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DeltaZip

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DeltaZip is a system for compressing and serving full-parameter fine-tuned LLMs.

Abstract

Fine-tuning large language models (LLMs) for downstream tasks can greatly improve model quality, however serving many different fine-tuned LLMs concurrently for users in multi-tenant environments is challenging. Dedicating GPU memory for each model is prohibitively expensive and naively swapping large model weights in and out of GPU memory is slow. Our key insight is that fine-tuned models can be quickly swapped in and out of GPU memory by extracting and compressing the delta between each model and its pre-trained base model.

We propose DeltaZip, an LLM serving system that efficiently serves multiple full-parameter fine-tuned models concurrently by aggressively compressing model deltas by a factor of 6x to 8x while maintaining high model quality. DeltaZip increases serving throughput by 1.5x to 3x and improves SLO attainment compared to a vanilla HuggingFace serving system.

Quick Start

Installation

We strongly recommend using Docker to install deltazip, with the following command:

docker pull xzyaoi/deltazip:0.0.1

Deltazip requires NVIDIA GPU to run. Please configure your docker environment according to the official document and stackoverflow.

The following examples assume that you have installed deltazip in this way.

Example: Compressing Vicuna-7B

We first compress the delta between Vicuna-7B and its pre-trained counterpart (meta-llama/Llama-2-7b-hf) to 2 bits + 50% sparsity:

First create a directory to store the cache files:

mkdir .cache

You will need >= 24GB GPU memory to run the following command:

docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -e PYTHONPATH=/app \
-e HUGGING_FACE_HUB_TOKEN={PLACE_YOUR_TOKEN_HERE} \
-e HF_HOME=/hf_home \
-it -v $PWD/.cache:/cache -v ~/.cache/huggingface:/hf_home \
xzyaoi/deltazip:0.0.1 \
python cli/compress.py --target-model lmsys/vicuna-7b-v1.5 --outdir /cache/compressed_vicuna --dataset /cache/lmsys.jsonl --n-samples 256 --bits 2 --sparsity 0.5 --lossless gdeflate --delta subtract --base-model meta-llama/Llama-2-7b-hf --shuffle-dataset --fast-tokenizer --perc-damp 0.01 --block-size 128

Then we can inspect the size post-compression:

ls -alh .cache/compressed_vicuna/

It should be around 1.4GB, so 10x compression compared with fp16, not too bad. Let's see the model quality then. We can evaluate the quality of the compressed model:

docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -e PYTHONPATH=/app \
-e HUGGING_FACE_HUB_TOKEN=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 \
-e HF_HOME=/hf_home \
-it -v $PWD/.cache:/cache -v ~/.cache/huggingface:/hf_home \
xzyaoi/deltazip:0.0.1 \
python cli/chat.py --base-model meta-llama/Llama-2-7b-hf --model-path /cache/compressed_vicuna

This command starts an interactive chatbot session. You can type in any text and see the response from the model. Example output is shown below:

[deltazip] Loading base model...
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.12s/it]
[deltazip] Loading target model...
2023-12-13 02:07:35.541 | INFO     | deltazip.modeling._base:from_compressed:740 - compress config: BaseCompressionConfig(bits=2, sparsity=0.5, prunen=0, prunem=0, group_size=-1, group_rows=-1, block_size=128, damp_percent=0.01, desc_act=True, sym=False, true_sequential=True, lossless='gdeflate', dtype='fp16')
2023-12-13 02:07:35.571 | INFO     | deltazip.modeling._base:from_compressed:786 - lm_head not been quantized, will be ignored when make_quant.
2023-12-13 02:07:38.796 | INFO     | deltazip.modeling._utils:unpack_model:136 - Unpacking model...
2023-12-13 02:07:54.914 | INFO     | deltazip.modeling._utils:unpack_model:140 - Model unpacked.
[deltazip] models loaded
User: What can we do in AI research to address climate change?
Assistant:  There are several ways that AI research can be applied to address climate change. The first is through developing models that can predict the impact of climate change on different ecosystems and species. This can help inform conservation efforts and help protect endangered species.

Another area where AI can be useful is in developing renewable energy sources. For example, AI can be used to optimize the design of solar panels or wind turbines to maximize their efficiency.

AI can also be used to improve the efficiency of transportation and logistics, which can reduce greenhouse gas emissions. For example...

Acknowledgements

Heavily inspired by

Citation

If you found this code useful, please cite our paper:

@article{yao2023deltazip,
  title={DeltaZip: Multi-Tenant Language Model Serving via Delta Compression},
  author={Yao, Xiaozhe and Klimovic, Ana},
  journal={arXiv preprint arXiv:2312.05215},
  year={2023}
}

@inproceedings{
    isik2023gptzip,
    title={{GPT}-Zip: Deep Compression of Finetuned Large Language Models},
    author={Berivan Isik and Hermann Kumbong and Wanyi Ning and Xiaozhe Yao and Sanmi Koyejo and Ce Zhang},
    booktitle={Workshop on Efficient Systems for Foundation Models @ ICML2023},
    year={2023},
    url={https://openreview.net/forum?id=hO0c2tG2xL}
}

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