🤖 Unlearning_LLM

This repo contains code and data for the ACL 2024 paper "Machine Unlearning of Pre-trained Large Language Models"

Paper | Dataset

🌟 Abstract

Abstract

This study investigates the concept of the `right to be forgotten' within the context of large language models (LLMs). We explore machine unlearning as a pivotal solution, with a focus on pre-trained models--a notably under-researched area. Our research delineates a comprehensive framework for machine unlearning in pre-trained LLMs, encompassing a critical analysis of seven diverse unlearning methods. Through rigorous evaluation using curated datasets from arXiv, books, and GitHub, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over $10^5$ times more computationally efficient than retraining. Our results show that integrating gradient ascent with gradient descent on in-distribution data improves hyperparameter robustness. We also provide detailed guidelines for efficient hyperparameter tuning in the unlearning process. Our findings advance the discourse on ethical AI practices, offering substantive insights into the mechanics of machine unlearning for pre-trained LLMs and underscoring the potential for responsible AI development.

📊 Dataset

We collect and provide the unlearn_dataset, which serves as a benchmark for evaluating unlearning methodologies in pre-trained large language models across diverse domains, including arXiv, GitHub. Access our unlearn_dataset directly on Hugging Face.

🔍 Loading the datasets

To load the dataset:

from datasets import load_dataset
dataset = load_dataset("llmunlearn/unlearn_dataset", name="arxiv", split="forget")

• Available configuration names and corresponding splits:
  • arxiv: forget, approximate, retain
  • github: forget, approximate, retain
  • general: evaluation, retain

✈️ How to run

Environment Setup

git clone https://github.com/yaojin17/Unlearning_LLM.git
cd Unlearning_LLM
conda install pytorch torchvision torchaudio cudatoolkit=11.8 -c pytorch
pip install -e .
pip install -r requirements.txt

Download Yi-6B model

mkdir models
cd models
git lfs install
git clone https://huggingface.co/01-ai/Yi-6B

Prepare tokenized datasets

cd utils
python save_tokenized_dataset.py --tokenizer_name_or_path ../../models/Yi-6B
python ascent_plus_descent_tokenizer.py --tokenizer_name_or_path ../../models/Yi-6B

Unlearning experiments

Remember to replace <your-wandb-key> in the run_unlearn.py, run_eval.py, and run_mia.py files to your own key.

# Make sure you are under the llm_unlearn dir
torchrun --nproc_per_node=8 --master_port=20001  run_unlearn.py   \
    --target_model_name_or_path ../../models/Yi-6B  \
    --per_device_train_batch_size 1     \
    --do_unlearn  \
    --output_dir ./output \
    --overwrite_output_dir     \
    --num_train_epochs 1    \
    --logging_steps 1     \
    --learning_rate 2e-5     \
    --warmup_ratio 0.03 \
    --overwrite_cache \
    --save_total_limit 1 \
    --fsdp "full_shard auto_wrap" \
    --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
    --bf16 True \
    --tf32 True \
    --weight_decay 0. \
    --lr_scheduler_type "cosine" \
    --domain github \
    --gradient_accumulation_steps 85 \
    --unlearn_method gradient_ascent 

• Available domains with corresponding arguments: :
  • --domain arxiv --gradient_accumulation_steps 60
  • --domain github --gradient_accumulation_steps 85
• Available methods with corresponding arguments:
  • --unlearn_method gradient_ascent
  • --unlearn_method random_label --completely_random True (named Fine-tuning with Random Labels in the paper)
  • --unlearn_method random_label --top_k 1 --rm_groundtruth True (named Unlearning with Adversarial Samples in the paper)
  • --unlearn_method ascent_plus_descent
  • --unlearn_method ascent_plus_kl_divergence
  • --unlearn_method ascent_plus_descent --general True
  • --unlearn_method ascent_plus_kl_divergence --general True

Eval unlearned model

torchrun --nproc_per_node=8 --master_port=20001 run_eval.py \
    --model_name_or_path ./output/github/Yi-6B/8_gpu_bs_1_gas_85_lr_2.0e_5_epoch1/unlearn/gradient_ascent \
    --per_device_eval_batch_size 1 \
    --do_eval \
    --output_dir ./output/github/Yi-6B-eval \
    --overwrite_output_dir \
    --overwrite_cache \
    --tf32 True \
    --domain github

Membership inference attack

torchrun --nproc_per_node=8 --master_port=20001 run_mia.py \
        --model_name_or_path ./output/github/Yi-6B/8_gpu_bs_1_gas_85_lr_2.0e_5_epoch1general/unlearn/ascent_plus_kl_divergence \
        --per_device_eval_batch_size 1 \
        --do_eval \
        --output_dir ./output/arxiv/Yi-6B-mia \
        --overwrite_output_dir \
        --overwrite_cache \
        --tf32 True \
        --domain github

⭐ Citation Information

If you find this code or dataset useful, please consider citing our paper:

@article{yao2024machine,
  title={Machine Unlearning of Pre-trained Large Language Models},
  author={Yao, Jin and Chien, Eli and Du, Minxin and Niu, Xinyao and Wang, Tianhao and Cheng, Zezhou and Yue, Xiang},
  journal={arXiv preprint arXiv:2402.15159},
  year={2024}
}

Contact

Feel free to reach out if you have any questions. Jin Yao, Eli Chien, Xiang Yue