When fine tuning an LLM you can use the LORA technique to make the fine tuning faster. LORA involves fine tuning a subset of parameters (really it's a low rank approximation of the weight matrix determined by picking the n largest eigenvalues in the SVD decomposition). The size of the subset is determined by the rank. The smaller the rank the faster the fine tuning. However if you make the rank too small then quality will suffer. So you want to pick the optimal rank. This paper describes a technique which can be used to find the optimal rank more easily.
Would you say the following understanding is correct?:
- You can fine-tune a model, regardless of whether it has been quantized (as in the 4-bit versions of models made to fit in consumer grade RAM sizes) or not.
- You can fine-tune any model on any hardware, provided it fits into RAM. That means, that the 30B llama-derived models in their 4-bit quantized version and 19.5GB of VRAM requirement can be fine-tuned on consumer grade GPUs with 24gb of VRAM. (Like the RTX 3090 and 4090)
One approach is to have the model learn to select between several separately fine tuned adapters by learning which adapter works best in a given context. So at any given time it's only really using one adapter but can switch to another. In this case one adapter can't really improve another but the overall impact might be a model which is improved in a variety of different contexts.
Yes, but the naïve way to combine rank k adaptations created by n different people would be to concatenate them to a rank nk adaptation, which wouldn't be as lightweight and easy to share, so you'd likely be better off mushing them into the baseline model.
Can they mathematically be “mushed” and then create an improved model?
I have yet to understand the difference between fine tuning and training and therefore yet to understand if a distributed decentralized eventually consistent training approach is a possibility or simply not realistic.
Kudos for the authors for providing the code https://github.com/huawei-noah/KD-NLP/tree/main/DyLoRA and the roberta example. Considering the current state of the OSS LLM community, I'm guessing someone is already porting it to Llama and gpt-style models.
I'm unsure of the value of dynamically reducing the rank of the LoRA matrix at inference time given that probably most of the parameter count comes from the original weights rather than the LoRA diff.
But nonetheless, training time improvements look interesting.
e: Oh I see, the training time improvement is compared to a grid search over the LoRA rank. Not for a single run.
I am not convinced that you shouldn't just train on the highest possible rank that you can with your compute budget. If you can train a DynLoRA with rank 8, why not just train a LoRA with that rank?
Err, I suppose trivially, the higher rank terms include the lower-rank subnets, so they dominate in terms of quality.
But if you have some capacity constraint (e.g., memory, I guess?) then you can imagine dynamic rank allocation helping in the case where the maximum rank across all layers isn't within budget.
As someone else mentioned [0], the procedure would basically be to train a DyLoRA for an initial few iterations, then do a search among the layers to find the best scoring combination of ranks, and then train pruned to just use those ranks to completion.
Seems complicated but I could see it being useful potentially.
So this can tune a model 7X faster than LoRA, which was already a massive speed boost? Curious to see what this will do to the LLaMA-derivative community in particular.
Highest posssible in which combination, though? If you’re fine tuning a model with N layers, then you could apply LoRA to any or all of them. Maybe it’s better to concentrate effort unevenly, in which case a uniform increase of adaptation rank (to compute budget) could still be subpar.
Fastest way to show what? That you should train with the maximum sized LoRA you can? Because the only upsides to having a smaller LoRA are in the training time, and if you are already able to train a DynLoRA with max rank 8, then you should just train a LoRA with that rank.
You get diminishing returns as you increase the rank, so with a fixed training budget it's not clear whether you get the best return from increasing rank vs increasing something else. If you start off by training DynLORA with max rank 8 you can see returns diminish fast beyond rank 5. Then you can use rank 5 for the rest of your training. You wouldn't know that with LoRA. I think this is the idea behind the paper. If you are just going to use your entire budget training a DyLoRA with max rank 8 then you're right there's no advantage over LoRA with rank 8. You'd have to use the ability to assess multiple ranks in order to see some benefit.
I can see that. But are we sure that a rank-based difference that doesn't manifest early in the training process won't manifest as you get further along? See also 'grokking' [0]
Why is the only advantage at training time? I might misunderstand something but with this method you can train once, and then deploy models that use arbitrary rank (according to end-users compute requirements) and expect to have a model that performs best for that specific rank.
It's very different, but hard to distill in a comment. They use a new regularization technique to basically create a LoRA with dynamically adjustable rank.