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[xdoctest] reformat example code with google style in No. 201 #56472

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[xdoctest] reformat example code with google style in No. 201 #56472

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Update python/paddle/tensor/einsum.py
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Merge branch 'PaddlePaddle:develop' into xdocnew_1
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130 changes: 63 additions & 67 deletions python/paddle/tensor/einsum.py
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Original file line number Diff line number Diff line change
Expand Up @@ -953,73 +953,69 @@ def einsum(equation, *operands):
Examples:
.. code-block:: python

import paddle
paddle.seed(102)
x = paddle.rand([4])
y = paddle.rand([5])

# sum
print(paddle.einsum('i->', x))
# Tensor(shape=[], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# 1.95791852)

# dot
print(paddle.einsum('i,i->', x, x))
# Tensor(shape=[], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# 1.45936954)

# outer
print(paddle.einsum("i,j->ij", x, y))
# Tensor(shape=[4, 5], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[0.00079869, 0.00120950, 0.00136844, 0.00187187, 0.00192194],
# [0.23455200, 0.35519385, 0.40186870, 0.54970956, 0.56441545],
# [0.11773264, 0.17828843, 0.20171674, 0.27592498, 0.28330654],
# [0.32897076, 0.49817693, 0.56364071, 0.77099484, 0.79162055]])

A = paddle.rand([2, 3, 2])
B = paddle.rand([2, 2, 3])

# transpose
print(paddle.einsum('ijk->kji', A))
# Tensor(shape=[2, 3, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[[0.95649719, 0.49684682],
# [0.80071914, 0.46258664],
# [0.49814570, 0.33383518]],
#
# [[0.07637714, 0.29374704],
# [0.51470858, 0.51907635],
# [0.99066722, 0.55802226]]])

# batch matrix multiplication
print(paddle.einsum('ijk, ikl->ijl', A,B))
# Tensor(shape=[2, 3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[[0.32172769, 0.50617385, 0.41394392],
# [0.51736701, 0.49921003, 0.38730967],
# [0.69078457, 0.42282537, 0.30161136]],
#
# [[0.32043904, 0.18164253, 0.27810261],
# [0.50226176, 0.24512935, 0.39881429],
# [0.51476848, 0.23367381, 0.39229113]]])

# Ellipsis transpose
print(paddle.einsum('...jk->...kj', A))
# Tensor(shape=[2, 2, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[[0.95649719, 0.80071914, 0.49814570],
# [0.07637714, 0.51470858, 0.99066722]],
#
# [[0.49684682, 0.46258664, 0.33383518],
# [0.29374704, 0.51907635, 0.55802226]]])

# Ellipsis batch matrix multiplication
print(paddle.einsum('...jk, ...kl->...jl', A,B))
# Tensor(shape=[2, 3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[[0.32172769, 0.50617385, 0.41394392],
# [0.51736701, 0.49921003, 0.38730967],
# [0.69078457, 0.42282537, 0.30161136]],
#
# [[0.32043904, 0.18164253, 0.27810261],
# [0.50226176, 0.24512935, 0.39881429],
# [0.51476848, 0.23367381, 0.39229113]]])
>>> import paddle
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>>> paddle.seed(102)
>>> x = paddle.rand([4])
>>> y = paddle.rand([5])

>>> # sum
>>> print(paddle.einsum('i->', x))
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.81225157)

>>> # dot
>>> print(paddle.einsum('i,i->', x, x))
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.13530672)

>>> # outer
>>> print(paddle.einsum("i,j->ij", x, y))
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.26443148, 0.05962684, 0.25360870, 0.21900642, 0.56994802],
[0.20955276, 0.04725220, 0.20097610, 0.17355499, 0.45166403],
[0.35836059, 0.08080698, 0.34369346, 0.29680005, 0.77240014],
[0.00484230, 0.00109189, 0.00464411, 0.00401047, 0.01043695]])

>>> A = paddle.rand([2, 3, 2])
>>> B = paddle.rand([2, 2, 3])

>>> # transpose
>>> print(paddle.einsum('ijk->kji', A))
Tensor(shape=[2, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[0.50882483, 0.56067896],
[0.84598064, 0.36310029],
[0.55289471, 0.33273944]],
[[0.04836850, 0.73811269],
[0.29769155, 0.28137168],
[0.84636718, 0.67521429]]])

>>> # batch matrix multiplication
>>> print(paddle.einsum('ijk, ikl->ijl', A,B))
Tensor(shape=[2, 3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[0.36321065, 0.42009076, 0.40849245],
[0.74353045, 0.79189068, 0.81345987],
[0.90488225, 0.79786193, 0.93451476]],
[[0.12680580, 1.06945944, 0.79821426],
[0.07774551, 0.55068684, 0.44512171],
[0.08053084, 0.80583858, 0.56031936]]])

>>> # Ellipsis transpose
>>> print(paddle.einsum('...jk->...kj', A))
Tensor(shape=[2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[0.50882483, 0.84598064, 0.55289471],
[0.04836850, 0.29769155, 0.84636718]],
[[0.56067896, 0.36310029, 0.33273944],
[0.73811269, 0.28137168, 0.67521429]]])

>>> # Ellipsis batch matrix multiplication
>>> print(paddle.einsum('...jk, ...kl->...jl', A,B))
Tensor(shape=[2, 3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[0.36321065, 0.42009076, 0.40849245],
[0.74353045, 0.79189068, 0.81345987],
[0.90488225, 0.79786193, 0.93451476]],
[[0.12680580, 1.06945944, 0.79821426],
[0.07774551, 0.55068684, 0.44512171],
[0.08053084, 0.80583858, 0.56031936]]])

"""
import os
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