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未验证 提交 2e3a1904 编写于 作者: P PommesPeter 提交者： GitHub
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[xdoctest] reformat example code with google style No.241-245 (#56359) 

* fix: refine code examples

* fix: refine detail

* refactor: refine detail

* refactor: refine detail

* fix: refine code style.

* Update python/paddle/incubate/autograd/functional.py

---------
Co-authored-by: NNyakku Shigure <sigure.qaq@gmail.com>
上级 f2ebbce7
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Showing with 247 addition and 200 deletion
python/paddle/incubate/autograd/functional.py
浏览文件 @ 2e3a1904
......@@ -46,24 +46,24 @@ def vjp(func, xs, v=None):
.. code-block:: python
import paddle
def func(x):
return paddle.matmul(x, x)
x = paddle.ones(shape=[2, 2], dtype='float32')
_, vjp_result = paddle.incubate.autograd.vjp(func, x)
print(vjp_result)
# Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[4., 4.],
# [4., 4.]])
v = paddle.to_tensor([[1.0, 0.0], [0.0, 0.0]])
_, vjp_result = paddle.incubate.autograd.vjp(func, x, v)
print(vjp_result)
# Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[2., 1.],
# [1., 0.]])
>>> import paddle
>>> def func(x):
... return paddle.matmul(x, x)
...
>>> x = paddle.ones(shape=[2, 2], dtype='float32')
>>> _, vjp_result = paddle.incubate.autograd.vjp(func, x)
>>> print(vjp_result)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[4., 4.],
[4., 4.]])
>>> v = paddle.to_tensor([[1.0, 0.0], [0.0, 0.0]])
>>> _, vjp_result = paddle.incubate.autograd.vjp(func, x, v)
>>> print(vjp_result)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[2., 1.],
[1., 0.]])
"""
_check_inputs(func, xs, v)
......@@ -106,25 +106,24 @@ def jvp(func, xs, v=None):
.. code-block:: python
import paddle
def func(x):
return paddle.matmul(x, x)
x = paddle.ones(shape=[2, 2], dtype='float32')
_, jvp_result = paddle.incubate.autograd.jvp(func, x)
print(jvp_result)
# Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[4., 4.],
# [4., 4.]])
v = paddle.to_tensor([[1.0, 0.0], [0.0, 0.0]])
_, jvp_result = paddle.incubate.autograd.jvp(func, x, v)
print(jvp_result)
# Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[2., 1.],
# [1., 0.]])
>>> import paddle
>>> def func(x):
... return paddle.matmul(x, x)
...
>>> x = paddle.ones(shape=[2, 2], dtype='float32')
>>> _, jvp_result = paddle.incubate.autograd.jvp(func, x)
>>> print(jvp_result)
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
[[4., 4.],
[4., 4.]])
>>> v = paddle.to_tensor([[1.0, 0.0], [0.0, 0.0]])
>>> _, jvp_result = paddle.incubate.autograd.jvp(func, x, v)
>>> print(jvp_result)
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
[[2., 1.],
[1., 0.]])
"""
_check_inputs(func, xs, v)
......@@ -217,28 +216,26 @@ class Jacobian:
.. code-block:: python
import paddle
def func(x, y):
return paddle.matmul(x, y)
x = paddle.to_tensor([[1., 2.], [3., 4.]])
J = paddle.incubate.autograd.Jacobian(func, [x, x])
print(J[:, :])
# Tensor(shape=[4, 8], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[1., 3., 0., 0., 1., 0., 2., 0.],
# [2., 4., 0., 0., 0., 1., 0., 2.],
# [0., 0., 1., 3., 3., 0., 4., 0.],
# [0., 0., 2., 4., 0., 3., 0., 4.]])
print(J[0, :])
# Tensor(shape=[8], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [1., 3., 0., 0., 1., 0., 2., 0.])
print(J[:, 0])
# Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [1., 2., 0., 0.])
>>> import paddle
>>> def func(x, y):
... return paddle.matmul(x, y)
...
>>> x = paddle.to_tensor([[1., 2.], [3., 4.]])
>>> J = paddle.incubate.autograd.Jacobian(func, [x, x])
>>> print(J[:, :])
Tensor(shape=[4, 8], dtype=float32, place=Place(cpu), stop_gradient=False,
[[1., 3., 0., 0., 1., 0., 2., 0.],
[2., 4., 0., 0., 0., 1., 0., 2.],
[0., 0., 1., 3., 3., 0., 4., 0.],
[0., 0., 2., 4., 0., 3., 0., 4.]])
>>> print(J[0, :])
Tensor(shape=[8], dtype=float32, place=Place(cpu), stop_gradient=False,
[1., 3., 0., 0., 1., 0., 2., 0.])
>>> print(J[:, 0])
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=False,
[1., 2., 0., 0.])
"""
......@@ -287,23 +284,22 @@ class Hessian:
Examples:
.. code-block:: python
import paddle
.. code-block:: python
def reducer(x):
return paddle.sum(x * x)
>>> import paddle
>>> def reducer(x):
... return paddle.sum(x * x)
...
>>> x = paddle.rand([2, 2])
>>> h = paddle.incubate.autograd.Hessian(reducer, x)
>>> print(h[:])
Tensor(shape=[4, 4], dtype=float32, place=CPUPlace(), stop_gradient=False,
[[2., 0., 0., 0.],
[0., 2., 0., 0.],
[0., 0., 2., 0.],
[0., 0., 0., 2.]])
x = paddle.rand([2, 2])
h = paddle.incubate.autograd.Hessian(reducer, x)
print(h[:])
# Tensor(shape=[4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[2., 0., 0., 0.],
# [0., 2., 0., 0.],
# [0., 0., 2., 0.],
# [0., 0., 0., 2.]])
"""
def __init__(self, func, xs, is_batched=False):
......@@ -619,27 +615,25 @@ def _separate(xs):
.. code-block:: python
import paddle
from paddle.autograd.functional import _separate
def func(x, y):
return x * y
x = paddle.ones((1,))
x.stop_gradient = False
y = func(x, x)
print(paddle.grad(y, x))
# [Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [2.])]
x1, x2 = _separate((x, x))
y = func(x1, x2)
print(paddle.grad(y, x1))
# [Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [1.])]
>>> import paddle
>>> from paddle.incubate.autograd.functional import _separate
>>> def func(x, y):
... return x * y
...
>>> x = paddle.ones((1,))
>>> x.stop_gradient = False
>>> y = func(x, x)
>>> print(paddle.grad(y, x))
[Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[2.])]
>>> x1, x2 = _separate((x, x))
>>> y = func(x1, x2)
>>> print(paddle.grad(y, x1))
[Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[1.])]
"""
if isinstance(xs, typing.Sequence):
......
python/paddle/incubate/autograd/primapi.py
浏览文件 @ 2e3a1904
......@@ -42,29 +42,29 @@ def forward_grad(outputs, inputs, grad_inputs=None):
.. code-block:: python
import numpy as np
import paddle
paddle.enable_static()
paddle.incubate.autograd.enable_prim()
startup_program = paddle.static.Program()
main_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
x = paddle.static.data('x', shape=[1], dtype='float32')
y = x * x
y_grad = paddle.incubate.autograd.forward_grad(y, x)
paddle.incubate.autograd.prim2orig()
exe = paddle.static.Executor()
exe.run(startup_program)
y_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[y_grad])
print(y_grad)
# [array([4.], dtype=float32)]
paddle.incubate.autograd.disable_prim()
paddle.disable_static()
>>> import numpy as np
>>> import paddle
>>> paddle.enable_static()
>>> paddle.incubate.autograd.enable_prim()
>>> startup_program = paddle.static.Program()
>>> main_program = paddle.static.Program()
>>> with paddle.static.program_guard(main_program, startup_program):
... x = paddle.static.data('x', shape=[1], dtype='float32')
... y = x * x
... y_grad = paddle.incubate.autograd.forward_grad(y, x)
... paddle.incubate.autograd.prim2orig()
...
>>> exe = paddle.static.Executor()
>>> exe.run(startup_program)
>>> y_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[y_grad])
>>> print(y_grad)
[array([4.], dtype=float32)]
>>> paddle.incubate.autograd.disable_prim()
>>> paddle.disable_static()
"""
if not utils.prim_enabled():
raise RuntimeError(
......@@ -125,29 +125,29 @@ def grad(outputs, inputs, grad_outputs=None):
.. code-block:: python
import numpy as np
import paddle
paddle.enable_static()
paddle.incubate.autograd.enable_prim()
startup_program = paddle.static.Program()
main_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
x = paddle.static.data('x', shape=[1], dtype='float32')
x.stop_gradients = False
y = x * x
x_grad = paddle.incubate.autograd.grad(y, x)
paddle.incubate.autograd.prim2orig()
exe = paddle.static.Executor()
exe.run(startup_program)
x_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[x_grad])
print(x_grad)
# [array([4.], dtype=float32)]
paddle.incubate.autograd.disable_prim()
paddle.disable_static()
>>> import numpy as np
>>> import paddle
>>> paddle.enable_static()
>>> paddle.incubate.autograd.enable_prim()
>>> startup_program = paddle.static.Program()
>>> main_program = paddle.static.Program()
>>> with paddle.static.program_guard(main_program, startup_program):
... x = paddle.static.data('x', shape=[1], dtype='float32')
... x.stop_gradients = False
... y = x * x
... x_grad = paddle.incubate.autograd.grad(y, x)
... paddle.incubate.autograd.prim2orig()
...
>>> exe = paddle.static.Executor()
>>> exe.run(startup_program)
>>> x_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[x_grad])
>>> print(x_grad)
[array([4.], dtype=float32)]
>>> paddle.incubate.autograd.disable_prim()
>>> paddle.disable_static()
"""
if not utils.prim_enabled():
grad_inputs = backward.gradients(outputs, inputs, grad_outputs)
......
python/paddle/incubate/autograd/primreg.py
浏览文件 @ 2e3a1904
......@@ -77,9 +77,15 @@ def op_position_inputs(op):
Examples:
.. code-block:: python
@REGISTER_FN('div_p', 'X', 'Y', 'Z')
def div(x, y, out=None):
return _simple_binop(LayerHelper('div_p', **locals()))
>>> from paddle.incubate.autograd.primops import _simple_binop
>>> from paddle.fluid.layer_helper import LayerHelper
>>> from paddle.incubate.autograd.primreg import REGISTER_FN
>>> # doctest: +SKIP('Depends on external code.')
>>> @REGISTER_FN('div_p', 'X', 'Y', 'Z')
>>> def div(x, y, out=None):
... return _simple_binop(LayerHelper('div_p', **locals()))
The registered inputs are ['X', 'Y'] for div_p and accordingly this
function will return inputs in the order of X then Y.
......@@ -117,9 +123,15 @@ def op_position_output(op):
Examples:
.. code-block:: python
@REGISTER_FN('div_p', 'X', 'Y', 'Z')
def div(x, y, out=None):
return _simple_binop(LayerHelper('div_p', **locals()))
>>> # doctest: +SKIP('Depends on external code.')
>>> from paddle.incubate.autograd.primops import _simple_binop
>>> from paddle.fluid.layer_helper import LayerHelper
>>> from paddle.incubate.autograd.primreg import REGISTER_FN
>>> @REGISTER_FN('div_p', 'X', 'Y', 'Z')
>>> def div(x, y, out=None):
... return _simple_binop(LayerHelper('div_p', **locals()))
The registered output is ['Z'] for div_p and accordingly this
function will return output Z.
......@@ -154,9 +166,15 @@ def REGISTER_FN(op_type, *position_argnames):
Examples:
.. code-block:: python
@REGISTER_FN('tanh_p', 'X', 'Y')
def tanh(x, out=None):
return _simple_unop(LayerHelper('tanh_p', **locals()))
>>> # doctest: +SKIP('Depends on external code.')
>>> from paddle.incubate.autograd.primops import _simple_binop
>>> from paddle.fluid.layer_helper import LayerHelper
>>> from paddle.incubate.autograd.primreg import REGISTER_FN
>>> @REGISTER_FN('tanh_p', 'X', 'Y')
>>> def tanh(x, out=None):
... return _simple_unop(LayerHelper('tanh_p', **locals()))
"""
......@@ -184,10 +202,17 @@ def REGISTER_ORIG2PRIM(op_type):
Examples:
.. code-block:: python
@REGISTER_ORIG2PRIM('tanh')
def tanh_orig2prim(op):
x, = get_input_var_list(op)
return primops.tanh(x)
>>> # doctest: +SKIP('Depends on external code.')
>>> from paddle.fluid.layer_helper import LayerHelper
>>> from paddle.incubate.autograd.utils import get_input_var_list
>>> from paddle.incubate.autograd import primops
>>> from paddle.incubate.autograd.primreg import REGISTER_ORIG2PRIM
>>> @REGISTER_ORIG2PRIM('tanh')
>>> def tanh_orig2prim(op):
... x, = get_input_var_list(op)
... return primops.tanh(x)
"""
if not isinstance(op_type, str):
......@@ -217,12 +242,17 @@ def REGISTER_COMPOSITE(op_type):
Examples:
.. code-block:: python
@REGISTER_COMPOSITE('softmax')
def softmax_composite(x, axis):
molecular = exp(x)
denominator = broadcast_to(sum(molecular, axis=axis, keepdim=True), x.shape)
res = divide(molecular, denominator)
return res
>>> # doctest: +SKIP('Depends on external code.')
>>> import paddle
>>> from paddle.incubate.autograd.primreg import REGISTER_COMPOSITE
>>> @REGISTER_COMPOSITE('softmax')
>>> def softmax_composite(x, axis):
... molecular = paddle.exp(x)
... denominator = paddle.broadcast_to(sum(molecular, axis=axis, keepdim=True), x.shape)
... res = paddle.divide(molecular, denominator)
... return res
"""
if not isinstance(op_type, str):
......@@ -252,11 +282,17 @@ def REGISTER_PRIM2ORIG(op_type):
Examples:
.. code-block:: python
@REGISTER_PRIM2ORIG('tanh_p')
def tanh_prim2orig(op):
x, = get_input_var_list(op)
return paddle.tanh(x)
>>> # doctest: +SKIP('Depends on external code.')
>>> import paddle
>>> from paddle.incubate.autograd.primreg import REGISTER_PRIM2ORIG
>>> from paddle.incubate.autograd.utils import get_input_var_list
>>> @REGISTER_PRIM2ORIG('tanh_p')
>>> def tanh_prim2orig(op):
... x, = get_input_var_list(op)
... return paddle.tanh(x)
...
"""
if not isinstance(op_type, str):
raise TypeError(f'op_type must be str, but got {type(op_type)}.')
......@@ -285,9 +321,14 @@ def REGISTER_JVP(op_type):
Examples:
.. code-block:: python
@REGISTER_JVP('add_p')
def add_jvp(op, x_dot, y_dot):
return primops.add(x_dot, y_dot)
>>> # doctest: +SKIP('Depends on external code.')
>>> from paddle.incubate.autograd import primops
>>> from paddle.incubate.autograd.primreg import REGISTER_JVP
>>> @REGISTER_JVP('add_p')
>>> def add_jvp(op, x_dot, y_dot):
... return primops.add(x_dot, y_dot)
"""
if not isinstance(op_type, str):
......@@ -319,9 +360,13 @@ def REGISTER_TRANSPOSE(op_type):
Examples:
.. code-block:: python
@REGISTER_TRANSPOSE('add_p')
def add_transpose(op, z_bar):
return z_bar, z_bar
>>> # doctest: +SKIP('Depends on external code.')
>>> from paddle.incubate.autograd.primreg import REGISTER_TRANSPOSE
>>> @REGISTER_TRANSPOSE('add_p')
>>> def add_transpose(op, z_bar):
... return z_bar, z_bar
"""
if not isinstance(op_type, str):
......
python/paddle/incubate/autograd/primx.py
浏览文件 @ 2e3a1904
......@@ -810,18 +810,18 @@ def prim2orig(block=None, blacklist=None):
.. code-block:: python
import paddle
from paddle.incubate.autograd import enable_prim, prim_enabled, prim2orig
paddle.enable_static()
enable_prim()
x = paddle.ones(shape=[2, 2], dtype='float32')
x.stop_gradients = False
y = x * x
dy_dx = paddle.static.gradients(y, x)
if prim_enabled():
prim2orig()
>>> import paddle
>>> from paddle.incubate.autograd import enable_prim, prim_enabled, prim2orig
>>> paddle.enable_static()
>>> enable_prim()
>>> x = paddle.ones(shape=[2, 2], dtype='float32')
>>> x.stop_gradients = False
>>> y = x * x
>>> dy_dx = paddle.static.gradients(y, x)
>>> if prim_enabled():
... prim2orig()
"""
block = default_main_program().current_block() if block is None else block
......
python/paddle/incubate/autograd/utils.py
浏览文件 @ 2e3a1904
......@@ -51,17 +51,20 @@ def prim_enabled():
.. code-block:: python
import paddle
from paddle.incubate.autograd import enable_prim, disable_prim, prim_enabled
>>> import paddle
>>> from paddle.incubate.autograd import enable_prim, disable_prim, prim_enabled
paddle.enable_static()
enable_prim()
>>> paddle.enable_static()
>>> enable_prim()
print(prim_enabled()) # True
>>> print(prim_enabled())
True
disable_prim()
>>> disable_prim()
>>> print(prim_enabled())
False
print(prim_enabled()) # False
"""
return prim_option.get_status()
......@@ -79,13 +82,15 @@ def enable_prim():
.. code-block:: python
import paddle
from paddle.incubate.autograd import enable_prim, prim_enabled
>>> import paddle
>>> from paddle.incubate.autograd import enable_prim, prim_enabled
>>> paddle.enable_static()
>>> enable_prim()
paddle.enable_static()
enable_prim()
>>> print(prim_enabled())
True
print(prim_enabled()) # True
"""
prim_option.set_status(True)
......@@ -103,17 +108,20 @@ def disable_prim():
.. code-block:: python
import paddle
from paddle.incubate.autograd import enable_prim, disable_prim, prim_enabled
>>> import paddle
>>> from paddle.incubate.autograd import enable_prim, disable_prim, prim_enabled
>>> paddle.enable_static()
>>> enable_prim()
paddle.enable_static()
enable_prim()
>>> print(prim_enabled())
True
print(prim_enabled()) # True
>>> disable_prim()
disable_prim()
>>> print(prim_enabled())
False
print(prim_enabled()) # False
"""
prim_option.set_status(False)
......
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