PrivateLoRA for GenAI Model Privatization

| Paper | Weight | Blog | Demo |

Overview

PrivateLoRA separates private compute from the model and deploy them on edge devices for data locality. Network overhead is optimized to ensure throughput close to GPU on edge devices.

Reproducing Experiment

Dependencies

pip install deepspeed==0.12.5 transformers==4.35.2 accelerate==0.25.0 peft==0.5.0 termplotlib
pip install -e ./lm-evaluation-harness

Training

scripts folder contains launch scripts to train baseline methods and PrivateLoRA.

cd experiments
bash scripts/baseline.sh
bash scripts/pl.sh

Evaluation

We write a custom callback based on EleutherAI/lm-evaluation-harness to evaluate during training.

To use our callback, firstly install our adapted version of lm-eval.

pip install -e ./lm-evaluation-harness

Then, import the callback and plug it in the trainer in your training scripts.

from infras.eval import EvalHarnessCallBack
trainer.add_callback(
    EvalHarnessCallBack(
        trainer=trainer,              # huggingface trainer
        tokenizer=tokenizer,          # tokenizer
        tasks=["gsm8k_yaml","mmlu"],  # task name defined in `lm-eval`
        eval_steps=20,                # number of steps as evaluation interval
        eval_start=0,                 # evaluation start
        do_init_eval=False,           # evaluate before the first training step
        eval_batch_size=16, 
    )
)

Evaluation results will be logged in tensorboard during training.

Demo

To demonstrate that PrivateLoRA offers great user experience from model privatization, we make a runnable demo.

Screenshots of Edge Device running on CPU.

Central Cloud hosts the decoder stack of LlaMA 2-7B and Edge Device hosts private parameters. Central Cloud runs on GPU and Edge Device runs on CPU.

User of Edge Device queries the model with a question picked from GSM8K. Since it's 0-shot test, the original LLaMA 2-7B can barely answer it correctly. But parameters on "edge device" are trained on GSM8K, they will steer the model to correctly solve it!👍

Detailed instructions and explanations are documented here.

Citation

@article{privatelora,
  author       = {Yiming Wang and
                  Yu Lin and
                  Xiaodong Zeng and
                  Guannan Zhang},
  title        = {PrivateLoRA For Efficient Privacy Preserving {LLM}},
  journal      = {CoRR},
  volume       = {abs/2311.14030},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2311.14030},
  doi          = {10.48550/ARXIV.2311.14030},
  eprinttype    = {arXiv},
  eprint       = {2311.14030},
}