I, naively (an uninformed guess), considered the non-determinism (multiple results possible, even with temperature=0 and fixed seed) stemming from floating point rounding errors propagating through the calculations.
How wrong am I ?
> The non-determinism at temperature zero, we guess, is caused by floating point errors during forward propagation. Possibly the “not knowing what to do” leads to maximum uncertainty, so that logits for multiple completions are maximally close and hence these errors (which, despite a lack of documentation, GPT insiders inform us are a known, but rare, phenomenon) are more reliably produced.
Are you talking about a DAG of FP calculations, where parallel steps might finish in different order across different executions? That's getting out of my area of knowledge, but I'd believe it's possible
but they're not: they are scheduled on some infrastructure in the cloud. So the code version might be slightly different, the compiler (settings) might differ, and the actual hardware might differ.
With a fixed seed there will be the same floating point rounding errors.
A fixed seed is enough for determinism. You don't need to set temperature=0. Setting temperature=0 also means that you aren't sampling, which means that you're doing greedy one-step probability maximization which might mean that the text ends up strange for that reason.
