pytorch 中的 autograd 可以处理同一模块中层的重复使用吗？

Question

Suppose I have a layer layer in a torch module and use it twice or more times during a single forward step, in a way that the result output by this layer is later again inputed to the same layer . Can pytorch's autograd compute the grad of the weights of this layer correctly?

Here is an mwe of what I am talking about:

import torch
import torch.nn as nn
import torch.nn.functional as F

class net(nn.Module):
    def __init__(self,in_dim,out_dim):
        super(net,self).__init__()
        self.layer = nn.Linear(in_dim,out_dim,bias=False)

    def forward(self,x):
        x = self.layer(x)
        x = self.layer(x)
        return x

input_x = torch.tensor([10.])
label = torch.tensor([5.])
n = net(1,1)
loss_fn = nn.MSELoss()

out = n(input_x)
loss = loss_fn(out,label)
n.zero_grad()
loss.backward()

for param in n.parameters():
    w = param.item()
    g = param.grad

print('Input = %.4f; label = %.4f'%(input_x,label))
print('Weight = %.4f; output = %.4f'%(w,out))
print('Gradient w.r.t. the weight is %.4f'%(g))
print('And it should be %.4f'%(4*(w**2*input_x-label)*w*input_x))

And the output is (may be different on your computer if the initial value of the weight is different):

Input = 10.0000; label = 5.0000
Weight = 0.9472; output = 8.9717
Gradient w.r.t. the weight is 150.4767
And it should be 150.4766

In this example, I have defined a module with only one linear layer ( in_dim=out_dim=1 and no bias). w is the weight of this layer; input_x is the input value; label is the desired value. Since the loss is chosen as MSE, the formula for the loss is

((w^2)*input_x-label)^2

Computing by hand, we have

dw/dx = 2*((w^2)*input_x-label)*(2*w*input_x)

The output of my example above shows that autograd gives the same result as computed by hand, giving me a reason to believe that it can work in this case. But in a real application, the layer may have inputs and outputs of higher dimensions, a nonlinear activation function after it, and the neural network could have multiple layers.

What I want to ask is: can I trust autograd to handle such situation, but a lot more complicated than that in my example? How does it work when a layer is called iteratively?

Answer 1

This will work just fine. From the perspective of the autograd engine this isn't a cyclic application since the resulting computation graph will unwrap the repeated computation as a linear sequence. To illustrate this, for a single layer you might have:

x -----> layer --------+
           ^           |
           |  2 times  |
           +-----------+

From the autograd perspective this looks like:

x ---> layer ---> layer ---> layer

Here layer is the same layer copied 3 times over the graph. This means when computing the gradient for the layer's weights they will be accumulated from all the three stages. So when using backward :

x ---> layer ---> layer ---> layer ---> loss_func
                                            |
       lback <--- lback <--- lback <--------+
         |          |          |
         |          v          |
         +------> weights <----+
                   _grad

Here lback represents the local derivative of the layer forward transformation which uses the upstream gradient as an input. Each one adds to the layer's weights_grad .

Recurrent Neural Networks use this repeated application of layers (cells) at their basis. See for example this tutorial about Classifying Names with a Character-Level RNN .