SVD-LLM: Singular Value Decomposition for Large Language Model Compression

Introduction

SVD-LLM: Singular Value Decomposition for Large Language Model Compression [arXiv]
Xin Wang¹, Yu Zheng², Zhongwei Wan¹, Mi Zhang^1
1The Ohio State University, ²Michigan State University

Key Designs

• Truncation-Aware Data Whitening: Ensure truncating smaller singular values has lower compression loss.
• Layer-Wise Closed-Form Update: Compensate for accuracy degradation under high compression ratio.

Abstract

The advancements in Large Language Models (LLMs) have been hindered by their substantial sizes, which necessitate LLM compression methods for practical deployment. Singular Value Decomposition (SVD) offers a promising solution for LLM compression. However, state-of-the-art SVD-based LLM compression methods have two key limitations: truncating smaller singular values may lead to higher compression loss, and the lack of update on the compressed weight after SVD truncation. In this work, we propose SVD-LLM, a new SVD-based LLM compression method that addresses the limitations of existing methods. SVD-LLM incorporates a truncation-aware data whitening strategy to ensure a direct mapping between singular values and compression loss. Moreover, SVD-LLM adopts a layer-wise closed-form model parameter update strategy to compensate for accuracy degradation under high compression ratios. We evaluate SVD-LLM on a total of 10 datasets and eight models from three different LLM families at four different scales. Our results demonstrate the superiority of SVD-LLM over state-of-the-arts, especially at high model compression ratios.

Quick Start

Installation

Please keep the version of the transformers package exactly equal to 4.35.2 since the svd-compressed version of LLM has a slight change of model structure (in the component/. folder).

pip install -r requirement.txt

Quick Example

bash compress_llama.sh

This script would compress the LLaMA-7B model under 20% compression ratio and automatically run the evaluation code, including both perplexity and efficiency of the compressed model.

Step-by-Step Instructions

We implement SVD-LLM with two different pipelines:

• Truncation-Aware Data Whitening + SVD Compression (used under low compression ratio)
• Truncation-Aware Data Whitening + SVD Compression + Layer-Wise Closed-Form Update (used under high compression ratio)

1. Truncation-Aware Data Whitening + SVD Compression (Used under low compression ratio)

Under the low compression ratio (recommended ratio <= 0.3), we first run the data whitening of the LLM and saved the weight along with the whitening information.

python SVDLLM.py \
--step 1  \
--ratio COMPRESSION_RATIO \
--model HUGGINGFACE_MODEL_REPO \
--whitening_nsamples WHITENING_SAMPLE_NUMBER \
--dataset WHITENING_DATASET \
--seed SAMPLING_SEED \
--model_seq_len MODEL_SEQ_LEN \
--save_path WHITENING_INFO_SAVING_PATH

To compress LLM with larger size, or to run the compression under the resource-constraint platform, we can add --run_low_resource to the command.

2. Truncation-Aware Data Whitening + SVD Compression + Layer-Wise Closed-Form Update (Used under high compression ratio)

Under the high compression ratio (recommended ratio > 0.3), we can further apply layer-wise closed-form update to update the weight matrix after the first pipeline to improve accuracy.

python SVDLLM.py \
--step 2  \
--ratio COMPRESSION_RATIO \
--model HUGGINGFACE_MODEL_REPO \
--whitening_nsamples WHITENING_SAMPLE_NUMBER \
--updating_nsamples UPDATING_SAMPLE_NUMBER \
--dataset WHITENING_DATASET \
--seed SAMPLING_SEED \
--model_seq_len MODEL_SEQ_LEN \
--save_path WHITENING_INFO_SAVING_PATH

3. SVD Compression + Layer-Wise Closed-Form Update (Although not the best but still better than exsiting baselines)

We also provide the implementation to run layer-wise closed-form update only in SVD-LLM. Although this version is not as good as the above two versions of SVD-LLM, it is still better than the existing baselines.

python SVDLLM.py \
--step 3  \
--ratio COMPRESSION_RATIO \
--model HUGGINGFACE_MODEL_REPO \
--updating_nsamples UPDATING_SAMPLE_NUMBER \
--dataset WHITENING_DATASET \
--seed SAMPLING_SEED \
--model_seq_len MODEL_SEQ_LEN \
--save_path WHITENING_INFO_SAVING_PATH

4. LoRA Fine-Tuning

The compressed model from either of the two pipelines above can also be combined with LoRA fine-tuning to get a better accuracy. We borrowed the LoRA fine-tuning code from LLM-Pruner with the same configuration.

python LoRA.py \
--prune_model COMPRESSED_MODEL_PATH \
--data_path yahma/alpaca-cleaned \
--output_dir LORA_OUTPUT_PATH  \
--lora_r 8 \
--num_epochs 2 \
--learning_rate 1e-4 \
--batch_size 64

5. SVD-LLM + GPTQ

SVD-LLM can also be integrated with quantization methods to achieve a better compression. Here is the example of how to integrate SVD-LLM (20% compression ratio) with GPTQ-4bit to compress LLaMA-7B

bash svdllm_gptq.sh

6. Evaluation

• Perplexity Evaluation:

python SVDLLM.py \
--step 4 \
--model_path COMPRESSD_MODEL_SAVING_PATH  \

We use the same c4 dataset as in SparseGPT. Since the original dowload link is invalid, please directly download it from this link and add the two json files under the utils/. folder.

• Efficiency Evaluation:

python SVDLLM.py \
--step 5 \
--model_path COMPRESSD_MODEL_SAVING_PATH  \

Citation

If you find this work useful, please cite

@article{wang2024svd,
  title={SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression},
  author={Wang, Xin and Zheng, Yu and Wan, Zhongwei and Zhang, Mi},
  journal={arXiv preprint arXiv:2403.07378},
  year={2024}
}