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[Add] Paddle 代码 CI 中引入 xdoctest 检查 #55295

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merged 18 commits into from
Jul 18, 2023
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[Add] Paddle 代码 CI 中引入 xdoctest 检查 #55295

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688a0ef
[Add]Add Xdoctester
megemini Jul 10, 2023
9a8ebdf
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
megemini Jul 10, 2023
7a943f1
[Fix]fix beta docstring
megemini Jul 11, 2023
a25b27a
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
megemini Jul 11, 2023
eaa65d4
[Doctest]change dirichlet docstring
megemini Jul 12, 2023
f6fd132
[Doctest]change gumbel docstring
megemini Jul 13, 2023
f394a4d
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
megemini Jul 13, 2023
0629f84
[Doctest]change bernoulli docstring
megemini Jul 14, 2023
961b9a3
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
megemini Jul 14, 2023
9507ece
[Doctest]change categorical docstring
megemini Jul 15, 2023
e5242af
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
megemini Jul 15, 2023
a22ea53
[Doctest]change ops.py docstring
megemini Jul 16, 2023
2d2c6d3
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
megemini Jul 16, 2023
99c1c32
[Doctest]change conv docstring
megemini Jul 16, 2023
bbd3e01
[Doctest]change distance docstring, test=docs_preview
megemini Jul 16, 2023
139d982
[Change]add ref
megemini Jul 16, 2023
14e77a2
[Change]patch xdoctest debug
megemini Jul 17, 2023
0eb1468
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
megemini Jul 17, 2023
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200 changes: 100 additions & 100 deletions python/paddle/distribution/bernoulli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,23 +72,23 @@ class Bernoulli(exponential_family.ExponentialFamily):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli
>>> import paddle
>>> from paddle.distribution import Bernoulli

# init `probs` with a float
rv = Bernoulli(probs=0.3)
>>> # init `probs` with a float
>>> rv = Bernoulli(probs=0.3)

print(rv.mean)
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 0.30000001)
>>> print(rv.mean)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.30000001)

print(rv.variance)
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 0.21000001)
>>> print(rv.variance)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.21000001)

print(rv.entropy())
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 0.61086434)
>>> print(rv.entropy())
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.61086434)
"""

def __init__(self, probs, name=None):
Expand Down Expand Up @@ -156,24 +156,24 @@ def sample(self, shape):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli
>>> import paddle
>>> from paddle.distribution import Bernoulli

rv = Bernoulli(paddle.full((), 0.3))
print(rv.sample([100]).shape)
# [100]
>>> rv = Bernoulli(paddle.full((1), 0.3))
>>> print(rv.sample([100]).shape)
[100, 1]

rv = Bernoulli(paddle.to_tensor(0.3))
print(rv.sample([100]).shape)
# [100, 1]
>>> rv = Bernoulli(paddle.to_tensor(0.3))
>>> print(rv.sample([100]).shape)
[100]

rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
print(rv.sample([100]).shape)
# [100, 2]
>>> rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
>>> print(rv.sample([100]).shape)
[100, 2]

rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
print(rv.sample([100, 2]).shape)
# [100, 2, 2]
>>> rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
>>> print(rv.sample([100, 2]).shape)
[100, 2, 2]
"""
name = self.name + '_sample'
if not in_dynamic_mode():
Expand Down Expand Up @@ -211,48 +211,48 @@ def rsample(self, shape, temperature=1.0):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli

paddle.seed(2023)

rv = Bernoulli(paddle.full((), 0.3))
print(rv.sample([100]).shape)
# [100]

rv = Bernoulli(0.3)
print(rv.rsample([100]).shape)
# [100, 1]

rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
print(rv.rsample([100]).shape)
# [100, 2]

rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
print(rv.rsample([100, 2]).shape)
# [100, 2, 2]

# `rsample` has to be followed by a `sigmoid`
rv = Bernoulli(0.3)
rsample = rv.rsample([3, ])
rsample_sigmoid = paddle.nn.functional.sigmoid(rsample)
print(rsample, rsample_sigmoid)
# Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[-0.88315082],
# [-0.62347704],
# [-0.31513220]]) Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[0.29252526],
# [0.34899110],
# [0.42186251]])

# The smaller the `temperature`, the distribution of `rsample` closer to `sample`, with `probs` of 0.3.
print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=1.0)).sum())
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 361.06829834)

print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=0.1)).sum())
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 288.66418457)
>>> import paddle
>>> from paddle.distribution import Bernoulli

>>> rv = Bernoulli(paddle.full((1), 0.3))
>>> print(rv.sample([100]).shape)
[100, 1]

>>> rv = Bernoulli(0.3)
>>> print(rv.rsample([100]).shape)
[100]

>>> rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
>>> print(rv.rsample([100]).shape)
[100, 2]

>>> rv = Bernoulli(paddle.to_tensor([0.3, 0.5]))
>>> print(rv.rsample([100, 2]).shape)
[100, 2, 2]

>>> # `rsample` has to be followed by a `sigmoid`
>>> # doctest: +SKIP
>>> rv = Bernoulli(0.3)
>>> rsample = rv.rsample([3, ])
>>> rsample_sigmoid = paddle.nn.functional.sigmoid(rsample)
>>> print(rsample, rsample_sigmoid)
Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.88315082],
[-0.62347704],
[-0.31513220]])
Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.29252526],
[0.34899110],
[0.42186251]])

>>> # The smaller the `temperature`, the distribution of `rsample` closer to `sample`, with `probs` of 0.3.
>>> print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=1.0)).sum())
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
361.06829834)

>>> print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=0.1)).sum())
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
288.66418457)
"""
name = self.name + '_rsample'
if not in_dynamic_mode():
Expand Down Expand Up @@ -308,13 +308,13 @@ def cdf(self, value):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli
>>> import paddle
>>> from paddle.distribution import Bernoulli

rv = Bernoulli(0.3)
print(rv.cdf(paddle.to_tensor([1.0])))
# Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
# [1.])
>>> rv = Bernoulli(0.3)
>>> print(rv.cdf(paddle.to_tensor([1.0])))
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[1.])
"""
name = self.name + '_cdf'
if not in_dynamic_mode():
Expand Down Expand Up @@ -346,13 +346,13 @@ def log_prob(self, value):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli
>>> import paddle
>>> from paddle.distribution import Bernoulli

rv = Bernoulli(0.3)
print(rv.log_prob(paddle.to_tensor([1.0])))
# Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
# [-1.20397282])
>>> rv = Bernoulli(0.3)
>>> print(rv.log_prob(paddle.to_tensor([1.0])))
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[-1.20397282])
"""
name = self.name + '_log_prob'
if not in_dynamic_mode():
Expand Down Expand Up @@ -385,13 +385,13 @@ def prob(self, value):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli
>>> import paddle
>>> from paddle.distribution import Bernoulli

rv = Bernoulli(0.3)
print(rv.prob(paddle.to_tensor([1.0])))
# Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
# [0.29999998])
>>> rv = Bernoulli(0.3)
>>> print(rv.prob(paddle.to_tensor([1.0])))
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.29999998])
"""
name = self.name + '_prob'
if not in_dynamic_mode():
Expand All @@ -415,13 +415,13 @@ def entropy(self):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli
>>> import paddle
>>> from paddle.distribution import Bernoulli

rv = Bernoulli(0.3)
print(rv.entropy())
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 0.61086434)
>>> rv = Bernoulli(0.3)
>>> print(rv.entropy())
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.61086434)
"""
name = self.name + '_entropy'

Expand All @@ -448,15 +448,15 @@ def kl_divergence(self, other):

.. code-block:: python

import paddle
from paddle.distribution import Bernoulli
>>> import paddle
>>> from paddle.distribution import Bernoulli

rv = Bernoulli(0.3)
rv_other = Bernoulli(0.7)
>>> rv = Bernoulli(0.3)
>>> rv_other = Bernoulli(0.7)

print(rv.kl_divergence(rv_other))
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 0.33891910)
>>> print(rv.kl_divergence(rv_other))
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.33891910)
"""
name = self.name + '_kl_divergence'
if not in_dynamic_mode():
Expand Down
54 changes: 29 additions & 25 deletions python/paddle/distribution/beta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,31 +55,35 @@ class Beta(exponential_family.ExponentialFamily):

.. code-block:: python

import paddle

# scale input
beta = paddle.distribution.Beta(alpha=0.5, beta=0.5)
print(beta.mean)
# Tensor(shape=[], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# 0.50000000)
print(beta.variance)
# Tensor(shape=[], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# 0.12500000)
print(beta.entropy())
# Tensor(shape=[], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# 0.12500000)

# tensor input with broadcast
beta = paddle.distribution.Beta(alpha=paddle.to_tensor([0.2, 0.4]), beta=0.6)
print(beta.mean)
# Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [0.25000000, 0.40000001])
print(beta.variance)
# Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [0.10416666, 0.12000000])
print(beta.entropy())
# Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [-1.91923141, -0.38095069])
>>> import paddle

>>> # scale input
>>> beta = paddle.distribution.Beta(alpha=0.5, beta=0.5)
>>> print(beta.mean)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.50000000)

>>> print(beta.variance)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.12500000)

>>> print(beta.entropy())
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
-0.24156499)

>>> # tensor input with broadcast
>>> beta = paddle.distribution.Beta(alpha=paddle.to_tensor([0.2, 0.4]), beta=0.6)
>>> print(beta.mean)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.25000000, 0.40000001])

>>> print(beta.variance)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.10416666, 0.12000000])

>>> print(beta.entropy())
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[-1.91923141, -0.38095081])
"""

def __init__(self, alpha, beta):
Expand Down
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