pytorch 禁止/允許計(jì)算局部梯度的操作

>>> x = torch.tensor([1.], requires_grad=True)
>>> with torch.no_grad():
...   y = x * 2
>>> y.requires_grad
Out[12]:False

>>> @torch.no_grad()
... def doubler(x):
...     return x * 2
>>> z = doubler(x)
>>> z.requires_grad
Out[13]:False

>>> x = torch.tensor([1.], requires_grad=True)
>>> with torch.no_grad():
...   with torch.enable_grad():
...     y = x * 2
>>> y.requires_grad
Out[14]:True
 
>>> y.backward()  # 計(jì)算梯度
>>> x.grad
Out[15]: tensor([2.])

>>> @torch.enable_grad()
... def doubler(x):
...     return x * 2
 
>>> with torch.no_grad():
...     z = doubler(x)
>>> z.requires_grad
 
Out[16]:True

torch.autograd.set_grad_enable()

>>> x = torch.tensor([1.], requires_grad=True)
>>> is_train = False
>>> with torch.set_grad_enabled(is_train):
...   y = x * 2
>>> y.requires_grad
Out[17]:False
 
>>> torch.set_grad_enabled(True)
>>> y = x * 2
>>> y.requires_grad
Out[18]:True
 
>>> torch.set_grad_enabled(False)
>>> y = x * 2
>>> y.requires_grad
Out[19]:False

x = torch.tensor([1.], requires_grad=True)
 
with torch.enable_grad():
    torch.set_grad_enabled(False)
    y = x * 2
    print(y.requires_grad)
Out[20]: False
 
torch.set_grad_enabled(True)
with torch.no_grad():
    z = x * 2
    print(z.requires_grad)
Out[21]:False

Locally disabling gradient computation
在局部區(qū)域內(nèi)關(guān)閉(禁用)梯度的計(jì)算.
The context managers torch.no_grad(), torch.enable_grad(), 
and torch.set_grad_enabled() are helpful for locally disabling 
and enabling gradient computation. See Locally disabling gradient 
computation for more details on their usage. These context 
managers are thread local, so they won't work if you send 
work to another thread using the threading module, etc.
上下文管理器torch.no_grad()、torch.enable_grad()和
torch.set_grad_enabled()可以用來(lái)在局部范圍內(nèi)啟用或禁用梯度計(jì)算.
在Locally disabling gradient computation章節(jié)中詳細(xì)介紹了
局部禁用梯度計(jì)算的使用方式.這些上下文管理器具有線程局部性,
因此,如果你使用threading模塊來(lái)將工作負(fù)載發(fā)送到另一個(gè)線程,
這些上下文管理器將不會(huì)起作用.

no_grad   Context-manager that disabled gradient calculation.
no_grad   用于禁用梯度計(jì)算的上下文管理器.
enable_grad  Context-manager that enables gradient calculation.
enable_grad  用于啟用梯度計(jì)算的上下文管理器.
set_grad_enabled  Context-manager that sets gradient calculation to on or off.
set_grad_enabled  用于設(shè)置梯度計(jì)算打開或關(guān)閉狀態(tài)的上下文管理器.

Microsoft Windows [版本 10.0.18363.1440]
(c) 2019 Microsoft Corporation。保留所有權(quán)利。
C:Userschenxuqi>conda activate pytorch_1.7.1_cu102
(pytorch_1.7.1_cu102) C:Userschenxuqi>python
Python 3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.manual_seed(seed=20200910)
<torch._C.Generator object at 0x000001A2E55A8870>
>>> a = torch.randn(3,4,requires_grad=True)
>>> a
tensor([[ 0.2824, -0.3715,  0.9088, -1.7601],
        [-0.1806,  2.0937,  1.0406, -1.7651],
        [ 1.1216,  0.8440,  0.1783,  0.6859]], requires_grad=True)
>>> b = a * 2
>>> b
tensor([[ 0.5648, -0.7430,  1.8176, -3.5202],
        [-0.3612,  4.1874,  2.0812, -3.5303],
        [ 2.2433,  1.6879,  0.3567,  1.3718]], grad_fn=<MulBackward0>)
>>> b.requires_grad
True
>>> b.grad
__main__:1: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the gradient for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations.
>>> print(b.grad)
None
>>> a.requires_grad
True
>>> a.grad
>>> print(a.grad)
None
>>>
>>> with torch.no_grad():
...     c = a * 2
...
>>> c
tensor([[ 0.5648, -0.7430,  1.8176, -3.5202],
        [-0.3612,  4.1874,  2.0812, -3.5303],
        [ 2.2433,  1.6879,  0.3567,  1.3718]])
>>> c.requires_grad
False
>>> print(c.grad)
None
>>> a.grad
>>>
>>> print(a.grad)
None
>>> c.sum()
tensor(6.1559)
>>>
>>> c.sum().backward()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "D:Anaconda3envspytorch_1.7.1_cu102libsite-packages	orch	ensor.py", line 221, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "D:Anaconda3envspytorch_1.7.1_cu102libsite-packages	orchautograd\__init__.py", line 132, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
>>>
>>>
>>> b.sum()
tensor(6.1559, grad_fn=<SumBackward0>)
>>> b.sum().backward()
>>>
>>>
>>> a.grad
tensor([[2., 2., 2., 2.],
        [2., 2., 2., 2.],
        [2., 2., 2., 2.]])
>>> a.requires_grad
True
>>>
>>>

Microsoft Windows [版本 10.0.18363.1440]
(c) 2019 Microsoft Corporation。保留所有權(quán)利。
C:Userschenxuqi>conda activate pytorch_1.7.1_cu102
(pytorch_1.7.1_cu102) C:Userschenxuqi>python
Python 3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.manual_seed(seed=20200910)
<torch._C.Generator object at 0x000002109ABC8870>
>>>
>>> a = torch.randn(3,4,requires_grad=True)
>>> a
tensor([[ 0.2824, -0.3715,  0.9088, -1.7601],
        [-0.1806,  2.0937,  1.0406, -1.7651],
        [ 1.1216,  0.8440,  0.1783,  0.6859]], requires_grad=True)
>>> a.requires_grad
True
>>>
>>> with torch.set_grad_enabled(False):
...     b = a * 2
...
>>> b
tensor([[ 0.5648, -0.7430,  1.8176, -3.5202],
        [-0.3612,  4.1874,  2.0812, -3.5303],
        [ 2.2433,  1.6879,  0.3567,  1.3718]])
>>> b.requires_grad
False
>>>
>>> with torch.set_grad_enabled(True):
...     c = a * 3
...
>>> c
tensor([[ 0.8472, -1.1145,  2.7263, -5.2804],
        [-0.5418,  6.2810,  3.1219, -5.2954],
        [ 3.3649,  2.5319,  0.5350,  2.0576]], grad_fn=<MulBackward0>)
>>> c.requires_grad
True
>>>
>>> d = a * 4
>>> d.requires_grad
True
>>>
>>> torch.set_grad_enabled(True)  # this can also be used as a function
<torch.autograd.grad_mode.set_grad_enabled object at 0x00000210983982C8>
>>>
>>> # 以函數(shù)調(diào)用的方式來(lái)使用
>>>
>>> e = a * 5
>>> e
tensor([[ 1.4119, -1.8574,  4.5439, -8.8006],
        [-0.9030, 10.4684,  5.2031, -8.8257],
        [ 5.6082,  4.2198,  0.8917,  3.4294]], grad_fn=<MulBackward0>)
>>> e.requires_grad
True
>>>
>>> d
tensor([[ 1.1296, -1.4859,  3.6351, -7.0405],
        [-0.7224,  8.3747,  4.1625, -7.0606],
        [ 4.4866,  3.3759,  0.7133,  2.7435]], grad_fn=<MulBackward0>)
>>>
>>> torch.set_grad_enabled(False) # 以函數(shù)調(diào)用的方式來(lái)使用
<torch.autograd.grad_mode.set_grad_enabled object at 0x0000021098394C48>
>>>
>>> f = a * 6
>>> f
tensor([[  1.6943,  -2.2289,   5.4527, -10.5607],
        [ -1.0836,  12.5621,   6.2437, -10.5908],
        [  6.7298,   5.0638,   1.0700,   4.1153]])
>>> f.requires_grad
False
>>>
>>>
>>>