Docs Clarification: Is prepare() inefficient for models that are frozen?
The docs specify “You don’t need to prepare a model if you only use it for inference without any kind of mixed precision” but make it unclear whether there’s any difference or inefficiency added here.For example, I have an nn.Module that I will perform distributed training with in bfloat16, but it has several child components (themselves nn.Modules) that are frozen for pure inference.In this case, is there a difference (specifically w.r.t. performance) betweenaccelerator.prepare(model.trainable_submodel)andaccelerator.prepare(model), besides having to manually move to device/dtype? Obviously the 2nd is more convenient but does this wrapper cause any performance loss (in my case I’m using simple DDP).I saw thisrelated questionbut it doesn’t directly address this.Thanks in advance!