efficient-pruners 0.1.0

Calibration-Free Model Compression with Reinforcement Learning-Based Policy Learning

PruneNet: Calibration-Free Model Compression with Policy Learning

This repository contains PruneNet, a novel model compression framework that uses reinforcement learning to compress large language models without requiring calibration data.

Based on the paper: You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning

✨ Key Features

• 🎯 No Calibration Data Required - Learns compression policy directly from model weights
• 🤖 Reinforcement Learning-Based - Learns optimal neuron selection strategy
• 📊 Preserves Spectral Properties - Maintains weight matrix characteristics
• 🚀 Easy to Use - Simple fit() and compress() API following scikit-learn patterns
• 🔧 Flexible Configuration - Extensive hyperparameter control
• 📦 Multiple Architectures - Supports OPT, Llama, Phi, Falcon

🚀 Quick Start

Installation

git clone https://github.com/parmanu-lcs2/efficient_pruners
cd efficient_pruners
pip install -e .

Basic Usage (New API)

from efficient_pruners import PruneNet, PruningConfig

# Configure hyperparameters
config = PruningConfig(
    num_episodes=20,
    learning_rate=0.001
)

# Initialize pruner
pruner = PruneNet(config)

# Train policy on specific model with target compression ratio
pruner.fit(model_name="facebook/opt-125m", compression_ratio=0.3)

# Compress with the same or different ratio
compressed_model = pruner.compress(compression_ratio=0.3)

# Save compressed model
compressed_model.save_pretrained("./compressed_model")

# Test text generation with compressed LLM
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
inputs = tokenizer("The future of AI is", return_tensors="pt")

# Generate text with compressed model
outputs = compressed_model.generate(**inputs, max_length=50)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(text)

Legacy CLI Usage

The original command-line interface is still available in the prunenet/ directory:

python3 -m prunenet \
    --model_name facebook/opt-125m \
    --compression_ratio 0.3 \
    --save_dir ./models/ \
    --device cuda:0

📖 Documentation

• API Guide - Complete API reference
• Test Notebook - Interactive fit/compress test with visualizations
• Test Script - Automated fit/compress test

📂 Project Structure

PruneNet/
├── src/efficient_pruners/     # Main package
│   ├── core.py                # PruneNet class (fit/compress API)
│   ├── config.py              # PruningConfig dataclass
│   ├── models/                # SparsityPredictor policy network
│   │   └── sparsity_predictor.py
│   └── utils/                 # Model and reward utilities
│       ├── model_utils.py
│       └── reward_utils.py
├── examples/                  # Test & usage examples
│   └── test_fit_compress.py   # Complete fit/compress test script
├── notebooks/                 # Interactive tutorials
│   └── test_fit_compress.ipynb  # Complete fit/compress test notebook
├── docs/                      # Documentation
│   └── API_GUIDE.md
├── prunenet/                  # Original CLI implementation
├── setup.py                   # Package setup
├── pyproject.toml             # Modern build system
└── requirements.txt           # Dependencies

🎯 Supported Models

• OPT: facebook/opt-125m, facebook/opt-1.3b, etc.
• Llama: meta-llama/Llama-2-7b-hf, etc.
• Phi: microsoft/phi-1, microsoft/phi-2, etc.
• Falcon: tiiuae/falcon-7b, etc.

🧪 Running Examples

Test Script

Run the comprehensive test to verify both fit() and compress() methods:

python examples/test_fit_compress.py

This script will:

• ✅ Train an RL policy using fit()
• ✅ Compress the model using compress()
• ✅ Test .generate() on the compressed LLM
• ✅ Compare outputs between original and compressed models
• ✅ Display compression statistics

Interactive Notebook

jupyter notebook notebooks/test_fit_compress.ipynb

The notebook includes:

• Step-by-step walkthrough of fit() and compress()
• Visualizations of training progress
• Interactive text generation testing with compressed model
• Side-by-side comparison of model outputs

⚙️ Advanced Configuration

config = PruningConfig(
    num_episodes=20,
    learning_rate=0.001,
    use_kld=True,          # Enable KL divergence regularization
    gamma=0.99,            # Reward discount factor
    device="auto",         # Auto-detect GPU/CPU
    save_dir="./outputs"   # Checkpoint directory
)

pruner = PruneNet(config)
pruner.fit(model_name="facebook/opt-125m")
compressed_model = pruner.compress(compression_ratio=0.3)

See API_GUIDE.md for all configuration options.

📊 Performance

Typical compression results on OPT-125M:

Compression	Size Reduction	Perplexity Impact
20%	~15%	+2-3%
30%	~22%	+3-5%
40%	~30%	+5-8%
50%	~37%	+8-12%

🔬 Research & Original Implementation

The original research scripts are preserved in prunenet/ and experiments/ directories. See the original README sections below for research-specific details.

---

Original Evaluation Scripts

Slicing the attention modules

Citation

If you find our work useful in your projects/research, kindly cite our paper:

@inproceedings{
    sengupta2025you,
    title={You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning},
    author={Ayan Sengupta and Siddhant Chaudhary and Tanmoy Chakraborty},
    booktitle={The Thirteenth International Conference on Learning Representations},
    year={2025},
    url={https://openreview.net/forum?id=5RZoYIT3u6}
}