@AjayTalati I managed to use BCELoss, binary_crossentropy and MultiLabelSoftMarginLoss on a MultiLabel problem
Here is the basic code
def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        # data, target = data.cuda(async=True), target.cuda(async=True) # On GPU
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.binary_cross_entropy(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))

And the source is here 271.
For BCELoss you can use criterion = BCELoss() and then loss = criterion(output, target) but as @Misha_E said, the NN must return a sigmoid activation.