As far as I understand, BCEWithLogitsLoss is used for Binary Cross Entropy loss and MultiLabelSoftMarginLoss for Multi-Label Cross Entropy loss.
Sure, when you have a binary case both of them will give you the same result. But using BCEWithLogitsLoss for binary classification will make your code more readable and easier to comprehend.