I really wonder sometimes where I left my brain.
Thank you! That was the mistake I made.
And now I’m getting a loss which I understand 
BCEWLL:
criterion = nn.BCEWithLogitsLoss(reduction='none', pos_weight=samples_weight)
pre = torch.tensor([-8., 0., 4., -2.])
des = torch.tensor([0., 0., 1., 0.])

criterion(pre, des)
--> tensor([3.3569e-04, 6.9315e-01, 1.8150e-02, 1.2693e-01])

torch.mean(criterion(pre,des))
--> tensor(0.2096)

Manual:
pre = torch.tensor([-8., 0., 4., -2.])
des = torch.tensor([0., 0., 1., 0.])
import math, torch, torch.nn as nn
import numpy as np
def bll(pre, des):
  bitloss = 0
  for p, d in zip(pre, des):
    sp = torch.sigmoid(p)
    loss = -(d*(math.log(sp))+(1-d)*math.log(1-sp))
    bitloss += loss
    print(loss)
  return bitloss

ll = bll(pre, des)
BCEWLL = (1/len(pre))*ll
print(f"MEAN:{BCEWLL}")

--> tensor(0.0003)
tensor(0.6931)
tensor(0.0181)
tensor(0.1269)
MEAN:0.20964014530181885

The only thing I wonder is how -8 can have such a low loss value when it’s the farthest away?
According to log(1 - 1/(1 + e^8)) it should also be a negative value.
EDIT:
Well I answer my question myself. Lowest loss because it’s the farthest away and thus “very wrong” -> Not dangerous to interfere the true prediction. The 0 prediction is the nearest so the biggest loss there.