This is also the case for other initializations, just rand in the following case. The problem scales with the size of the tensor:
for i in range(10):
    a = torch.rand(3, 10000)
    print(a.sum() - a[[2, 1, 0]].sum())

outputs:
tensor(0.0020)
tensor(0.0029)
tensor(-0.0020)
tensor(-0.0010)
tensor(-0.0039)
tensor(0.0049)
tensor(0.0039)
tensor(0.0020)
tensor(-0.0039)
tensor(-0.0020)

I was also able to reproduce this in pytorch 0.4.1.