Can't backward pass two losses in Classification Transformer Model
For my model I'm using a roberta transformer model and the Trainer from the Huggingface transformer library.
I calculate two losses:
lloss is a Cross Entropy Loss and dloss calculates the loss inbetween hierarchy layers.
The total loss is the sum of lloss and dloss. (Based on this)
When calling total_loss.backwards() however, I get the error:
RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed

Any idea why that happens? Can I force it to only call backwards once? Here is the loss calculation part:
dloss = calculate_dloss(prediction, labels, 3)
lloss = calculate_lloss(predeiction, labels, 3)
total_loss = lloss + dloss 
total_loss.backward()

def calculate_lloss(predictions, true_labels, total_level):
    '''Calculates the layer loss.
    '''

    loss_fct = nn.CrossEntropyLoss()

    lloss = 0
    for l in range(total_level):

        lloss += loss_fct(predictions[l], true_labels[l])

    return self.alpha * lloss

def calculate_dloss(predictions, true_labels, total_level):
    '''Calculate the dependence loss.
    '''

    dloss = 0
    for l in range(1, total_level):

        current_lvl_pred = torch.argmax(nn.Softmax(dim=1)(predictions[l]), dim=1)
        prev_lvl_pred = torch.argmax(nn.Softmax(dim=1)(predictions[l-1]), dim=1)

        D_l = self.check_hierarchy(current_lvl_pred, prev_lvl_pred, l)  #just a boolean tensor

        l_prev = torch.where(prev_lvl_pred == true_labels[l-1], torch.FloatTensor([0]).to(self.device), torch.FloatTensor([1]).to(self.device))
        l_curr = torch.where(current_lvl_pred == true_labels[l], torch.FloatTensor([0]).to(self.device), torch.FloatTensor([1]).to(self.device))

        dloss += torch.sum(torch.pow(self.p_loss, D_l*l_prev)*torch.pow(self.p_loss, D_l*l_curr) - 1)

    return self.beta * dloss


There is nothing wrong with having a loss that is the sum of two individual losses, here is a small proof of principle adapted from the docs:
import torch
import numpy
from sklearn.datasets import make_blobs

class Feedforward(torch.nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Feedforward, self).__init__()
        self.input_size = input_size
        self.hidden_size  = hidden_size
        self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
        self.relu = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(self.hidden_size, 1)
        self.sigmoid = torch.nn.Sigmoid()
    def forward(self, x):
        hidden = self.fc1(x)
        relu = self.relu(hidden)
        output = self.fc2(relu)
        output = self.sigmoid(output)
        return output

def blob_label(y, label, loc): # assign labels
    target = numpy.copy(y)
    for l in loc:
        target[y == l] = label
    return target

x_train, y_train = make_blobs(n_samples=40, n_features=2, cluster_std=1.5, shuffle=True)
x_train = torch.FloatTensor(x_train)
y_train = torch.FloatTensor(blob_label(y_train, 0, [0]))
y_train = torch.FloatTensor(blob_label(y_train, 1, [1,2,3]))

x_test, y_test = make_blobs(n_samples=10, n_features=2, cluster_std=1.5, shuffle=True)
x_test = torch.FloatTensor(x_test)
y_test = torch.FloatTensor(blob_label(y_test, 0, [0]))
y_test = torch.FloatTensor(blob_label(y_test, 1, [1,2,3]))


model = Feedforward(2, 10)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)


model.eval()
y_pred = model(x_test)
before_train = criterion(y_pred.squeeze(), y_test)
print('Test loss before training' , before_train.item())

model.train()
epoch = 20
for epoch in range(epoch):
    optimizer.zero_grad()    # Forward pass
    y_pred = model(x_train)    # Compute Loss
    lossCE= criterion(y_pred.squeeze(), y_train)
    lossSQD = (y_pred.squeeze()-y_train).pow(2).mean()
    loss=lossCE+lossSQD
    print('Epoch {}: train loss: {}'.format(epoch, loss.item()))    # Backward pass
    loss.backward()
    optimizer.step()

There must be a real second time that you call directly or indirectly backward on some varaible that then traverses through your graph. It is a bit too much to ask for the complete code here, only you can check this or at least reduce it to a minimal example (while doing so, you might already find the issue). Apart from that, I would start checking:

Does it already occur in the first iteration of training? If not: are you reusing any calculation results for the second iteration without a detach?
When you do backward on your losses individually lloss.backward() followed by dloss.backward() (this has the same effect as adding them together first as gradients are accumulated): what happens? This will let you track down for which of the two losses the error occurs.

