You are saturating the sigmoid with such a high number.
Have a look at this small test:
labels = torch.zeros([1])

for x in torch.linspace(10, 100):
    print((torch.sigmoid(x) - 1.) == 0, ' for ', x)
    x = x.view(1)
    ce = F.binary_cross_entropy(torch.sigmoid(x), labels)
    ce_logit = F.binary_cross_entropy_with_logits(x, labels)
    err = torch.abs(ce - ce_logit)
    print('error ', err)

As you can see, the first print statement will return True for logits of ~17, which means the limited floating point precision returns a value of 1. for torch.sigmoid(x).
Likewise the error will jump up by a large margin.