You can add the L1 regularization to your loss and call backward on the sum of both.
Here is a small example using the weight matrix of the first linear layer to apply the L1 reg:
model = nn.Sequential(
    nn.Linear(10, 10),
    nn.ReLU(),
    nn.Linear(10, 2)
)

x = torch.randn(10, 10)
target = torch.empty(10, dtype=torch.long).random_(2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=1e-3)

for epoch in range(1000):
    optimizer.zero_grad()
    output = model(x)
    loss = criterion(output, target)
    l1_norm = torch.norm(model[0].weight, p=1)
    loss += l1_norm
    loss.backward()
    optimizer.step()
    
    print('Epoch {}, loss {}, mat0 norm {}'.format(
        epoch, loss.item(), l1_norm.item()))

If you comment out loss += l1_norm you’ll see, that the norm won’t necessarily be decreased.