LongCodeZip
This repository is the official implementation of LongCodeZip, a novel two-stage long code compression method.
Method Overview
LongCodeZip introduces a two-stage code compression framework specifically designed for code LLMs:
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Coarse-grained Compression: Function-based chunking and ranking using conditional perplexity with respect to the query to select the most relevant functions.
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Fine-grained Compression: Entropy-based block detection combined with 0/1 knapsack optimization to maximize relevance within adaptive token budgets.
The method is plug-and-play and can be integrated with existing code LLMs to achieve significant compression ratios while maintaining or improving task performance.
Repository Structure
This repository contains implementations and experiments for three code-related tasks:
LongCodeZip/
├── repo-qa/ # Code Retrieval Task
│ ├── main.py # Main evaluation script
│ ├── run.sh # Experiment runner
│ ├── code_compressor.py # Core compression implementation
│ ├── compute_score.py # Evaluation metrics
│ └── ...
├── long-code-completion/ # Code Completion Task
│ ├── main.py # Main evaluation script
│ ├── run.sh # Experiment runner
│ ├── code_compressor.py # Core compression implementation
│ ├── utils.py # Utility functions
│ └── ...
├── module-summarization/ # Code Summarization Task
│ ├── main.py # Main evaluation script
│ ├── run.sh # Experiment runner
│ ├── code_compressor.py # Core compression implementation
│ ├── utils.py # Utility functions
│ └── ...
└── README.md
Installation
pip install -r requirements.txt
Usage
Quick Start
Each task directory contains a run.sh script for easy experimentation. Simply navigate to the desired task directory and run:
cd <task_directory>
bash run.sh
Code Retrieval (RepoQA)
Navigate to the repo-qa directory and run experiments with different compression ratios:
cd repo-qa
bash run.sh
The script will evaluate LongCodeZip on the RepoQA dataset with compression ratios of 0.1, 0.2, 0.3, and 0.4, running experiments in parallel on multiple GPUs.
Key Parameters:
--compression-ratio: Controls the compression level (0.1-0.4)--model: Specifies the base LLM model--backend: Backend for model inference (vllm)
Code Completion
Navigate to the long-code-completion directory:
cd long-code-completion
bash run.sh
This evaluates LongCodeZip on long-context code completion tasks with various configurations including different target token limits, fine-grained compression ratios, and importance beta values.
Key Parameters:
--code_compressor_target_token: Target token budget (2048, 4096)--code_compressor_fine_ratio: Fine-grained compression ratio (0.5, 0.8)--importance_beta: Importance weighting parameter (0.0, 0.5)
Code Summarization
Navigate to the module-summarization directory:
cd module-summarization
bash run.sh
This runs code summarization experiments with fine-grained compression and various beta values for importance weighting.
Key Parameters:
--code_compressor_target_token: Target token budget--code_compressor_fine_ratio: Fine-grained compression ratio--importance_beta: Importance weighting parameter
Configuration
Each task can be customized by modifying the respective run.sh file or by directly calling the main scripts with custom parameters. Key configuration options include:
- Model Selection: Compatible with various code LLMs (default: Qwen2.5-Coder-7B-Instruct)
- Compression Ratios: Adjustable compression levels for different use cases
- Token Budgets: Configurable target token limits
- GPU Configuration: Multi-GPU support for parallel experiments
Performance
LongCodeZip achieves up to 5.6× compression ratio without sacrificing task performance across code completion, summarization, and retrieval tasks. And even when using a 0.5B Qwen model as the compressor, it can also achieve competitive performance.
Contact
Please feel free to contact us if you have any questions.