Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization (2412.06858v1)

Abstract: Quantization is a critical step to enable efficient LLM serving under limited resource. However, previous research observes that certain weights in the LLM, known as outliers, are significantly sensitive to quantization noises. Existing quantization methods leave these outliers as floating points or higher precisions to retain performance, posting challenges on the efficient hardware deployment of the mixed-precision model. This work investigates an alternative way to tame the sensitive weights' impact on the quantization error, by reducing the loss Hessian trace with respect to outliers through an efficient fine-tuning process. We propose Noise Perturbation Fine-tuning (NPFT), which identifies outlier weights and add random weight perturbations on the outliers as the model going through a PEFT optimization. NPFT tames the sensitivity of outlier weights so that the quantized model performance can be improved without special treatment to the outliers. When applied to OPT and LLaMA models, our NPFT method achieves stable performance improvements for both uniform and non-uniform quantizers, while also offering better inference efficiency. Notably, the simplest RTN can achieve performance on par with GPTQ using our NPFT on LLaMA2-7B-4bits benchmark.

Analyzing "Taming Sensitive Weights: Noise Perturbation Fine-tuning for Robust LLM Quantization"

Quantization of LLMs is increasingly pivotal for efficient deployment, especially on resource-constrained hardware. The paper "Taming Sensitive Weights: Noise Perturbation Fine-tuning for Robust LLM Quantization" presents a novel approach called Noise Perturbation Fine-tuning (NPFT), which aims to address challenges associated with existing model quantization techniques, particularly those concerning sensitive weights, also known as outliers.

The Problem and Approach

Traditional quantization methods often encounter significant performance degradation due to the sensitivity of certain weights. These methods often address the issue by preserving outliers in higher precision formats, resulting in a mixed-precision model that is suboptimal for hardware efficiency. The central thesis of the paper is the introduction of NPFT, which seeks to mitigate the sensitivity of these outlier weights, allowing them to be uniformly quantized alongside other weights without substantial loss in model performance.

NPFT leverages a parameter-efficient fine-tuning process informed by insights into the loss function's Hessian trace. By introducing random weight perturbations during the fine-tuning stage, NPFT reduces the sensitivity of outlier weights. This approach helps in achieving improvements in the performance of quantized models for both uniform and non-uniform quantizers, demonstrated on models such as OPT and LLaMA.

Results and Comparisons

The empirical evaluations presented in the paper indicate that NPFT yields performance improvements across various quantization methods:

• For the OPT-1.3B-4bits, an NPFT-tuned Round-to-Nearest (RTN) quantizer realized a perplexity improvement of over 2.9 on the C4 benchmark compared to baselines.
• In the case of the LLaMA2-7B-4bits, NPFT enabled the RTN to achieve performance on par with more complex quantization methods like GPTQ, without the need for mixed-precision formats.
• Additional metrics showed that NPFT facilitated a 10% reduction in inference latency for specific hardware benchmarks, underscoring its practical efficiency gains.

Implications and Future Directions

The methodology presented underscores a shift towards more hardware-efficient quantization methods. By alleviating the requirement for preserving outliers at higher precisions, NPFT paves the way for deploying LLMs on a wider array of devices without significant loss in performance. Furthermore, the practice of employing noise perturbation as a fine-tuning mechanism introduces a pathway that could be harnessed to regularize other aspects of model performance, potentially impacting other areas in machine learning where model robustness is a concern.

Looking to the future, the NPFT approach may inspire new paradigms in model compression and optimization, as researchers continue to explore its integration with other state-of-the-art techniques. Future research could focus on expanding the application of NPFT to even larger models or other varieties of neural architectures, examining how such techniques could be generalized or specialized further to capture weight sensitivity beyond statistical perturbation techniques currently explored.

The paper contributes significantly to the dialogue around efficient LLM deployment, offering both theoretical insights and practical advancements. Its implications extend to multiple facets of AI development, promising advancements in both computational efficiency and the broader accessibility of AI technologies.