CCF: A Context Compression Framework

Experiment setup

conda env create -f environment.yaml
conda activate ccf

Our repository requires flash attention and PyTorch dependencies, which are related to the local environment, and need to be installed manually with suitable versions according to your environment.

Download Dataset

cd ccf

wget https://huggingface.co/datasets/namespace-Pt/projects/resolve/main/activation-beacon.tar.gz?download=true -O ./raw_data/activation-beacon.tar.gz

cd raw_data
tar -xzvf activation-beacon.tar.gz
cp activation-beacon/pretrain/redpajama-sample.json .
cp activation-beacon/finetune/longalpaca.json .

Pretrain

cd ccf
python train.py --env_conf 32x.json

Illustration on 32x.json Config

• You can modify the device_map field in the 32x.json file to change the GPU used for model loading. By assigning different GPUs to different modules, you can achieve pipeline parallelism.
• You can adjust the config/32x.json configuration file to change the parameters of LoRA fine-tuning, such as chunk size and compression ratio, etc.
• You can modify the corpus field to configure your desired dataset. All supported datasets and their specific configuration methods can be found in the src/data.py file. Use the truncation field to configure the maximum token count for each dataset, and partition to set the instance ratio for the dataset.
• You can modify the save_ckp field to set the save path for checkpoints.

Benchmark

Language Modeling

cd ccf
python test.py --env_conf 32x.json

As long as a checkpoint is generated during the training process (controlled by the save_ckp and save fields), you can use the above command to perform evaluation.

The test.py script will automatically search for a test.json file in the working directory, which is used to configure the dataset to be evaluated. Each instance in the file has the following format:

{
    "task_type": "perplexity",
    "task_name": "pg19.test.128k", 
    "num_instance": 100,
    "truncation": 99382
}