4 ms·
https://ar5iv.labs.arxiv.org/html/2106.09685/assets/x1.png https://ar5iv.labs.arxiv.org/html/2106.09685/assets/x1.png > incurs a non-trivial computation cost
by eternalban 4y ago
https://ar5iv.labs.arxiv.org/html/2106.09685/assets/x1.png https://ar5iv.labs.arxiv.org/html/2106.09685/assets/x1.png
> 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.]
- arugulum 4y ago> My assumption based on the latency claims in paper. 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.