如何在 pytorch 模型中使参数不是叶子并在计算图中?

Question

我正在尝试更新/更改神经网络模型的参数，然后将更新的神经网络的前向传递放在计算图中(无论我们做了多少更改/更新(。

我尝试了这个想法，但每当我这样做时，pytorch 都会将我更新的张量(在模型内(设置为叶子，这会杀死梯度流向我想要接收梯度的网络。它杀死了梯度的流动，因为叶节点不是我想要的计算图的一部分(因为它们不是真正的叶子(。

我已经尝试了多种方法，但似乎没有任何效果。我创建了一个自包含的虚拟代码，用于打印我希望具有渐变的网络的梯度：

import torch
import torch.nn as nn
import copy
from collections import OrderedDict
# img = torch.randn([8,3,32,32])
# targets = torch.LongTensor([1, 2, 0, 6, 2, 9, 4, 9])
# img = torch.randn([1,3,32,32])
# targets = torch.LongTensor([1])
x = torch.randn(1)
target = 12.0*x**2
criterion = nn.CrossEntropyLoss()
#loss_net = nn.Sequential(OrderedDict([('conv0',nn.Conv2d(in_channels=3,out_channels=10,kernel_size=32))]))
loss_net = nn.Sequential(OrderedDict([('fc0', nn.Linear(in_features=1,out_features=1))]))
hidden = torch.randn(size=(1,1),requires_grad=True)
updater_net = nn.Sequential(OrderedDict([('fc0',nn.Linear(in_features=1,out_features=1))]))
print(f'updater_net.fc0.weight.is_leaf = {updater_net.fc0.weight.is_leaf}')
#
nb_updates = 2
for i in range(nb_updates):
print(f'i = {i}')
new_params = copy.deepcopy( loss_net.state_dict() )
## w^<t> := f(w^<t-1>,delta^<t-1>)
for (name, w) in loss_net.named_parameters():
print(f'name = {name}')
print(w.size())
hidden = updater_net(hidden).view(1)
print(hidden.size())
#delta = ((hidden**2)*w/2)
delta = w + hidden
wt = w + delta
print(wt.size())
new_params[name] = wt
#del loss_net.fc0.weight
#setattr(loss_net.fc0, 'weight', nn.Parameter( wt ))
#setattr(loss_net.fc0, 'weight', wt)
#loss_net.fc0.weight = wt
#loss_net.fc0.weight = nn.Parameter( wt )
##
loss_net.load_state_dict(new_params)
#
print()
print(f'updater_net.fc0.weight.is_leaf = {updater_net.fc0.weight.is_leaf}')
outputs = loss_net(x)
loss_val = 0.5*(target - outputs)**2
loss_val.backward()
print()
print(f'-- params that dont matter if they have gradients --')
print(f'loss_net.grad = {loss_net.fc0.weight.grad}')
print('-- params we want to have gradients --')
print(f'hidden.grad = {hidden.grad}')
print(f'updater_net.fc0.weight.grad = {updater_net.fc0.weight.grad}')
print(f'updater_net.fc0.bias.grad = {updater_net.fc0.bias.grad}')

如果有人知道如何做到这一点，请给我一个ping...我将更新的次数设置为 2，因为更新操作应该在计算图中任意次数......所以它必须适用于 2。

---

相关帖子：

• SO：如何在pytorch模型中使参数不是叶子，而是在计算图中？
• PyTorch 论坛：https://discuss.pytorch.org/t/how-does-one-have-the-parameters-of-a-model-not-be-leafs/70076

交叉发布：

• 夸拉： https://www.quora.com/unanswered/How-does-one-have-parameters-in-a-PyTorch-model-not-be-leaves-and-be-in-the-computation-graph
• 红迪网：https://www.reddit.com/r/pytorch/comments/f5gu3g/how_does_one_have_parameters_in_a_pytorch_model/

Answer 1

无法正常工作，因为命名参数模块被删除。

---

似乎这有效：

import torch
import torch.nn as nn
from torchviz import make_dot
import copy
from collections import OrderedDict
# img = torch.randn([8,3,32,32])
# targets = torch.LongTensor([1, 2, 0, 6, 2, 9, 4, 9])
# img = torch.randn([1,3,32,32])
# targets = torch.LongTensor([1])
x = torch.randn(1)
target = 12.0*x**2
criterion = nn.CrossEntropyLoss()
#loss_net = nn.Sequential(OrderedDict([('conv0',nn.Conv2d(in_channels=3,out_channels=10,kernel_size=32))]))
loss_net = nn.Sequential(OrderedDict([('fc0', nn.Linear(in_features=1,out_features=1))]))
hidden = torch.randn(size=(1,1),requires_grad=True)
updater_net = nn.Sequential(OrderedDict([('fc0',nn.Linear(in_features=1,out_features=1))]))
print(f'updater_net.fc0.weight.is_leaf = {updater_net.fc0.weight.is_leaf}')
#
def del_attr(obj, names):
if len(names) == 1:
delattr(obj, names[0])
else:
del_attr(getattr(obj, names[0]), names[1:])
def set_attr(obj, names, val):
if len(names) == 1:
setattr(obj, names[0], val)
else:
set_attr(getattr(obj, names[0]), names[1:], val)
nb_updates = 2
for i in range(nb_updates):
print(f'i = {i}')
new_params = copy.deepcopy( loss_net.state_dict() )
## w^<t> := f(w^<t-1>,delta^<t-1>)
for (name, w) in list(loss_net.named_parameters()):
hidden = updater_net(hidden).view(1)
#delta = ((hidden**2)*w/2)
delta = w + hidden
wt = w + delta
del_attr(loss_net, name.split("."))
set_attr(loss_net, name.split("."), wt)
##
#
print()
print(f'updater_net.fc0.weight.is_leaf = {updater_net.fc0.weight.is_leaf}')
print(f'loss_net.fc0.weight.is_leaf = {loss_net.fc0.weight.is_leaf}')
outputs = loss_net(x)
loss_val = 0.5*(target - outputs)**2
loss_val.backward()
print()
print(f'-- params that dont matter if they have gradients --')
print(f'loss_net.grad = {loss_net.fc0.weight.grad}')
print('-- params we want to have gradients --')
print(f'hidden.grad = {hidden.grad}') # None because this is not a leaf, it is overriden in the for loop above.
print(f'updater_net.fc0.weight.grad = {updater_net.fc0.weight.grad}')
print(f'updater_net.fc0.bias.grad = {updater_net.fc0.bias.grad}')
make_dot(loss_val)

输出：

updater_net.fc0.weight.is_leaf = True
i = 0
i = 1
updater_net.fc0.weight.is_leaf = True
loss_net.fc0.weight.is_leaf = False
-- params that dont matter if they have gradients --
loss_net.grad = None
-- params we want to have gradients --
hidden.grad = None
updater_net.fc0.weight.grad = tensor([[0.7152]])
updater_net.fc0.bias.grad = tensor([-7.4249])

致谢：来自 pytorch 团队的强大阿尔班D：https://discuss.pytorch.org/t/how-does-one-have-the-parameters-of-a-model-not-be-leafs/70076/9?u=pinocchio

Answer 2

你应该尝试保持相同的张量，而不是创建新的张量。

选择他们的data属性并设置一个新值。

for (name, w) in loss_net.named_parameters():
....
w.data = wt.data

这在这个问题中对我有用：如何在不破坏反向传播的情况下为 pytorch 变量分配一个新值？