Quentin Anthony: observation

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…Sure. So when I started deep learning, it was all FP32. You have 32 bits, four bytes per parameter. Things were pretty simple. You didn't have to do any loss scaling at all. But the problem was that you didn't get a whole lot of flops once NVIDIA moved to V100s and introduced Tensor cores. So Tensor cores do all of their computation at FP16 precision. So you're kind of throwing all of those away if you're doing things in FP32. So once the hardware moved to V100, the software moved to like mixed precision and APEX and AMP and such. And one counterintuitive part of mixed precision is that you actually require more memory when you're trained because you need an FP16 copy of the weights and an FP32 copy of the weights. The FP16 copy is where you're doing like your actual computation on the Tensor cores. So you get maybe it's not uncommon to get double the throughput that you would see before in FP32. And then you at each step update that FP32 copy with the FP16 update. So both need to be stored in memory. The problem with that is that FP16 is very precise but doesn't have a whole lot of range, dynamic range.…

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