LLoCO: Learning Long Contexts Offline

Paper

LLoCO is a technique that learns documents offline through context compression and in-domain parameter-efficient finetuning using LoRA, which enables LLMs to handle long context efficiently.

Getting Started

Installation

Setup a new environment and run:

pip install -r requirements.txt

Download Datasets (Optional)

Use the following command to download the QuALITY dataset. Other datasets are loaded from HuggingFace and can be downloaded automatically during data loading.

cd data
wget https://raw.githubusercontent.com/nyu-mll/quality/main/data/v1.0.1/QuALITY.v1.0.1.htmlstripped.train
wget https://raw.githubusercontent.com/nyu-mll/quality/main/data/v1.0.1/QuALITY.v1.0.1.htmlstripped.dev

Preprocess Summary Embeddings

First generate summary embeddings for the datasets. An example bash script is stored in scripts/preproc_emb.sh, which preprocess the training dataset of QuALITY:

python3 preproc_embs.py \
    --emb_model_name "autocomp" \
    --dataset quality \
    --split train \
    --data_path ./data/QuALITY.v1.0.1.htmlstripped.train \
    --out_path ./embeddings/quality_train_embs.pth \
    --truncation False \

This script will generate summary embeddings for QuALITY training set, and store the embeddings in the /embeddings folder. Embedding generation for other datasets works similarly.

Finetune

Here is an example bash script to finetune the QuALITY dataset. This script is in scripts/finetune_quality.sh.

torchrun --nproc_per_node=4 finetune_quality.py  \
        --output_dir output/lloco_quality \
        --run_name lloco_quality \
        --data_path ./data/QuALITY.v1.0.1.htmlstripped.train \
        --embedding_path ./embeddings/quality_train_embs.pth \
        ...

Inference & Evaluation

Below is a bash script to run inference over the validation sets are contained in script/inference.sh. Evaluation results are stored in out_path, and the finetuned model is specified by peft_model.

python3 inference.py  \
    --model_name_or_path meta-llama/Llama-2-7b-chat-hf \
    --dataset_name qmsum \
    --eval_mode autocomp \
    --out_path ./eval/qmsum_lloco.json \
    --peft_model output/lloco_qmsum  \
    --embedding_path ./embeddings/qmsum_val_embs.pth \
    ...

After obtaining the prediction files, use the following evaluation scripts in the /eval folder to get the scores for each dataset.

Evaluate QuALITY:

python3 quality_evaluator.py --quality_path {quality_path} --pred_path {prediction_file}

Evaluate QMSum, Qasper, NarrativeQA:

python3 scroll_evaluator.py --split validation --dataset_name {dataset_name} --predictions {prediction_file} --metrics_output_dir .

Evaluate HotpotQA:

python3 hotpot_evaluator.py --pred_path {prediction_fild}

TODOs

Release finetuning and inference code.
Release pre-trained LoRA weights on HuggingFace.
Integrate to VLLM.

Citation

If you find LLoCO useful or relevant to your project and research, please kindly cite our paper:

@article{tan2024lloco,
  title   = {LLoCO: Learning Long Contexts Offline},
  author  = {Sijun Tan and Xiuyu Li and Shishir Patil and Ziyang Wu and Tianjun Zhang and Kurt Keutzer and Joseph E. Gonzalez and Raluca Ada Popa},
  year    = {2024},
  journal = {arXiv preprint arXiv: 2404.07979}
}

Acknowledgements

We referred to AutoCompressors for the context encoder implementation.

jeffreysijuntan/lloco