Besides flipping the loss, you can modify the gradient update rule by using a for loop over network parameters and updating the gradient accordingly. This will give you more flexibility.
learning_rate = 0.01
for f in net.parameters():
    f.data.sub_(f.grad.data * learning_rate * -1.0)

(Source: code snippet taken from 60-min blaze)