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KLAWQ: KL-Aware Weight Quantization for Edge AI

A novel post-training quantization framework that enhances GPTQ by integrating KL divergence for better accuracy preservation when deploying Large Language Models on edge devices. 1

Core Innovation

KLAWQ extends GPTQ by adding a KL divergence term to align quantized model outputs with the original model's distribution: 2

L(Q) = L_MSE(Q) + β * L_KL(Q)

The algorithm modifies the Hessian computation as H_tot = H + βA, where A is the KL Hessian matrix. 3

Key Components

  • Configuration: Hyperparameters (β, τ) in KLAWQ/gptqmodel/quantization/config.py 4
  • Core Algorithm: KL Hessian computation in KLAWQ/kl-aware-quant/quantization/gptq.py 5
  • Quantization Engine: Low-level operations in KLAWQ/kl-aware-quant/quantization/quantizer.py 6
  • Analysis Notebooks: Experimental validation in kl-hessian-gptq-*.ipynb files 7

Quick Start

  1. Clone and Setup: 8

    git clone https://github.com/ha405/Compression-Framework-for-EdgeAI
cd Compression-Framework-for-EdgeAI

  2. Install Dependencies: Install PyTorch, transformers, and other requirements from requirements.txt

  3. Run Quantization: Use the Jupyter notebooks for experimentation or integrate the KLAWQ modules directly

Results

Experiments on GPT-2 at 8-bit precision demonstrate improved perplexity scores compared to vanilla GPTQ while maintaining post-training quantization efficiency. 9

Notes

The framework is built on a comprehensive infrastructure stack including PyTorch >=2.4.1, transformers >=4.51.2, and FastAPI for model serving. The project structure shows a modular design with separate components for adapter functionality, model definitions, and processing loops, though the core KLAWQ innovation is concentrated in the quantization modules. 10

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