You can always leverage the fact that nan != nan:
>>> x = torch.tensor([1, 2, np.nan])
tensor([ 1., 2., nan.])
>>> x != x
tensor([ 0, 0, 1], dtype=torch.uint8)
With pytorch 0.4 there is also torch.isnan:
>>> torch.isnan(x)
tensor([ 0, 0, 1], dtype=torch.uint8)