Alessio Fanelli: belief

Transcript context

…So the parameters are the FP32 copies of the parameters. We include them in the optimizer discussion. Some people don't, but just for clarity, it's 12 bytes per param for the optimizer states and four of them are for that FP32 copy of the weights. Four of them are for the momentum. I already went into why it's important to store momentum, but that's also per parameter. You need to store where that parameter is going and where it's been going in the past. You also need to know, okay, we know where it's going, but there's going to be bumps on this canyon that we're going down. So we need to store its variance. How often are those bumps? Should we be focusing more on the momentum? Or is this parameter just kind of jumping around everywhere? Those are all important answers that we need the optimizer to store, and it's per parameter. So that's where all three of those terms come from. And we also include some competing bits and bytes, for example, an SGD to show that depending on your optimizer, you may store all or none of these and in different representations. I'm looking at the total training memory. You essentially have model memory, optimizer memory, gradient memory, and activation memory. I think that's one of the last discussed things. So maybe just give people a little bit of a view. Yeah, this is completely new to me.…

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