Quentin Anthony: observation

Transcript context

…I want to put this equation to the test a little bit. We can do this from either GPT-3's perspective or GPT-NeoX, whatever you're more comfortable with. You have this distinction of actual flops versus theoretical flops. And a lot of times when people report the flops it took to train a model, like we just saw one in Lama 2 where the estimate is something that the amount of flops and that's, that's what we go with. So GPT-3 took a 3.14 times 10 to the power 23 flops. That is the theoretical flops. I want to get to a point where I can sort of work out if a number passes the smell test. And I wonder how to do that because I should be able to plug in this equation, right? I know that GPT-3 was trained on 300 billion tokens. I know the parameter size of 175. Is it, is it just like a 6 times 175 times 300? Like I haven't done the math, but what are the nuances here that you might want to call out? Theoretical flops is usually given from, you have a given set of hardware and this is what you expect your hardware to get. The problem is that in practice, full utilization, that's the key word, right? Because in practice, there are a lot of cases where like you're spending time waiting on data movement from like the GPU to CPU. Or for example, you might be waiting to synchronize across the different GPUs. So there's a lot of idle time basically that you're going to be spending during training. Smell tests.…

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