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Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

License: MIT Arxiv Hugging Face Transformers GitHub Stars

This is the implementation for the paper Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference.

Install

conda create -n uld python=3.10 -y
conda activate uld
conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit -y
pip install flash-attn==2.5.6 --no-build-isolation
pip install deepspeed
pip install -e .

Training

ULD Training

python scripts/hf_forget_train.py \
    data=[tofu|harry] \
    data.dataset.split=${DATASPLIT} \
    model=[tofu-llama-2|mistral] \
    model_mode=uld \
    model_mode.num_layer=8 \
    unlearn_loss=remember+uniform \
    trainer.strategy=ddp \
    OUTPUTMODELDIR=${OUTPUTMODELDIR}

For more detailed training options, please refer to bashes/tofu/uld_train_eval.sh and bashes/harry/uld_train_eval.sh. This would save the assistant model to ${OUTPUTMODELDIR}.

Offset Training

python scripts/hf_forget_train.py \
    data=[tofu|harry] \
    data.dataset.split=${DATASPLIT} \
    model=[tofu-llama-2|mistral] \
    model_mode=offset \
    unlearn_loss=${UNLEARN_LOSS} \
    trainer.strategy=deepspeed \
    OUTPUTMODELDIR=${OUTPUTMODELDIR}

For more detailed training options, please refer to bashes/tofu/offset_train_eval.sh and bashes/harry/offset_train_eval.sh. This would save the assistant model to ${OUTPUTMODELDIR}.

Other Training

python scripts/hf_forget_train.py \
    data=[tofu|harry] \
    data.dataset.split=${DATASPLIT} \
    model=[tofu-llama-2|mistral] \
    model_mode=base \
    unlearn_loss=${UNLEARN_LOSS} \
    trainer.strategy=deepspeed \
    OUTPUTMODELDIR=${OUTPUTMODELDIR}

For more detailed training options, please refer to bashes/tofu/base_train_eval.sh and bashes/harry/base_train_eval.sh. This would save the unlearned model to ${OUTPUTMODELDIR}.

Evaluation

python scripts/eval_tofu.py \
    data=[tofu|harry] \
    model=[tofu-llama-2|mistral] \
    model_mode=[base|uld|offset] \
    ckpt_path=${CHECKPOINT_DIR} \
    data.dataset.split=${DATASPLIT}

For more detailed options, please refer to bashes/tofu/tofu_eval.sh and bashes/harry/harry_eval.sh.

Development

We also implement several other unlearning methods employed in previous works, including:

You can follow the guide below to implement other unlearning methods.

Code Structure

  • scripts/: scripts for training and evaluation
  • uld/data/: data processing and dataloader
  • uld/models/: model definition
  • uld/trainer/: unlearn trainer and unlearn losses.

Add other dataset

  • Add dataset to uld/data/ and register it in uld/data/__init__.py
  • Implement new dataset class by inheriting TrainDataModule class, reference implementation for ToFU dataset is in uld/data/tofu.py. Typically, you need to implement the logic to load forget data and retain data.

Add other unlearn loss

  • Add unlearn loss to uld/trainer/unlearn_losses.py and add it in configs/unlearn_loss.
  • Implement new unlearn loss class by defining the forget_loss_func and retain_loss_func for ForgetRetainLoss class, reference implementation is in create_unlearn_loss function.

Citation

If you find this work useful, please consider cite our paper:

@article{ji2024reversing,
  title   = {Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference},
  author  = {Jiabao Ji and Yujian Liu and Yang Zhang and Gaowen Liu and Ramana Rao Kompella and Sijia Liu and Shiyu Chang},
  year    = {2024},
  journal = {arXiv preprint arXiv: 2406.08607}
}

Acknowledgement

Huge thanks for following repos that greatly help our implementation: