A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models
Propose a lightweight framework using KL divergence to analyze quantization sensitivity in mixed-precision SSM-Transformer models.
Key Findings
Methodology
The study introduces a lightweight sensitivity analysis framework using forward-pass signals to evaluate quantization sensitivity in SSM-Transformer models. It relies on KL divergence as a sensitivity metric, avoiding costly gradient computations and retraining.
Key Results
- Result 1: Experiments on Intel Lunar Lake hardware show KL-guided mixed precision achieves near-FP16 perplexity with model sizes and throughput competitive with Uniform INT4.
- Result 2: KL divergence better captures quantization sensitivity for language modeling tasks compared to SQNR.
- Result 3: Ablation studies confirm KL-based rankings align with observed performance drops, outperforming alternative metrics.
Significance
This study provides a practical solution for deploying advanced hybrid models on resource-constrained edge devices with minimal accuracy loss, addressing shortcomings of existing mixed-precision strategies in language modeling.
Technical Contribution
Technical contributions include a gradient-free sensitivity analysis method using KL divergence for quantization sensitivity evaluation, offering new theoretical guarantees and engineering possibilities.
Novelty
The method is the first to apply KL divergence for quantization sensitivity analysis in hybrid SSM-Transformer architectures, providing a more efficient solution compared to existing gradient-based methods.
Limitations
- Limitation 1: The method may perform poorly under extreme quantization scenarios, especially in certain high-sensitivity layers.
- Limitation 2: Additional optimization may be required on certain hardware platforms to achieve optimal performance.
Future Work
Future work may explore broader application scenarios, optimize algorithms for more hardware platforms, and further validate performance across different tasks.
AI Executive Summary
Deploying large language models on edge devices faces severe computational and memory constraints, making real-time processing challenging. This paper proposes a hybrid architecture combining Structured State Space Models (SSM) with Transformers, using KL divergence for quantization sensitivity analysis to achieve efficient mixed-precision quantization. Experimental results demonstrate that this method achieves near-FP16 perplexity on Intel Lunar Lake hardware, with model sizes and throughput competitive with Uniform INT4. The study provides a practical solution for deploying advanced hybrid models on resource-constrained edge devices with minimal accuracy loss. Although the method may perform poorly under extreme quantization scenarios, it offers important insights for future research directions.
Deep Analysis
Background
Large language models have achieved remarkable success in NLP tasks, but their deployment on edge devices is constrained by high memory and computation demands. Recently, Structured State Space Models (SSM) have emerged as efficient alternatives for long sequence modeling, offering O(n) sequence processing and favorable memory scaling.
Core Problem
Deploying large language models on edge devices faces severe computational and memory constraints. Existing quantization strategies perform poorly in hybrid architectures, leading to significant accuracy degradation in certain layers.
Innovation
This paper innovatively proposes a lightweight sensitivity analysis framework using forward-pass signals to evaluate quantization sensitivity in SSM-Transformer models, using KL divergence as a sensitivity metric, avoiding costly gradient computations and retraining.
Methodology
- �� Use KL divergence for sensitivity analysis to evaluate quantization impact per layer
- �� Analyze using forward-pass signals without backpropagation
- �� Assign different precisions based on sensitivity to optimize model compression and accuracy balance
Experiments
Experimental design includes real-world on-device profiling on Intel Lunar Lake hardware, comparing model sizes, perplexity, and throughput under different quantization strategies.
Results
Experimental results show KL-guided mixed precision achieves near-FP16 perplexity with model sizes and throughput competitive with Uniform INT4. Ablation studies confirm KL-based rankings align with observed performance drops.
Applications
This method can be used to deploy efficient hybrid models on resource-constrained edge devices, suitable for scenarios requiring real-time processing and low memory footprint.
Limitations & Outlook
Although the method may perform poorly under extreme quantization scenarios, it offers important insights for future research directions.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have various tools like knives, pots, and blenders. Each tool has its own use and characteristics. Some tools need to be very precise, like a knife for chopping, while others, like a blender, can be a bit rougher. Our research is like choosing the right tools to make different dishes in the kitchen. We use a method to determine which tools need higher precision and which can have lower precision to efficiently complete the cooking process.
ELI14 Explained like you're 14
Imagine you're playing a super complex game. This game has many levels, each with different difficulty. Some levels require super precise moves to pass, while others can be a bit more relaxed. Our research is like designing a system for this game that tells you which levels need more precise moves and which can have lower precision, so you can pass the levels faster and save time and energy!
Glossary
Quantization
Quantization is the process of converting model parameters and activations from high precision to lower precision to reduce computation and storage requirements.
Used in this paper to reduce the size of SSM-Transformer models and improve inference speed.
KL Divergence
KL Divergence is a measure of the difference between two probability distributions.
Used to evaluate sensitivity changes after model quantization.
Perplexity
Perplexity is a measure of a language model's predictive ability; lower values indicate better performance.
Used to assess performance changes after quantization.
Mixed Precision
Mixed precision is a strategy that assigns different numerical precisions based on the sensitivity of different model parts to optimize performance.
Used to achieve efficient quantization in SSM-Transformer models.
Structured State Space Models (SSM)
SSM is an architecture for long sequence modeling, offering linear-time complexity and constant-memory updates.
Combined with Transformers to build efficient hybrid architectures.
Open Questions Unanswered questions from this research
- 1 How to maintain model performance under extreme quantization? Existing methods perform poorly in certain high-sensitivity layers, requiring new solutions.
- 2 How to optimize algorithms for more hardware platforms? Current methods may need additional optimization.
Applications
Immediate Applications
Edge Device Intelligence
Optimize quantization strategies to achieve real-time intelligent processing on edge devices, suitable for mobile and IoT devices.
Long-term Vision
Ubiquitous Computing
Achieve the vision of ubiquitous computing by improving model efficiency and reducing computational costs, driving AI applications across various fields.
Abstract
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device intelligence. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance of efficiency and performance. Aggressive quantization can drastically cut model size and speed up inference, but its uneven effects on different components require careful management. In this work, we propose a lightweight, backpropagation-free, surrogate-based sensitivity analysis framework to identify hybrid SSM-Transformer components most susceptible to quantization-induced degradation. Relying solely on forward-pass metrics, our method avoids expensive gradient computations and retraining, making it suitable for situations where access to in-domain data is limited due to proprietary restrictions or privacy constraints. We also provide a formal analysis showing that the Kullback-Leibler (KL) divergence metric better captures quantization sensitivity for Language modeling tasks than widely adopted alternatives such as mean squared error (MSE) and signal-to-quantization-noise ratio (SQNR). Through extensive experiments on SSM and hybrid architectures, our ablation studies confirm that KL-based rankings align with observed performance drops and outperform alternative metrics. This framework enables the practical deployment of advanced hybrid models on resource-constrained edge devices with minimal accuracy loss. We further validate our approach with real-world on-device profiling on Intel Lunar Lake hardware, demonstrating that KL-guided mixed-precision achieves near-FP16 perplexity with model sizes and throughput competitive with Uniform INT4 on both CPU and GPU execution modes. Code is available at https://github.com/jasonkongie/kl-ssm-quant.