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v0.11.0

huggingface/peft

版本发布时间: 2024-05-16 17:53:26

huggingface/peft最新发布版本:v0.12.0(2024-07-24 19:55:42)

Highlights

peft-v0 11 0

New methods

BOFT

Thanks to @yfeng95, @Zeju1997, and @YuliangXiu, PEFT was extended with BOFT: Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization (#1326, BOFT paper link). In PEFT v0.7.0, we already added OFT, but BOFT is even more parameter efficient. Check out the included BOFT controlnet and BOFT dreambooth examples.

VeRA

If the parameter reduction of LoRA is not enough for your use case, you should take a close look at VeRA: Vector-based Random Matrix Adaptation (#1564, VeRA paper link). This method resembles LoRA but adds two learnable scaling vectors to the two LoRA weight matrices. However, the LoRA weights themselves are shared across all layers, considerably reducing the number of trainable parameters.

The bulk of this PR was implemented by contributor @vvvm23 with the help of @dkopi.

PiSSA

PiSSA, Principal Singular values and Singular vectors Adaptation, is a new initialization method for LoRA, which was added by @fxmeng (#1626, PiSSA paper link). The improved initialization promises to speed up convergence and improve the final performance of LoRA models. When using models quantized with bitsandbytes, PiSSA initialization should reduce the quantization error, similar to LoftQ.

Quantization

HQQ

Thanks to @fahadh4ilyas, PEFT LoRA linear layers now support Half-Quadratic Quantization, HQQ (#1618, HQQ repo). HQQ is fast and efficient (down to 2 bits), while not requiring calibration data.

EETQ

Another new quantization method supported in PEFT is Easy & Efficient Quantization for Transformers, EETQ (#1675, EETQ repo). This 8 bit quantization method works for LoRA linear layers and should be faster than bitsandbytes.

Show adapter layer and model status

We added a feature to show adapter layer and model status of PEFT models in #1663. With the newly added methods, you can easily check what adapters exist on your model, whether gradients are active, whether they are enabled, which ones are active or merged. You will also be informed if irregularities have been detected.

To use this new feature, call model.get_layer_status() for layer-level information, and model.get_model_status() for model-level information. For more details, check out our docs on layer and model status.

Changes

Edge case of how we deal with modules_to_save

We had the issue that when we were using classes such as PeftModelForSequenceClassification, we implicitly added the classifier layers to model.modules_to_save. However, this would only add a new ModulesToSaveWrapper instance for the first adapter being initialized. When initializing a 2nd adapter via model.add_adapter, this information was ignored. Now, peft_config.modules_to_save is updated explicitly to add the classifier layers (#1615). This is a departure from how this worked previously, but it reflects the intended behavior better.

Furthermore, when merging together multiple LoRA adapters using model.add_weighted_adapter, if these adapters had modules_to_save, the original parameters of these modules would be used. This is unexpected and will most likely result in bad outputs. As there is no clear way to merge these modules, we decided to raise an error in this case (#1615).

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/peft/compare/v0.10.0...v0.11.0

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