Could you post a code snippet regarding the bug, as it could be serious?
This dummy example works as expected:
N = 10
x = torch.ones(N, 1)
y = torch.zeros(N, 1)

print(F.mse_loss(x, y, reduction='none'))
print(F.mse_loss(x, y, reduction='mean'))
print(F.mse_loss(x, y, reduction='sum'))

>tensor([[1.],
        [1.],
        [1.],
        [1.],
        [1.],
        [1.],
        [1.],
        [1.],
        [1.],
        [1.]])
tensor(1.)
tensor(10.)