output_shape)
+            zero_tmp_shape = nn.shape(zero_tmp_reshape)
+            normal_random_tmp = nn.gaussian_random(
+                zero_tmp_shape, mean=0., std=1., seed=seed, dtype=self.dtype)
+            output = normal_random_tmp * (zero_tmp_reshape + self.scale)
+            output = elementwise_add(output, self.loc, name=name)
+            return output
+        else:
+            output_shape = shape + batch_shape
+            output = nn.gaussian_random(output_shape, mean=0., std=1., seed=seed, dtype=self.dtype) * \
+                     (tensor.zeros(output_shape, dtype=self.dtype) + self.scale)
+            output = elementwise_add(output, self.loc, name=name)
+            if self.all_arg_is_float:
+                return nn.reshape(output, shape, name=name)
+            else:
+                return output
+
+    def entropy(self):
+        r"""Shannon entropy in nats.
+
+        The entropy is
+
+        .. math::
+
+            entropy(\sigma) = 0.5 \\log (2 \pi e \sigma^2)
+
+        In the above equation:
+
+        * :math:`scale = \sigma`: is the std.
+
+        Returns:
+          Tensor: Shannon entropy of normal distribution.The data type is float32.
+
+        """
+        name = self.name + '_entropy'
+        batch_shape = list((self.loc + self.scale).shape)
+        zero_tmp = tensor.fill_constant_batch_size_like(
+            self.loc + self.scale, batch_shape, self.dtype, 0.)
+        return elementwise_add(
+            0.5 + zero_tmp,
+            0.5 * math.log(2 * math.pi) + nn.log((self.scale + zero_tmp)),
+            name=name)
+
+    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.
+
+        """
+        name = self.name + '_log_prob'
+        value = self._check_values_dtype_in_probs(self.loc, value)
+
+        var = self.scale * self.scale
+        log_scale = nn.log(self.scale)
+        return elementwise_sub(
+            -1. * ((value - self.loc) * (value - self.loc)) / (2. * var),
+            log_scale + math.log(math.sqrt(2. * math.pi)),
+            name=name)
+
+    def probs(self, value):
+        """Probability density/mass function.
+
+        Args:
+          value (Tensor): The input tensor.
+
+        Returns:
+          Tensor: probability.The data type is same with value.
+
+        """
+        name = self.name + '_probs'
+        value = self._check_values_dtype_in_probs(self.loc, value)
+
+        var = self.scale * self.scale
+        return elementwise_div(
+            ops.exp(-1. * ((value - self.loc) * (value - self.loc)) /
+                    (2. * var)), (math.sqrt(2 * math.pi) * self.scale),
+            name=name)
+
+    def kl_divergence(self, other):
+        r"""The KL-divergence between two normal distributions.
+
+        The probability density function (pdf) is
+
+        .. math::
+
+            KL\_divergence(\mu_0, \sigma_0; \mu_1, \sigma_1) = 0.5 (ratio^2 + (\\frac{diff}{\sigma_1})^2 - 1 - 2 \\ln {ratio})
+
+        .. math::
+
+            ratio = \\frac{\sigma_0}{\sigma_1}
+        
+        .. math::
+
+            diff = \mu_1 - \mu_0
+
+        In the above equation:
+
+        * :math:`loc = \mu_0`: is the mean of current Normal distribution.
+        * :math:`scale = \sigma_0`: is the std of current Normal distribution.
+        * :math:`loc = \mu_1`: is the mean of other Normal distribution.
+        * :math:`scale = \sigma_1`: is the std of other Normal distribution.
+        * :math:`ratio`: is the ratio of scales.
+        * :math:`diff`: is the difference between means.
+
+        Args:
+            other (Normal): instance of Normal.
+
+        Returns:
+            Tensor: kl-divergence between two normal distributions.The data type is float32.
+
+        """
+        if not in_dygraph_mode():
+            check_type(other, 'other', Normal, 'kl_divergence')
+
+        name = self.name + '_kl_divergence'
+        var_ratio = self.scale / other.scale
+        var_ratio = (var_ratio * var_ratio)
+        t1 = (self.loc - other.loc) / other.scale
+        t1 = (t1 * t1)
+        return elementwise_add(
+            0.5 * var_ratio, 0.5 * (t1 - 1. - nn.log(var_ratio)), name=name)

python/paddle/distribution/uniform.py

+# 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 math
+import warnings
+
+import numpy as np
+from paddle import _C_ops
+
+from ..fluid import core
+from ..fluid.data_feeder import (check_dtype, check_type,
+                                 check_variable_and_dtype, convert_dtype)
+from ..fluid.framework import in_dygraph_mode
+from ..fluid.layers import (control_flow, elementwise_add, elementwise_div,
+                            elementwise_mul, elementwise_sub, nn, ops, tensor)
+from ..tensor import arange, concat, gather_nd, multinomial
+from .distribution import Distribution
+
+
+class Uniform(Distribution):
+    r"""Uniform distribution with `low` and `high` parameters.
+
+    Mathematical Details
+
+    The probability density function (pdf) is
+
+    .. math::
+
+        pdf(x; a, b) = \\frac{1}{Z}, \ a <=x <b
+
+    .. math::
+
+        Z = b - a
+
+    In the above equation:
+
+    * :math:`low = a`,
+    * :math:`high = b`,
+    * :math:`Z`: is the normalizing constant.
+
+    The parameters `low` and `high` must be shaped in a way that supports
+    [broadcasting](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basic_concept/broadcasting_en.html) (e.g., `high - low` is a valid operation).
+
+    Args:
+        low(int|float|list|tuple|numpy.ndarray|Tensor): The lower boundary of uniform distribution.The data type is int, float, list, numpy.ndarray or Tensor
+        high(int|float|list|tuple|numpy.ndarray|Tensor): The higher boundary of uniform distribution.The data type is int, float, list, numpy.ndarray or Tensor
+        name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Examples:
+        .. code-block:: python
+
+          import paddle
+          from paddle.distribution import Uniform
+
+          # Without broadcasting, a single uniform distribution [3, 4]:
+          u1 = Uniform(low=3.0, high=4.0)
+          # 2 distributions [1, 3], [2, 4]
+          u2 = Uniform(low=[1.0, 2.0], high=[3.0, 4.0])
+          # 4 distributions
+          u3 = Uniform(low=[[1.0, 2.0], [3.0, 4.0]],
+                    high=[[1.5, 2.5], [3.5, 4.5]])
+
+          # With broadcasting:
+          u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])
+
+          # Complete example
+          value_tensor = paddle.to_tensor([0.8], dtype="float32")
+
+          uniform = Uniform([0.], [2.])
+
+          sample = uniform.sample([2])
+          # a random tensor created by uniform distribution with shape: [2, 1]
+          entropy = uniform.entropy()
+          # [0.6931472] with shape: [1]
+          lp = uniform.log_prob(value_tensor)
+          # [-0.6931472] with shape: [1]
+          p = uniform.probs(value_tensor)
+          # [0.5] with shape: [1]
+    """
+
+    def __init__(self, low, high, name=None):
+        if not in_dygraph_mode():
+            check_type(low, 'low',
+                       (int, float, np.ndarray, tensor.Variable, list, tuple),
+                       'Uniform')
+            check_type(high, 'high',
+                       (int, float, np.ndarray, tensor.Variable, list, tuple),
+                       'Uniform')
+
+        self.all_arg_is_float = False
+        self.batch_size_unknown = False
+        self.name = name if name is not None else 'Uniform'
+        self.dtype = 'float32'
+
+        if isinstance(low, int):
+            low = float(low)
+        if isinstance(high, int):
+            high = float(high)
+
+        if self._validate_args(low, high):
+            self.batch_size_unknown = True
+            self.low = low
+            self.high = high
+            self.dtype = convert_dtype(low.dtype)
+        else:
+            if isinstance(low, float) and isinstance(high, float):
+                self.all_arg_is_float = True
+            if isinstance(
+                    low,
+                    np.ndarray) and str(low.dtype) in ['float32', 'float64']:
+                self.dtype = low.dtype
+            elif isinstance(
+                    high,
+                    np.ndarray) and str(high.dtype) in ['float32', 'float64']:
+                self.dtype = high.dtype
+            # pylint: disable=unbalanced-tuple-unpacking
+            self.low, self.high = self._to_tensor(low, high)
+            if self.dtype != convert_dtype(self.low.dtype):
+                self.low = tensor.cast(self.low, dtype=self.dtype)
+                self.high = tensor.cast(self.high, dtype=self.dtype)
+
+    def sample(self, shape, seed=0):
+        """Generate samples of the specified shape.
+
+        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.
+
+        """
+        if not in_dygraph_mode():
+            check_type(shape, 'shape', (list), 'sample')
+            check_type(seed, 'seed', (int), 'sample')
+
+        name = self.name + '_sample'
+        batch_shape = list((self.low + self.high).shape)
+        if self.batch_size_unknown:
+            output_shape = shape + batch_shape
+            zero_tmp = tensor.fill_constant_batch_size_like(
+                self.low + self.high, batch_shape + shape, self.dtype, 0.)
+            uniform_random_tmp = nn.uniform_random_batch_size_like(
+                zero_tmp,
+                zero_tmp.shape,
+                dtype=self.dtype,
+                min=0.,
+                max=1.,
+                seed=seed)
+            zero_tmp_reshape = nn.reshape(zero_tmp, output_shape)
+            uniform_random_tmp_reshape = nn.reshape(uniform_random_tmp,
+                                                    output_shape)
+            output = uniform_random_tmp_reshape * (
+                zero_tmp_reshape + self.high - self.low)
+            output = elementwise_add(output, self.low, name=name)
+            return output
+        else:
+            output_shape = shape + batch_shape
+            output = nn.uniform_random(
+                output_shape, dtype=self.dtype, min=0., max=1.,
+                seed=seed) * (tensor.zeros(
+                    output_shape, dtype=self.dtype) + (self.high - self.low))
+            output = elementwise_add(output, self.low, name=name)
+            if self.all_arg_is_float:
+                return nn.reshape(output, shape, name=name)
+            else:
+                return output
+
+    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.
+
+        """
+        value = self._check_values_dtype_in_probs(self.low, value)
+        if in_dygraph_mode():
+            # ensure value in [low, high]
+            lb_bool = self.low < value
+            ub_bool = value < self.high
+
+            lb = _C_ops.cast(lb_bool, 'in_dtype', lb_bool.dtype, 'out_dtype',
+                             value.dtype)
+            ub = _C_ops.cast(ub_bool, 'in_dtype', ub_bool.dtype, 'out_dtype',
+                             value.dtype)
+            return nn.log(lb * ub) - nn.log(self.high - self.low)
+
+        name = self.name + '_log_prob'
+        lb_bool = self.low < value
+        ub_bool = value < self.high
+        lb = tensor.cast(lb_bool, dtype=value.dtype)
+        ub = tensor.cast(ub_bool, dtype=value.dtype)
+        return elementwise_sub(
+            nn.log(lb * ub), nn.log(self.high - self.low), name=name)
+
+    def probs(self, value):
+        """Probability density/mass function.
+
+        Args:
+          value (Tensor): The input tensor.
+
+        Returns:
+          Tensor: probability.The data type is same with value.
+
+        """
+        value = self._check_values_dtype_in_probs(self.low, value)
+        if in_dygraph_mode():
+            lb_bool = self.low < value
+            ub_bool = value < self.high
+
+            lb = _C_ops.cast(lb_bool, 'in_dtype', lb_bool.dtype, 'out_dtype',
+                             value.dtype)
+            ub = _C_ops.cast(ub_bool, 'in_dtype', ub_bool.dtype, 'out_dtype',
+                             value.dtype)
+            return (lb * ub) / (self.high - self.low)
+
+        name = self.name + '_probs'
+        lb_bool = self.low < value
+        ub_bool = value < self.high
+        lb = tensor.cast(lb_bool, dtype=value.dtype)
+        ub = tensor.cast(ub_bool, dtype=value.dtype)
+        return elementwise_div((lb * ub), (self.high - self.low), name=name)
+
+    def entropy(self):
+        r"""Shannon entropy in nats.
+
+        The entropy is
+
+        .. math::
+
+            entropy(low, high) = \\log (high - low)
+
+        Returns:
+          Tensor: Shannon entropy of uniform distribution.The data type is float32.
+
+        """
+        name = self.name + '_entropy'
+        return nn.log(self.high - self.low, name=name)

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -772,6 +772,7 @@ add_subdirectory(sequence)
 add_subdirectory(dygraph_to_static)
 add_subdirectory(rnn)
 add_subdirectory(autograd)
+add_subdirectory(distribution)
 
 if (NOT WIN32 OR NOT WITH_GPU)
     add_subdirectory(fft)

python/paddle/fluid/tests/unittests/distribution/CMakeLists.txt

+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+foreach(TEST_OP ${TEST_OPS})
+    py_test_modules(${TEST_OP} MODULES ${TEST_OP})
+endforeach(TEST_OP)

python/paddle/fluid/tests/unittests/distribution/test_distribution.py

+#   Copyright (c) 2020 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 math
+import unittest
+
+import numpy as np
+import paddle
+from paddle import fluid
+from paddle.distribution import *
+from paddle.fluid import layers
+
+paddle.enable_static()
+
+
+class DistributionNumpy():
+    def sample(self):
+        raise NotImplementedError
+
+    def entropy(self):
+        raise NotImplementedError
+
+    def kl_divergence(self, other):
+        raise NotImplementedError
+
+    def log_prob(self, value):
+        raise NotImplementedError
+
+    def probs(self, value):
+        raise NotImplementedError
+
+
+class DistributionTestName(unittest.TestCase):
+    def get_prefix(self, string):
+        return (string.split('.')[0])
+
+    def test_normal_name(self):
+        name = 'test_normal'
+        normal1 = Normal(0.0, 1.0, name=name)
+        self.assertEqual(normal1.name, name)
+
+        normal2 = Normal(0.0, 1.0)
+        self.assertEqual(normal2.name, 'Normal')
+
+        paddle.enable_static()
+
+        sample = normal1.sample([2])
+        self.assertEqual(self.get_prefix(sample.name), name + '_sample')
+
+        entropy = normal1.entropy()
+        self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
+
+        value_npdata = np.array([0.8], dtype="float32")
+        value_tensor = layers.create_tensor(dtype="float32")
+        layers.assign(value_npdata, value_tensor)
+
+        lp = normal1.log_prob(value_tensor)
+        self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
+
+        p = normal1.probs(value_tensor)
+        self.assertEqual(self.get_prefix(p.name), name + '_probs')
+
+        kl = normal1.kl_divergence(normal2)
+        self.assertEqual(self.get_prefix(kl.name), name + '_kl_divergence')
+
+    def test_uniform_name(self):
+        name = 'test_uniform'
+        uniform1 = Uniform(0.0, 1.0, name=name)
+        self.assertEqual(uniform1.name, name)
+
+        uniform2 = Uniform(0.0, 1.0)
+        self.assertEqual(uniform2.name, 'Uniform')
+
+        paddle.enable_static()
+
+        sample = uniform1.sample([2])
+        self.assertEqual(self.get_prefix(sample.name), name + '_sample')
+
+        entropy = uniform1.entropy()
+        self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
+
+        value_npdata = np.array([0.8], dtype="float32")
+        value_tensor = layers.create_tensor(dtype="float32")
+        layers.assign(value_npdata, value_tensor)
+
+        lp = uniform1.log_prob(value_tensor)
+        self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
+
+        p = uniform1.probs(value_tensor)
+        self.assertEqual(self.get_prefix(p.name), name + '_probs')
+
+    def test_categorical_name(self):
+        name = 'test_categorical'
+        categorical1 = Categorical([0.4, 0.6], name=name)
+        self.assertEqual(categorical1.name, name)
+
+        categorical2 = Categorical([0.5, 0.5])
+        self.assertEqual(categorical2.name, 'Categorical')
+
+        paddle.enable_static()
+
+        sample = categorical1.sample([2])
+        self.assertEqual(self.get_prefix(sample.name), name + '_sample')
+
+        entropy = categorical1.entropy()
+        self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
+
+        kl = categorical1.kl_divergence(categorical2)
+        self.assertEqual(self.get_prefix(kl.name), name + '_kl_divergence')
+
+        value_npdata = np.array([0], dtype="int64")
+        value_tensor = layers.create_tensor(dtype="int64")
+        layers.assign(value_npdata, value_tensor)
+
+        p = categorical1.probs(value_tensor)
+        self.assertEqual(self.get_prefix(p.name), name + '_probs')
+
+        lp = categorical1.log_prob(value_tensor)
+        self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')

python/paddle/fluid/tests/unittests/distribution/test_distribution_categorical.py

+# 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 math
+import unittest
+
+import numpy as np
+import paddle
+from paddle import fluid
+from paddle.distribution import *
+from paddle.fluid import layers
+
+from test_distribution import DistributionNumpy
+
+
+class CategoricalNumpy(DistributionNumpy):
+    def __init__(self, logits):
+        self.logits = np.array(logits).astype('float32')
+
+    def entropy(self):
+        logits = self.logits - np.max(self.logits, axis=-1, keepdims=True)
+        e_logits = np.exp(logits)
+        z = np.sum(e_logits, axis=-1, keepdims=True)
+        prob = e_logits / z
+        return -1. * np.sum(prob * (logits - np.log(z)), axis=-1, keepdims=True)
+
+    def kl_divergence(self, other):
+        logits = self.logits - np.max(self.logits, axis=-1, keepdims=True)
+        other_logits = other.logits - np.max(
+            other.logits, axis=-1, keepdims=True)
+        e_logits = np.exp(logits)
+        other_e_logits = np.exp(other_logits)
+        z = np.sum(e_logits, axis=-1, keepdims=True)
+        other_z = np.sum(other_e_logits, axis=-1, keepdims=True)
+        prob = e_logits / z
+        return np.sum(prob *
+                      (logits - np.log(z) - other_logits + np.log(other_z)),
+                      axis=-1,
+                      keepdims=True)
+
+
+class CategoricalTest(unittest.TestCase):
+    def setUp(self, use_gpu=False, batch_size=3, dims=5):
+        self.use_gpu = use_gpu
+        if not use_gpu:
+            self.place = fluid.CPUPlace()
+            self.gpu_id = -1
+        else:
+            self.place = fluid.CUDAPlace(0)
+            self.gpu_id = 0
+
+        self.batch_size = batch_size
+        self.dims = dims
+        self.init_numpy_data(batch_size, dims)
+
+        paddle.disable_static(self.place)
+        self.init_dynamic_data(batch_size, dims)
+
+        paddle.enable_static()
+        self.test_program = fluid.Program()
+        self.executor = fluid.Executor(self.place)
+        self.init_static_data(batch_size, dims)
+
+    def init_numpy_data(self, batch_size, dims):
+        # input logtis is 2-D Tensor
+        # value used in probs and log_prob method is 1-D Tensor
+        self.logits_np = np.random.rand(batch_size, dims).astype('float32')
+        self.other_logits_np = np.random.rand(batch_size,
+                                              dims).astype('float32')
+        self.value_np = np.array([2, 1, 3]).astype('int64')
+
+        self.logits_shape = [batch_size, dims]
+        # dist_shape = logits_shape[:-1], it represents the number of
+        #  different distributions.
+        self.dist_shape = [batch_size]
+        # sample shape represents the number of samples
+        self.sample_shape = [2, 4]
+        # value used in probs and log_prob method
+        # If value is 1-D and logits is 2-D or higher dimension, value will be
+        #  broadcasted to have the same number of distributions with logits.
+        # If value is 2-D or higher dimentsion, it should have the same number
+        #  of distributions with logtis. ``value[:-1] = logits[:-1]
+        self.value_shape = [3]
+
+    def init_dynamic_data(self, batch_size, dims):
+        self.logits = paddle.to_tensor(self.logits_np)
+        self.other_logits = paddle.to_tensor(self.other_logits_np)
+        self.value = paddle.to_tensor(self.value_np)
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.logits_static = fluid.data(
+                name='logits', shape=self.logits_shape, dtype='float32')
+            self.other_logits_static = fluid.data(
+                name='other_logits', shape=self.logits_shape, dtype='float32')
+            self.value_static = fluid.data(
+                name='value', shape=self.value_shape, dtype='int64')
+
+    def get_numpy_selected_probs(self, probability):
+        np_probs = np.zeros(self.dist_shape + self.value_shape)
+        for i in range(self.batch_size):
+            for j in range(3):
+                np_probs[i][j] = probability[i][self.value_np[j]]
+        return np_probs
+
+    def compare_with_numpy(self, fetch_list, tolerance=1e-6):
+        sample, entropy, kl, probs, log_prob = fetch_list
+        log_tolerance = 1e-4
+
+        np.testing.assert_equal(sample.shape,
+                                self.sample_shape + self.dist_shape)
+
+        np_categorical = CategoricalNumpy(self.logits_np)
+        np_other_categorical = CategoricalNumpy(self.other_logits_np)
+        np_entropy = np_categorical.entropy()
+        np_kl = np_categorical.kl_divergence(np_other_categorical)
+
+        np.testing.assert_allclose(
+            entropy, np_entropy, rtol=log_tolerance, atol=log_tolerance)
+        np.testing.assert_allclose(
+            kl, np_kl, rtol=log_tolerance, atol=log_tolerance)
+
+        sum_dist = np.sum(self.logits_np, axis=-1, keepdims=True)
+        probability = self.logits_np / sum_dist
+        np_probs = self.get_numpy_selected_probs(probability)
+        np_log_prob = np.log(np_probs)
+
+        np.testing.assert_allclose(
+            probs, np_probs, rtol=tolerance, atol=tolerance)
+        np.testing.assert_allclose(
+            log_prob, np_log_prob, rtol=tolerance, atol=tolerance)
+
+    def test_categorical_distribution_dygraph(self, tolerance=1e-6):
+        paddle.disable_static(self.place)
+        categorical = Categorical(self.logits)
+        other_categorical = Categorical(self.other_logits)
+
+        sample = categorical.sample(self.sample_shape).numpy()
+        entropy = categorical.entropy().numpy()
+        kl = categorical.kl_divergence(other_categorical).numpy()
+        probs = categorical.probs(self.value).numpy()
+        log_prob = categorical.log_prob(self.value).numpy()
+
+        fetch_list = [sample, entropy, kl, probs, log_prob]
+        self.compare_with_numpy(fetch_list)
+
+    def test_categorical_distribution_static(self, tolerance=1e-6):
+        paddle.enable_static()
+        with fluid.program_guard(self.test_program):
+            categorical = Categorical(self.logits_static)
+            other_categorical = Categorical(self.other_logits_static)
+
+            sample = categorical.sample(self.sample_shape)
+            entropy = categorical.entropy()
+            kl = categorical.kl_divergence(other_categorical)
+            probs = categorical.probs(self.value_static)
+            log_prob = categorical.log_prob(self.value_static)
+
+            fetch_list = [sample, entropy, kl, probs, log_prob]
+
+        feed_vars = {
+            'logits': self.logits_np,
+            'other_logits': self.other_logits_np,
+            'value': self.value_np
+        }
+
+        self.executor.run(fluid.default_startup_program())
+        fetch_list = self.executor.run(program=self.test_program,
+                                       feed=feed_vars,
+                                       fetch_list=fetch_list)
+
+        self.compare_with_numpy(fetch_list)
+
+
+class CategoricalTest2(CategoricalTest):
+    def init_numpy_data(self, batch_size, dims):
+        # input logtis is 2-D Tensor with dtype Float64
+        # value used in probs and log_prob method is 1-D Tensor
+        self.logits_np = np.random.rand(batch_size, dims).astype('float64')
+        self.other_logits_np = np.random.rand(batch_size,
+                                              dims).astype('float64')
+        self.value_np = np.array([2, 1, 3]).astype('int64')
+
+        self.logits_shape = [batch_size, dims]
+        self.dist_shape = [batch_size]
+        self.sample_shape = [2, 4]
+        self.value_shape = [3]
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.logits_static = fluid.data(
+                name='logits', shape=self.logits_shape, dtype='float64')
+            self.other_logits_static = fluid.data(
+                name='other_logits', shape=self.logits_shape, dtype='float64')
+            self.value_static = fluid.data(
+                name='value', shape=self.value_shape, dtype='int64')
+
+
+class CategoricalTest3(CategoricalTest):
+    def init_dynamic_data(self, batch_size, dims):
+        # input logtis is 2-D numpy.ndarray with dtype Float32
+        # value used in probs and log_prob method is 1-D Tensor
+        self.logits = self.logits_np
+        self.other_logits = self.other_logits_np
+        self.value = paddle.to_tensor(self.value_np)
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.logits_static = self.logits_np
+            self.other_logits_static = self.other_logits_np
+            self.value_static = fluid.data(
+                name='value', shape=self.value_shape, dtype='int64')
+
+
+class CategoricalTest4(CategoricalTest):
+    def init_numpy_data(self, batch_size, dims):
+        # input logtis is 2-D numpy.ndarray with dtype Float64
+        # value used in probs and log_prob method is 1-D Tensor
+        self.logits_np = np.random.rand(batch_size, dims).astype('float64')
+        self.other_logits_np = np.random.rand(batch_size,
+                                              dims).astype('float64')
+        self.value_np = np.array([2, 1, 3]).astype('int64')
+
+        self.logits_shape = [batch_size, dims]
+        self.dist_shape = [batch_size]
+        self.sample_shape = [2, 4]
+        self.value_shape = [3]
+
+    def init_dynamic_data(self, batch_size, dims):
+        self.logits = self.logits_np
+        self.other_logits = self.other_logits_np
+        self.value = paddle.to_tensor(self.value_np)
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.logits_static = self.logits_np
+            self.other_logits_static = self.other_logits_np
+            self.value_static = fluid.data(
+                name='value', shape=self.value_shape, dtype='int64')
+
+
+# test shape of logits and value used in probs and log_prob method
+class CategoricalTest5(CategoricalTest):
+    def init_numpy_data(self, batch_size, dims):
+        # input logtis is 1-D Tensor
+        # value used in probs and log_prob method is 1-D Tensor
+        self.logits_np = np.random.rand(dims).astype('float32')
+        self.other_logits_np = np.random.rand(dims).astype('float32')
+        self.value_np = np.array([2, 1, 3]).astype('int64')
+
+        self.logits_shape = [dims]
+        self.dist_shape = []
+        self.sample_shape = [2, 4]
+        self.value_shape = [3]
+
+    def get_numpy_selected_probs(self, probability):
+        np_probs = np.zeros(self.value_shape)
+        for i in range(3):
+            np_probs[i] = probability[self.value_np[i]]
+        return np_probs
+
+
+class CategoricalTest6(CategoricalTest):
+    def init_numpy_data(self, batch_size, dims):
+        # input logtis is 2-D Tensor
+        # value used in probs and log_prob method has the same number of batches with input
+        self.logits_np = np.random.rand(3, 5).astype('float32')
+        self.other_logits_np = np.random.rand(3, 5).astype('float32')
+        self.value_np = np.array([[2, 1], [0, 3], [2, 3]]).astype('int64')
+
+        self.logits_shape = [3, 5]
+        self.dist_shape = [3]
+        self.sample_shape = [2, 4]
+        self.value_shape = [3, 2]
+
+    def get_numpy_selected_probs(self, probability):
+        np_probs = np.zeros(self.value_shape)
+        for i in range(3):
+            for j in range(2):
+                np_probs[i][j] = probability[i][self.value_np[i][j]]
+        return np_probs
+
+
+class CategoricalTest7(CategoricalTest):
+    def init_numpy_data(self, batch_size, dims):
+        # input logtis is 3-D Tensor
+        # value used in probs and log_prob method has the same number of distribuions with input
+        self.logits_np = np.random.rand(3, 2, 5).astype('float32')
+        self.other_logits_np = np.random.rand(3, 2, 5).astype('float32')
+        self.value_np = np.array([2, 1, 3]).astype('int64')
+
+        self.logits_shape = [3, 2, 5]
+        self.dist_shape = [3, 2]
+        self.sample_shape = [2, 4]
+        self.value_shape = [3]
+
+    def get_numpy_selected_probs(self, probability):
+        np_probs = np.zeros(self.dist_shape + self.value_shape)
+        for i in range(3):
+            for j in range(2):
+                for k in range(3):
+                    np_probs[i][j][k] = probability[i][j][self.value_np[k]]
+        return np_probs
+
+
+class CategoricalTest8(CategoricalTest):
+    def init_dynamic_data(self, batch_size, dims):
+        # input logtis is 2-D list
+        # value used in probs and log_prob method is 1-D Tensor
+        self.logits = self.logits_np.tolist()
+        self.other_logits = self.other_logits_np.tolist()
+        self.value = paddle.to_tensor(self.value_np)
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.logits_static = self.logits_np.tolist()
+            self.other_logits_static = self.other_logits_np.tolist()
+            self.value_static = fluid.data(
+                name='value', shape=self.value_shape, dtype='int64')
+
+
+class CategoricalTest9(CategoricalTest):
+    def init_dynamic_data(self, batch_size, dims):
+        # input logtis is 2-D tuple
+        # value used in probs and log_prob method is 1-D Tensor
+        self.logits = tuple(self.logits_np.tolist())
+        self.other_logits = tuple(self.other_logits_np.tolist())
+        self.value = paddle.to_tensor(self.value_np)
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.logits_static = tuple(self.logits_np.tolist())
+            self.other_logits_static = tuple(self.other_logits_np.tolist())
+            self.value_static = fluid.data(
+                name='value', shape=self.value_shape, dtype='int64')
+
+
+class DistributionTestError(unittest.TestCase):
+    def test_distribution_error(self):
+        distribution = Distribution()
+
+        self.assertRaises(NotImplementedError, distribution.sample)
+        self.assertRaises(NotImplementedError, distribution.entropy)
+
+        normal = Normal(0.0, 1.0)
+        self.assertRaises(NotImplementedError, distribution.kl_divergence,
+                          normal)
+
+        value_npdata = np.array([0.8], dtype="float32")
+        value_tensor = layers.create_tensor(dtype="float32")
+        self.assertRaises(NotImplementedError, distribution.log_prob,
+                          value_tensor)
+        self.assertRaises(NotImplementedError, distribution.probs, value_tensor)
+
+    def test_normal_error(self):
+        paddle.enable_static()
+        normal = Normal(0.0, 1.0)
+
+        value = [1.0, 2.0]
+        # type of value must be variable
+        self.assertRaises(TypeError, normal.log_prob, value)
+
+        value = [1.0, 2.0]
+        # type of value must be variable
+        self.assertRaises(TypeError, normal.probs, value)
+
+        shape = 1.0
+        # type of shape must be list
+        self.assertRaises(TypeError, normal.sample, shape)
+
+        seed = 1.0
+        # type of seed must be int
+        self.assertRaises(TypeError, normal.sample, [2, 3], seed)
+
+        normal_other = Uniform(1.0, 2.0)
+        # type of other must be an instance of Normal
+        self.assertRaises(TypeError, normal.kl_divergence, normal_other)
+
+    def test_uniform_error(self):
+        paddle.enable_static()
+        uniform = Uniform(0.0, 1.0)
+
+        value = [1.0, 2.0]
+        # type of value must be variable
+        self.assertRaises(TypeError, uniform.log_prob, value)
+
+        value = [1.0, 2.0]
+        # type of value must be variable
+        self.assertRaises(TypeError, uniform.probs, value)
+
+        shape = 1.0
+        # type of shape must be list
+        self.assertRaises(TypeError, uniform.sample, shape)
+
+        seed = 1.0
+        # type of seed must be int
+        self.assertRaises(TypeError, uniform.sample, [2, 3], seed)
+
+    def test_categorical_error(self):
+        paddle.enable_static()
+
+        categorical = Categorical([0.4, 0.6])
+
+        value = [1, 0]
+        # type of value must be variable
+        self.assertRaises(AttributeError, categorical.log_prob, value)
+
+        value = [1, 0]
+        # type of value must be variable
+        self.assertRaises(AttributeError, categorical.probs, value)
+
+        shape = 1.0
+        # type of shape must be list
+        self.assertRaises(TypeError, categorical.sample, shape)
+
+        categorical_other = Uniform(1.0, 2.0)
+        # type of other must be an instance of Categorical
+        self.assertRaises(TypeError, categorical.kl_divergence,
+                          categorical_other)
+
+        def test_shape_not_match_error():
+            # shape of value must match shape of logits
+            # value_shape[:-1] == logits_shape[:-1]
+            paddle.disable_static()
+            logits = paddle.rand([3, 5])
+            cat = Categorical(logits)
+            value = paddle.to_tensor([[2, 1, 3], [3, 2, 1]], dtype='int64')
+            cat.log_prob(value)
+
+        self.assertRaises(ValueError, test_shape_not_match_error)

python/paddle/fluid/tests/unittests/distribution/test_distribution_uniform.py

+# 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 math
+import unittest
+
+import numpy as np
+import paddle
+from paddle import fluid
+from paddle.distribution import *
+from paddle.fluid import layers
+
+from test_distribution import DistributionNumpy
+
+
+class UniformNumpy(DistributionNumpy):
+    def __init__(self, low, high):
+        self.low = np.array(low)
+        self.high = np.array(high)
+        if str(self.low.dtype) not in ['float32', 'float64']:
+            self.low = self.low.astype('float32')
+            self.high = self.high.astype('float32')
+
+    def sample(self, shape):
+        shape = tuple(shape) + (self.low + self.high).shape
+        return self.low + (np.random.uniform(size=shape) *
+                           (self.high - self.low))
+
+    def log_prob(self, value):
+        lb = np.less(self.low, value).astype(self.low.dtype)
+        ub = np.less(value, self.high).astype(self.low.dtype)
+        return np.log(lb * ub) - np.log(self.high - self.low)
+
+    def probs(self, value):
+        lb = np.less(self.low, value).astype(self.low.dtype)
+        ub = np.less(value, self.high).astype(self.low.dtype)
+        return (lb * ub) / (self.high - self.low)
+
+    def entropy(self):
+        return np.log(self.high - self.low)
+
+
+class UniformTest(unittest.TestCase):
+    def setUp(self, use_gpu=False, batch_size=5, dims=6):
+        self.use_gpu = use_gpu
+        if not use_gpu:
+            self.place = fluid.CPUPlace()
+            self.gpu_id = -1
+        else:
+            self.place = fluid.CUDAPlace(0)
+            self.gpu_id = 0
+
+        self.init_numpy_data(batch_size, dims)
+
+        paddle.disable_static(self.place)
+        self.init_dynamic_data(batch_size, dims)
+
+        paddle.enable_static()
+        self.test_program = fluid.Program()
+        self.executor = fluid.Executor(self.place)
+        self.init_static_data(batch_size, dims)
+
+    def init_numpy_data(self, batch_size, dims):
+        # low ans high are 'float'
+        self.low_np = np.random.uniform(-2, 1)
+        self.high_np = np.random.uniform(2, 4)
+        self.values_np = np.array([1.0]).astype('float32')
+
+    def init_dynamic_data(self, batch_size, dims):
+        self.dynamic_low = self.low_np
+        self.dynamic_high = self.high_np
+        self.dynamic_values = paddle.to_tensor(self.values_np)
+
+    def init_static_data(self, batch_size, dims):
+        self.static_low = self.low_np
+        self.static_high = self.high_np
+        with fluid.program_guard(self.test_program):
+            self.static_values = layers.data(
+                name='values', shape=[], dtype='float32')
+
+    def compare_with_numpy(self, fetch_list, sample_shape=7, tolerance=1e-6):
+        sample, entropy, log_prob, probs = fetch_list
+
+        np_uniform = UniformNumpy(self.low_np, self.high_np)
+        np_sample = np_uniform.sample([sample_shape])
+        np_entropy = np_uniform.entropy()
+        np_lp = np_uniform.log_prob(self.values_np)
+        np_p = np_uniform.probs(self.values_np)
+
+        np.testing.assert_equal(sample.shape, np_sample.shape)
+        np.testing.assert_allclose(
+            entropy, np_entropy, rtol=tolerance, atol=tolerance)
+        np.testing.assert_allclose(
+            log_prob, np_lp, rtol=tolerance, atol=tolerance)
+        np.testing.assert_allclose(probs, np_p, rtol=tolerance, atol=tolerance)
+
+    def test_uniform_distribution_dygraph(self, sample_shape=7, tolerance=1e-6):
+        paddle.disable_static(self.place)
+        uniform = Uniform(self.dynamic_low, self.dynamic_high)
+        sample = uniform.sample([sample_shape]).numpy()
+        entropy = uniform.entropy().numpy()
+        log_prob = uniform.log_prob(self.dynamic_values).numpy()
+        probs = uniform.probs(self.dynamic_values).numpy()
+        fetch_list = [sample, entropy, log_prob, probs]
+
+        self.compare_with_numpy(fetch_list)
+
+    def test_uniform_distribution_static(self, sample_shape=7, tolerance=1e-6):
+        paddle.enable_static()
+        with fluid.program_guard(self.test_program):
+            uniform = Uniform(self.static_low, self.static_high)
+            sample = uniform.sample([sample_shape])
+            entropy = uniform.entropy()
+            log_prob = uniform.log_prob(self.static_values)
+            probs = uniform.probs(self.static_values)
+            fetch_list = [sample, entropy, log_prob, probs]
+
+        feed_vars = {
+            'low': self.low_np,
+            'high': self.high_np,
+            'values': self.values_np
+        }
+
+        self.executor.run(fluid.default_startup_program())
+        fetch_list = self.executor.run(program=self.test_program,
+                                       feed=feed_vars,
+                                       fetch_list=fetch_list)
+
+        self.compare_with_numpy(fetch_list)
+
+
+class UniformTest2(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low ans high are 'int'
+        self.low_np = int(np.random.uniform(-2, 1))
+        self.high_np = int(np.random.uniform(2, 4))
+        self.values_np = np.array([1.0]).astype('float32')
+
+
+class UniformTest3(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # test broadcast: low is float, high is numpy.ndarray with dtype 'float32'.
+        self.low_np = np.random.uniform(-2, 1)
+        self.high_np = np.random.uniform(5.0, 15.0,
+                                         (batch_size, dims)).astype('float32')
+        self.values_np = np.random.randn(batch_size, dims).astype('float32')
+
+    def init_static_data(self, batch_size, dims):
+        self.static_low = self.low_np
+        self.static_high = self.high_np
+        with fluid.program_guard(self.test_program):
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float32')
+
+
+class UniformTest4(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are numpy.ndarray with dtype 'float32'.
+        self.low_np = np.random.randn(batch_size, dims).astype('float32')
+        self.high_np = np.random.uniform(5.0, 15.0,
+                                         (batch_size, dims)).astype('float32')
+        self.values_np = np.random.randn(batch_size, dims).astype('float32')
+
+    def init_static_data(self, batch_size, dims):
+        self.static_low = self.low_np
+        self.static_high = self.high_np
+        with fluid.program_guard(self.test_program):
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float32')
+
+
+class UniformTest5(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are numpy.ndarray with dtype 'float64'.
+        self.low_np = np.random.randn(batch_size, dims).astype('float64')
+        self.high_np = np.random.uniform(5.0, 15.0,
+                                         (batch_size, dims)).astype('float64')
+        self.values_np = np.random.randn(batch_size, dims).astype('float64')
+
+    def init_dynamic_data(self, batch_size, dims):
+        self.dynamic_low = self.low_np
+        self.dynamic_high = self.high_np
+        self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64')
+
+    def init_static_data(self, batch_size, dims):
+        self.static_low = self.low_np
+        self.static_high = self.high_np
+        with fluid.program_guard(self.test_program):
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float64')
+
+
+class UniformTest6(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are Tensor with dtype 'VarType.FP32'.
+        self.low_np = np.random.randn(batch_size, dims).astype('float32')
+        self.high_np = np.random.uniform(5.0, 15.0,
+                                         (batch_size, dims)).astype('float32')
+        self.values_np = np.random.randn(batch_size, dims).astype('float32')
+
+    def init_dynamic_data(self, batch_size, dims):
+        self.dynamic_low = paddle.to_tensor(self.low_np)
+        self.dynamic_high = paddle.to_tensor(self.high_np)
+        self.dynamic_values = paddle.to_tensor(self.values_np)
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.static_low = layers.data(
+                name='low', shape=[dims], dtype='float32')
+            self.static_high = layers.data(
+                name='high', shape=[dims], dtype='float32')
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float32')
+
+
+class UniformTest7(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are Tensor with dtype 'VarType.FP64'.
+        self.low_np = np.random.randn(batch_size, dims).astype('float64')
+        self.high_np = np.random.uniform(5.0, 15.0,
+                                         (batch_size, dims)).astype('float64')
+        self.values_np = np.random.randn(batch_size, dims).astype('float64')
+
+    def init_dynamic_data(self, batch_size, dims):
+        self.dynamic_low = paddle.to_tensor(self.low_np, dtype='float64')
+        self.dynamic_high = paddle.to_tensor(self.high_np, dtype='float64')
+        self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64')
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.static_low = layers.data(
+                name='low', shape=[dims], dtype='float64')
+            self.static_high = layers.data(
+                name='high', shape=[dims], dtype='float64')
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float64')
+
+
+class UniformTest8(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are Tensor with dtype 'VarType.FP64'. value's dtype is 'VarType.FP32'.
+        self.low_np = np.random.randn(batch_size, dims).astype('float64')
+        self.high_np = np.random.uniform(5.0, 15.0,
+                                         (batch_size, dims)).astype('float64')
+        self.values_np = np.random.randn(batch_size, dims).astype('float32')
+
+    def init_dynamic_data(self, batch_size, dims):
+        self.dynamic_low = paddle.to_tensor(self.low_np, dtype='float64')
+        self.dynamic_high = paddle.to_tensor(self.high_np, dtype='float64')
+        self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float32')
+
+    def init_static_data(self, batch_size, dims):
+        with fluid.program_guard(self.test_program):
+            self.static_low = layers.data(
+                name='low', shape=[dims], dtype='float64')
+            self.static_high = layers.data(
+                name='high', shape=[dims], dtype='float64')
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float32')
+
+
+class UniformTest9(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are numpy.ndarray with dtype 'float32'.
+        # high < low.
+        self.low_np = np.random.randn(batch_size, dims).astype('float32')
+        self.high_np = np.random.uniform(-10.0, -5.0,
+                                         (batch_size, dims)).astype('float32')
+        self.values_np = np.random.randn(batch_size, dims).astype('float32')
+
+    def init_static_data(self, batch_size, dims):
+        self.static_low = self.low_np
+        self.static_high = self.high_np
+        with fluid.program_guard(self.test_program):
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float32')
+
+
+class UniformTest10(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are list.
+        self.low_np = np.random.randn(batch_size,
+                                      dims).astype('float32').tolist()
+        self.high_np = np.random.uniform(
+            5.0, 15.0, (batch_size, dims)).astype('float32').tolist()
+        self.values_np = np.random.randn(batch_size, dims).astype('float32')
+
+    def init_static_data(self, batch_size, dims):
+        self.static_low = self.low_np
+        self.static_high = self.high_np
+        with fluid.program_guard(self.test_program):
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float32')
+
+
+class UniformTest11(UniformTest):
+    def init_numpy_data(self, batch_size, dims):
+        # low and high are tuple.
+        self.low_np = tuple(
+            np.random.randn(batch_size, dims).astype('float32').tolist())
+        self.high_np = tuple(
+            np.random.uniform(5.0, 15.0, (batch_size, dims)).astype('float32')
+            .tolist())
+        self.values_np = np.random.randn(batch_size, dims).astype('float32')
+
+    def init_static_data(self, batch_size, dims):
+        self.static_low = self.low_np
+        self.static_high = self.high_np
+        with fluid.program_guard(self.test_program):
+            self.static_values = layers.data(
+                name='values', shape=[dims], dtype='float32')
+
+
+class UniformTestSample(unittest.TestCase):
+    def setUp(self):
+        self.init_param()
+
+    def init_param(self):
+        self.low = 3.0
+        self.high = 4.0
+
+    def test_uniform_sample(self):
+        paddle.disable_static()
+        uniform = Uniform(low=self.low, high=self.high)
+        s = uniform.sample([100])
+        self.assertTrue((s >= self.low).all())
+        self.assertTrue((s < self.high).all())
+        paddle.enable_static()
+
+
+class UniformTestSample2(UniformTestSample):
+    def init_param(self):
+        self.low = -5.0
+        self.high = 2.0

python/setup.py.in

@@ -276,6 +276,7 @@ packages=['paddle',
           'paddle.incubate.tensor',
           'paddle.incubate.nn',
           'paddle.incubate.passes',
+          'paddle.distribution',
           'paddle.distributed.fleet',
           'paddle.distributed.fleet.base',
           'paddle.distributed.fleet.elastic',