move distribution.py into distribution package for better scalability

python/paddle/distribution/__init__.py

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+# 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/categorical.py

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+# 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 Categorical(Distribution):
+ r"""
+ Categorical distribution is a discrete probability distribution that 
+ describes the possible results of a random variable that can take on 
+ one of K possible categories, with the probability of each category 
+ separately specified.
+
+ The probability mass function (pmf) is:
+
+ .. math::
+
+ pmf(k; p_i) = \prod_{i=1}^{k} p_i^{[x=i]}
+
+ In the above equation:
+
+ * :math:`[x=i]` : it evaluates to 1 if :math:`x==i` , 0 otherwise.
+
+ Args:
+ logits(list|tuple|numpy.ndarray|Tensor): The logits input of categorical distribution. The data type is float32 or float64.
+ 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 Categorical
+
+ paddle.seed(100) # on CPU device
+ x = paddle.rand([6])
+ print(x)
+ # [0.5535528 0.20714243 0.01162981
+ # 0.51577556 0.36369765 0.2609165 ]
+
+ paddle.seed(200) # on CPU device
+ y = paddle.rand([6])
+ print(y)
+ # [0.77663314 0.90824795 0.15685187
+ # 0.04279523 0.34468332 0.7955718 ]
+
+ cat = Categorical(x)
+ cat2 = Categorical(y)
+
+ paddle.seed(1000) # on CPU device
+ cat.sample([2,3])
+ # [[0, 0, 5],
+ # [3, 4, 5]]
+
+ cat.entropy()
+ # [1.77528]
+
+ cat.kl_divergence(cat2)
+ # [0.071952]
+
+ value = paddle.to_tensor([2,1,3])
+ cat.probs(value)
+ # [0.00608027 0.108298 0.269656]
+
+ cat.log_prob(value)
+ # [-5.10271 -2.22287 -1.31061]
+
+ """
+
+ def __init__(self, logits, name=None):
+ """
+ Args:
+ logits(list|tuple|numpy.ndarray|Tensor): The logits input of categorical distribution. The data type is float32 or float64.
+ name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+ """
+ if not in_dygraph_mode():
+ check_type(logits, 'logits',
+ (np.ndarray, tensor.Variable, list, tuple),
+ 'Categorical')
+
+ self.name = name if name is not None else 'Categorical'
+ self.dtype = 'float32'
+
+ if self._validate_args(logits):
+ self.logits = logits
+ self.dtype = convert_dtype(logits.dtype)
+ else:
+ if isinstance(logits, np.ndarray) and str(
+ logits.dtype) in ['float32', 'float64']:
+ self.dtype = logits.dtype
+ self.logits = self._to_tensor(logits)[0]
+ if self.dtype != convert_dtype(self.logits.dtype):
+ self.logits = tensor.cast(self.logits, dtype=self.dtype)
+
+ def sample(self, shape):
+ """Generate samples of the specified shape.
+
+ Args:
+ shape (list): Shape of the generated samples.
+
+ Returns:
+ Tensor: A tensor with prepended dimensions shape.
+
+ Examples:
+ .. code-block:: python
+
+ import paddle
+ from paddle.distribution import Categorical
+
+ paddle.seed(100) # on CPU device
+ x = paddle.rand([6])
+ print(x)
+ # [0.5535528 0.20714243 0.01162981
+ # 0.51577556 0.36369765 0.2609165 ]
+
+ cat = Categorical(x)
+
+ paddle.seed(1000) # on CPU device
+ cat.sample([2,3])
+ # [[0, 0, 5],
+ # [3, 4, 5]]
+
+ """
+ name = self.name + '_sample'
+ if not in_dygraph_mode():
+ check_type(shape, 'shape', (list), 'sample')
+
+ num_samples = np.prod(np.array(shape))
+
+ logits_shape = list(self.logits.shape)
+ if len(logits_shape) > 1:
+ sample_shape = shape + logits_shape[:-1]
+ logits = nn.reshape(self.logits,
+ [np.prod(logits_shape[:-1]), logits_shape[-1]])
+ else:
+ sample_shape = shape
+ logits = self.logits
+
+ sample_index = multinomial(logits, num_samples, True)
+ return nn.reshape(sample_index, sample_shape, name=name)
+
+ def kl_divergence(self, other):
+ """The KL-divergence between two Categorical distributions.
+
+ Args:
+ other (Categorical): instance of Categorical. The data type is float32.
+
+ Returns:
+ Tensor: kl-divergence between two Categorical distributions.
+
+ Examples:
+ .. code-block:: python
+
+ import paddle
+ from paddle.distribution import Categorical
+
+ paddle.seed(100) # on CPU device
+ x = paddle.rand([6])
+ print(x)
+ # [0.5535528 0.20714243 0.01162981
+ # 0.51577556 0.36369765 0.2609165 ]
+
+ paddle.seed(200) # on CPU device
+ y = paddle.rand([6])
+ print(y)
+ # [0.77663314 0.90824795 0.15685187
+ # 0.04279523 0.34468332 0.7955718 ]
+
+ cat = Categorical(x)
+ cat2 = Categorical(y)
+
+ cat.kl_divergence(cat2)
+ # [0.071952]
+
+ """
+ name = self.name + '_kl_divergence'
+ if not in_dygraph_mode():
+ check_type(other, 'other', Categorical, 'kl_divergence')
+
+ logits = self.logits - nn.reduce_max(self.logits, dim=-1, keep_dim=True)
+ other_logits = other.logits - nn.reduce_max(
+ other.logits, dim=-1, keep_dim=True)
+ e_logits = ops.exp(logits)
+ other_e_logits = ops.exp(other_logits)
+ z = nn.reduce_sum(e_logits, dim=-1, keep_dim=True)
+ other_z = nn.reduce_sum(other_e_logits, dim=-1, keep_dim=True)
+ prob = e_logits / z
+ kl = nn.reduce_sum(
+ prob * (logits - nn.log(z) - other_logits + nn.log(other_z)),
+ dim=-1,
+ keep_dim=True,
+ name=name)
+
+ return kl
+
+ def entropy(self):
+ """Shannon entropy in nats.
+
+ Returns:
+ Tensor: Shannon entropy of Categorical distribution. The data type is float32.
+
+ Examples:
+ .. code-block:: python
+
+ import paddle
+ from paddle.distribution import Categorical
+
+ paddle.seed(100) # on CPU device
+ x = paddle.rand([6])
+ print(x)
+ # [0.5535528 0.20714243 0.01162981
+ # 0.51577556 0.36369765 0.2609165 ]
+
+ cat = Categorical(x)
+
+ cat.entropy()
+ # [1.77528]
+
+ """
+ name = self.name + '_entropy'
+ logits = self.logits - nn.reduce_max(self.logits, dim=-1, keep_dim=True)
+ e_logits = ops.exp(logits)
+ z = nn.reduce_sum(e_logits, dim=-1, keep_dim=True)
+ prob = e_logits / z
+
+ neg_entropy = nn.reduce_sum(
+ prob * (logits - nn.log(z)), dim=-1, keep_dim=True)
+ entropy = nn.scale(neg_entropy, scale=-1.0, name=name)
+ return entropy
+
+ def probs(self, value):
+ """Probabilities of the given category (``value``).
+
+ If ``logits`` is 2-D or higher dimension, the last dimension will be regarded as 
+ category, and the others represents the different distributions.
+ At the same time, if ``vlaue`` is 1-D Tensor, ``value`` will be broadcast to the 
+ same number of distributions as ``logits``.
+ If ``value`` is not 1-D Tensor, ``value`` should have the same number distributions
+ with ``logits. That is, ``value[:-1] = logits[:-1]``.
+
+ Args:
+ value (Tensor): The input tensor represents the selected category index.
+
+ Returns:
+ Tensor: probability according to the category index.
+
+ Examples:
+ .. code-block:: python
+
+ import paddle
+ from paddle.distribution import Categorical
+
+ paddle.seed(100) # on CPU device
+ x = paddle.rand([6])
+ print(x)
+ # [0.5535528 0.20714243 0.01162981
+ # 0.51577556 0.36369765 0.2609165 ]
+
+ cat = Categorical(x)
+
+ value = paddle.to_tensor([2,1,3])
+ cat.probs(value)
+ # [0.00608027 0.108298 0.269656]
+
+ """
+ name = self.name + '_probs'
+
+ dist_sum = nn.reduce_sum(self.logits, dim=-1, keep_dim=True)
+ prob = self.logits / dist_sum
+
+ shape = list(prob.shape)
+ value_shape = list(value.shape)
+ if len(shape) == 1:
+ num_value_in_one_dist = np.prod(value_shape)
+ index_value = nn.reshape(value, [num_value_in_one_dist, 1])
+ index = index_value
+ else:
+ num_dist = np.prod(shape[:-1])
+ num_value_in_one_dist = value_shape[-1]
+ prob = nn.reshape(prob, [num_dist, shape[-1]])
+ if len(value_shape) == 1:
+ value = nn.expand(value, [num_dist])
+ value_shape = shape[:-1] + value_shape
+ index_value = nn.reshape(value, [num_dist, -1, 1])
+ if shape[:-1] != value_shape[:-1]:
+ raise ValueError(
+ "shape of value {} must match shape of logits {}".format(
+ str(value_shape[:-1]), str(shape[:-1])))
+
+ index_prefix = nn.unsqueeze(
+ arange(
+ num_dist, dtype=index_value.dtype), axes=-1)
+ index_prefix = nn.expand(index_prefix, [1, num_value_in_one_dist])
+ index_prefix = nn.unsqueeze(index_prefix, axes=-1)
+
+ if index_value.dtype != index_prefix.dtype:
+ tensor.cast(index_prefix, dtype=index_value.dtype)
+ index = concat([index_prefix, index_value], axis=-1)
+
+ # value is the category index to search for the corresponding probability.
+ select_prob = gather_nd(prob, index)
+ return nn.reshape(select_prob, value_shape, name=name)
+
+ def log_prob(self, value):
+ """Log probabilities of the given category. Refer to ``probs`` method.
+
+ Args:
+ value (Tensor): The input tensor represents the selected category index.
+
+ Returns:
+ Tensor: Log probability.
+
+ Examples:
+ .. code-block:: python
+
+ import paddle
+ from paddle.distribution import Categorical
+
+ paddle.seed(100) # on CPU device
+ x = paddle.rand([6])
+ print(x)
+ # [0.5535528 0.20714243 0.01162981
+ # 0.51577556 0.36369765 0.2609165 ]
+
+ cat = Categorical(x)
+
+ value = paddle.to_tensor([2,1,3])
+ cat.log_prob(value)
+ # [-5.10271 -2.22287 -1.31061]
+
+ """
+ name = self.name + '_log_prob'
+
+ return nn.log(self.probs(value), name=name)