Is torch.empty_like truely random?

[MasterGodzilla]

Edit: nvm i was stupid.

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Hi, I saw in the generate.py, you guys used below code to do gumbel sampling

def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization
q = torch.empty_like(probs_sort).exponential_(1)
return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)

Are you sure that empty_like behaves the same as uniform random variable? I mean, it seems like it just take what ever value is in the stack at the moment, and can very likely follow some pattern and not behave pseudorandomly. Would this affect the sampling and cause bias?

[Horace He]

Sounds like you figured it out, but for future reference, the exponential_ in-place modifies the empty_like.