RunTime Error: One of the differentiated Tensors appears to not have been used in the graph

I’m currently implementing a project for my machine learning course, and i have faced an issue in gradient computation based on additional trainable parameters. I’m trying actually to implement the algorithm

with the help also of a github project that i’m taking as reference.
I’m receving “RuntimeError: One of the differentiated Tensors appears to not have been used in the graph. Set allow_unused=True if this is the desired behavior” during step 10 “Computation of gradients w.r.t epsilon”
The piece of my code that implements from step 1 to step 10 is as follows

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=gamma, momentum=rho)
for epoch in range(T):
    running_loss = 0.0
    for idx, (train_x, train_label) in enumerate(trainloader):
      # initialize a dummy network for the meta learning of the weights
      meta_net = LeNet().to(device)
      meta_net_optimizer = torch.optim.SGD(meta_net.parameters(), lr=gamma, momentum=rho)
      # Step 4 - 5 forward pass to compute the initial weighted loss
      # Forward propagation
      outputs = meta_net(train_x)
      # Error evaluation
      train_label = train_label.squeeze()
      loss = criterion(outputs,train_label.long())
      eps = to_var(torch.zeros(loss.size()))
      l_f_meta = torch.sum(loss * eps)
      # Line 6 perform a parameter update
      grads = torch.autograd.grad(l_f_meta, meta_net.parameters(), create_graph=True)
      # Line 8 - 10 2nd forward pass and getting the gradients with respect to epsilon
      y_g_hat = meta_net(test_data_t)
      l_g_meta = criterion(y_g_hat,test_labels_t.long())  
      grad_eps = torch.autograd.grad(l_g_meta, eps, only_inputs=True) # >> Step 10

Could you advise please why i’m receiving that? Supposedly, epsilon is a tensor. Issue is in
grad_eps = torch.autograd.grad(l_g_meta, eps, only_inputs=True). My target is to

In torch.zeros have you tried setting requires grad to True like this:

      eps = to_var(torch.zeros(loss.size(), requires_grad=True))

Same issue. Is there a way to determine the differentiable variables? Because i get that eps cannot be found but no idea why

No because eps is not in the graph the derivative will always be none. I do not know a way to get around this. You could try looking at these functions to see if you can use them to calculate the correct gradients but I cannot find a why to add eps to the graph.