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pytorch 禁止/允許計算局部梯度的操作

蘿莉的小草莓 2021-08-19 15:02:31 瀏覽數(shù) (4036)
反饋

在pytorch中，我們可以禁止計算局部梯度，也可以允許計算局部梯度，禁用或者允許得根據(jù)模型的具體情況而定，那么怎么進行這兩種操作呢？接下來的這篇文章帶你了解。

一、禁止計算局部梯度

torch.autogard.no_grad: 禁用梯度計算的上下文管理器。

當(dāng)確定不會調(diào)用Tensor.backward()計算梯度時，設(shè)置禁止計算梯度會減少內(nèi)存消耗。如果需要計算梯度設(shè)置Tensor.requires_grad=True

兩種禁用方法：

將不用計算梯度的變量放在with torch.no_grad()里

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

使用裝飾器 @torch.no_gard()修飾的函數(shù)，在調(diào)用時不允許計算梯度

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

二、禁止后允許計算局部梯度

torch.autogard.enable_grad ：允許計算梯度的上下文管理器

在一個no_grad上下文中使能梯度計算。在no_grad外部此上下文管理器無影響.

用法和上面類似：

使用with torch.enable_grad()允許計算梯度

>>> 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()  # 計算梯度
>>> x.grad
Out[15]: tensor([2.])

在禁止計算梯度下調(diào)用被允許計算梯度的函數(shù)，結(jié)果可以計算梯度

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

三、是否計算梯度

torch.autograd.set_grad_enable()

可以作為一個函數(shù)使用：

>>> 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

總結(jié)：

單獨使用這三個函數(shù)時沒有什么，但是若是嵌套，遵循就近原則。

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

補充：pytorch局部范圍內(nèi)禁用梯度計算，no_grad、enable_grad、set_grad_enabled使用舉例

在這里插入圖片描述 在這里插入圖片描述

原文及翻譯

Locally disabling gradient computation
在局部區(qū)域內(nèi)關(guān)閉(禁用)梯度的計算.
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()可以用來在局部范圍內(nèi)啟用或禁用梯度計算.
在Locally disabling gradient computation章節(jié)中詳細介紹了
局部禁用梯度計算的使用方式.這些上下文管理器具有線程局部性,
因此,如果你使用threading模塊來將工作負載發(fā)送到另一個線程,
這些上下文管理器將不會起作用.

no_grad   Context-manager that disabled gradient calculation.
no_grad   用于禁用梯度計算的上下文管理器.
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)置梯度計算打開或關(guān)閉狀態(tài)的上下文管理器.

例子1

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
>>>
>>>

例子2

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
>>>
>>>
>>>

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