Backward error with retain_graph=True

for j in range(n_rnn_batches):
    print x.size()
    h_t = Variable(torch.zeros(x.size(0), 20))
    c_t = Variable(torch.zeros(x.size(0), 20))
    h_t2 = Variable(torch.zeros(x.size(0), 20))
    c_t2 = Variable(torch.zeros(x.size(0), 20))
    for s in range(n_steps / n_bptt_steps):
        h_t, c_t, h_t2, c_t2 = Variable(, Variable(, Variable(, Variable(
        for s2 in range(n_bptt_steps):
            data, target = next(iter((train_loader)))
            data, target = Variable(data), Variable(target)
            output = optimizee(data, x)
            loss = F.nll_loss(output, target)
            grads = autograd.grad(loss, x, retain_graph=True)[0].view(x.size(0),-1)
            grads.volatile = False
            grads = grads.detach()

            out, h_t, c_t, h_t2, c_t2 = rnn(grads, h_t, c_t, h_t2, c_t2)
            x = x - out
            print x.volatile

I’m trying to implement Learning to Learn by Gradient Descent By Gradient Descent. But I’m getting the Trying to backward through the graph a second time error in the loss.backward() step, even though I have set retain_graph to True. Can someone please help?

n_bptt_steps at some point, so it doesn’t go into the inner loop. That’s the reason for your error.

I’m sorry, I didn’t get you. But it does go into the inner loop.

not always. at some point it doesn’t go into the inner loop and uses the loss from the last outer loop iteration in loss.backward(). That’s what I think is happening that corresponds to the error message.

I have set n_bptt_steps to 2, hence it always goes into that loop. And I just noticed that the first iteration of the outer loop works fine, but it throws that error in the second iteration(i.e. s=1). Do you know why that maybe? I’m sorry if these questions as silly, but I’m pretty new to pytorch.