如何使pytorch模型中的参数不存在于计算图中？

Question

我试图更新/更改神经网络模型的参数，然后将更新后的神经网络的前向传递放在计算图中（无论我们做了多少次更改/更新）

我尝试过这个想法，但每当我这么做的时候，pytorch都会将我更新的张量（在模型内部）设置为leafs，这会阻止梯度流向我想要接收梯度的网络。它扼杀了渐变流，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）

我试过很多方法，但似乎都不管用。我创建了一个自包含的伪代码，用于打印我希望具有梯度的网络的梯度：

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}')

如果有人知道怎么做，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出现任意次数…所以它必须工作2次

---

密切相关的职位：

• 所以：How does one have parameters in a pytorch model not be leafs and be in the computation graph?
• pytorch论坛：https://discuss.pytorch.org/t/how-does-one-have-the-parameters-of-a-model-not-be-leafs/70076

交叉张贴：

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

Answer 1

你应该尽量保持相同的张量，而不是创建新的张量

转到它们的data属性并设置一个新值

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

这在这个问题上对我起了作用：How to assign a new value to a pytorch Variable without breaking backpropagation?

Answer 2

无法正常工作，因为命名参数模块已被删除

---

这似乎有效：

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])

感谢：pytorch团队的强大albanD：https://discuss.pytorch.org/t/how-does-one-have-the-parameters-of-a-model-not-be-leafs/70076/9?u=pinocchio