How to calculate loss over a sliding window of samples and then backpropagate the weighted average loss

SlinkyPlatypus: <p>I am trying to implement a learning technique from a paper. The relevant portion is: The SNN baseline used a sliding window of 50 consecutive data points, representing 200 ms of data (50-point window, single-point stride) in order to calculate the loss, to allow for more information for backpropagation and avoid dead neurons and vanishing gradients. The MSE loss was linearly weighted from 0 to 1 for the 50 points within the window. However, I am getting issues with the gradient calculation.</p>
<p>My attempt at implementing this is below: `</p>
<pre><code class="lang-auto">for epoch in range(100):
    net.train()
    
    best_loss = float('inf')
    patience_counter = 0
    best_state = torch.save(net.state_dict(), "cur_state.pth")
    
    loss_window = deque(maxlen=50)
    window_sum = 0.0
    
    for i, data in enumerate(train_set_loader):
        inputs, label = data
        optimizer.zero_grad()
        outputs, _ = net(inputs)
        
        loss = criterion(outputs, label)
        
        loss_window.append(loss)
        
        window_sum = window_sum + loss.item()

        # # If the window is full, calculate the weighted loss and perform backpropagation
        if len(loss_window) == 50:
            weighted_loss = sum(((j + 1) / 50) * loss_value for j, loss_value in  enumerate(loss_window))
       
            print(f'weighted_loss: {weighted_loss}, requires_grad: {weighted_loss.requires_grad}')
            
            weighted_loss.backward(retain_graph=True)
            #weighted_loss.backward()
            optimizer.step()
            
            #Print gradients for debugging
            for name, param in net.named_parameters():
                if param.grad is not None:
                    print(f'{name}: {param.grad.norm()}')
            
            
            # Update the window sum by subtracting the oldest loss
            window_sum -= loss_window[0].item()

            #Remove oldest loss element
            loss_window.popleft()
  
        


    net.eval()
    val_loss = evaluate_model(net, criterion, val_set_loader)
    
    
    if val_loss &lt; best_loss:
        best_loss = val_loss
        best_state = torch.save(net.state_dict(), "cur_state.pth")
        patience_counter = 0
    else:
        patience_counter += 1
    
    if patience_counter &gt;= patience:
        print("Early Stopping")
        print(f"Best loss: {best_loss}")
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        net.load_state_dict(torch.load("cur_state.pth", map_location=device))
        print("Model state returned to best performing.")
        break
</code></pre>
<p>If I try without the retain_graph=True line in my backwards pass, I get the error that I’m trying to backward through the graph a second time. This makes sense as it is trying to backwards pass 49 of the same 50 gradients from the first call. When I do have retain_graph=True, I get the error: “one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [50, 2]]”. I believe this is an issue with the updating of loss_window, but I am not sure how to go about changing this. My question is somewhat similar to <a href="https://discuss.pytorch.org/t/manually-calculate-loss-over-a-number-of-images-and-then-back-propagate-the-average-loss-and-update-network-weight/9530" class="inline-onebox">Manually calculate loss over a number of images and then back propagate the average loss and update network weight</a> but in that question he doesn’t have overlap in his window which I believe avoids the problem.</p>

soulitzer: <p>The case where you have a large tensor you are viewing into is tricky for autograd because the single large tensor shares version counter, and doesn’t know whether the parts of that tensor you are mutating are actually the same parts that you saved for backward.</p>
<p>You could try using this context manager that will automatically apply copy on write to avoid this type of error  <a href="https://pytorch.org/docs/stable/autograd.html#torch.autograd.graph.allow_mutation_on_saved_tensors" class="inline-onebox" rel="noopener nofollow ugc">Automatic differentiation package - torch.autograd — PyTorch 2.3 documentation</a></p>
<p>Let me know if it works for your case.</p>

SlinkyPlatypus: <p>This seems to have solved my issue, thank you!</p>