GPT-3 was instead undertrained, being both larger and less performant than the hypothetical compute optimal alternative
You’re more fluent in the scaling laws than me: is there an easy way to roughly estimate how much compute would’ve been needed to train a model as capable as GPT-3 if it were done Chinchilla-optimally + with MoEs? That is: what’s the actual effective “scale” of GPT-3?
(Training GPT-3 reportedly took 3e23 FLOPS, and GPT-4 2e25 FLOPS. Naively, the scale-up factor is 67x. But if GPT-3′s level is attainable using less compute, the effective scale-up is bigger. I’m wondering how much bigger.)
IsoFLOP curves for dependence of perplexity on log-data seem mostly symmetric (as in Figure 2 of Llama 3 report), so overtraining by 10x probably has about the same effect as undertraining by 10x. Starting with a compute optimal model, increasing its data 10x while decreasing its active parameters 3x (making it 30x overtrained, using 3x more compute) preserves perplexity (see Figure 1).
GPT-3 is a 3e23 FLOPs dense transformer with 175B parameters trained for 300B tokens (see Table D.1). If Chinchilla’s compute optimal 20 tokens/parameter is approximately correct for GPT-3, it’s 10x undertrained. Interpolating from the above 30x overtraining example, a compute optimal model needs about 1.5e23 FLOPs to get the same perplexity.
(The effect from undertraining of GPT-3 turns out to be quite small, reducing effective compute by only 2x. Probably wasn’t worth mentioning compared to everything else about it that’s different from GPT-4.)
Thanks!
You’re more fluent in the scaling laws than me: is there an easy way to roughly estimate how much compute would’ve been needed to train a model as capable as GPT-3 if it were done Chinchilla-optimally + with MoEs? That is: what’s the actual effective “scale” of GPT-3?
(Training GPT-3 reportedly took 3e23 FLOPS, and GPT-4 2e25 FLOPS. Naively, the scale-up factor is 67x. But if GPT-3′s level is attainable using less compute, the effective scale-up is bigger. I’m wondering how much bigger.)
IsoFLOP curves for dependence of perplexity on log-data seem mostly symmetric (as in Figure 2 of Llama 3 report), so overtraining by 10x probably has about the same effect as undertraining by 10x. Starting with a compute optimal model, increasing its data 10x while decreasing its active parameters 3x (making it 30x overtrained, using 3x more compute) preserves perplexity (see Figure 1).
GPT-3 is a 3e23 FLOPs dense transformer with 175B parameters trained for 300B tokens (see Table D.1). If Chinchilla’s compute optimal 20 tokens/parameter is approximately correct for GPT-3, it’s 10x undertrained. Interpolating from the above 30x overtraining example, a compute optimal model needs about 1.5e23 FLOPs to get the same perplexity.
(The effect from undertraining of GPT-3 turns out to be quite small, reducing effective compute by only 2x. Probably wasn’t worth mentioning compared to everything else about it that’s different from GPT-4.)