In that case, you can think of the prompt as being one vector of the model that is being tuned while the rest is frozen.
Not exactly the same, to be sure. But fulfills a similar need: more efficient "fine tuning" of a large model.
Not exactly the same, to be sure. But fulfills a similar need: more efficient "fine tuning" of a large model.
"The other direction, as exemplified by prefix tuning (Li & Liang, 2021), faces a different challenge. We observe that prefix tuning is difficult to optimize and that its performance changes non-monotonically in trainable parameters, confirming similar observations in the original paper. More fundamentally, reserving a part of the sequence length for adaptation necessarily reduces the sequence length available to process a downstream task, which we suspect makes tuning the prompt less performant compared to other methods."
https://ar5iv.labs.arxiv.org/html/2106.09685
This is key imo: "More fundamentally, reserving a part of the sequence length for adaptation necessarily reduces the sequence length available to process a downstream task".
The benefit of prompt and prefix tuning (note: these are two separate methods) is that you can serve different soft-prompts and soft-prefixes efficiently with a single shared set of model weights.
> incurs a non-trivial computation cost
The hit seems to be in energy/cpu not time since the W0 computation is in parallel with the BAx. (My assumption based on the latency claims in paper.) So an issue in edge deployments (battery life, etc.).
> you are stuck with that one model on that device
Upfront I have 0 clue on the actual numbers, but from a purely software architecture pov [in unmerged setup], having that W0 forward process once with n distinct BAx paths (for distinct fine tunings!) would address that, no?
[p.s. say an application that takes as input A/V+Txt, runs that through an Ensemble LoRA (ELoRA™ /g) which each participant contributing its own BAx finetuing processing, sharing the single pre-trained W0.]
The latency claims are based on the merged version, where the modifications are merged into the model weights. Hence there is no latency cost, since the final model has the same shape as the original.
> having that W0 forward process once with n distinct BAx paths (for distinct fine tunings!) would address that, no?
The tl;dr is that that works, but is more expensive. Not ridiculously more expensive, but certainly more expensive that processing a few additional tokens with prefix/prompt tuning.
If one is careful with floating point issues, it's straightforward to unmerge the weights.
W_0 = W_1 - BA
Yes, prompt-based methods don't involve swapping weights.
(Also, I'm assuming you're the first author of LoRA.)
Yup, I am!