Beyond Outliers: A Data-Free Layer-wise Mixed-Precision Quantization Approach Driven by Numerical and Structural Dual-Sensitivity
arXiv:2603.17354v1 Announce Type: new Abstract: Layer-wise mixed-precision quantization (LMPQ) enables effective compression under extreme low-bit settings by allocating higher precision to sensitive layers. However, existing methods typically treat all intra-layer weight modules uniformly and rely on a single numerical property when estimating sensitivity, overlooking their distinct operational roles and structural characteristics. To address this, we propose NSDS, a novel calibration-free LMPQ framework driven by Numerical and Structural Dual-Sensitivity. Specifically, it first mechanistically decomposes each layer into distinct operational roles and quantifies their sensitivity from both numerical and structural perspectives. These dual-aspect scores are then aggregated into a unified layer-wise metric through a robust aggregation scheme based on MAD-Sigmoid and Soft-OR to guide bit allocation. Extensive experiments demonstrate that NSDS consistently achieves superior performance c
arXiv:2603.17354v1 Announce Type: new Abstract: Layer-wise mixed-precision quantization (LMPQ) enables effective compression under extreme low-bit settings by allocating higher precision to sensitive layers. However, existing methods typically treat all intra-layer weight modules uniformly and rely on a single numerical property when estimating sensitivity, overlooking their distinct operational roles and structural characteristics. To address this, we propose NSDS, a novel calibration-free LMPQ framework driven by Numerical and Structural Dual-Sensitivity. Specifically, it first mechanistically decomposes each layer into distinct operational roles and quantifies their sensitivity from both numerical and structural perspectives. These dual-aspect scores are then aggregated into a unified layer-wise metric through a robust aggregation scheme based on MAD-Sigmoid and Soft-OR to guide bit allocation. Extensive experiments demonstrate that NSDS consistently achieves superior performance compared to various baselines across diverse models and downstream tasks, without relying on any calibration data.
Executive Summary
This article proposes a novel layer-wise mixed-precision quantization framework driven by Numerical and Structural Dual-Sensitivity (NSDS). NSDS decomposes layers into distinct operational roles, quantifying sensitivity from both numerical and structural perspectives. The dual-aspect scores are aggregated into a unified metric using MAD-Sigmoid and Soft-OR, guiding bit allocation without calibration data. Extensive experiments demonstrate superior performance across diverse models and tasks. NSDS's calibration-free approach and dual-sensitivity metrics offer significant advancements in mixed-precision quantization, particularly in low-bit settings. The framework's robustness and adaptability make it a promising solution for efficient neural network deployment.
Key Points
- ▸ NSDS introduces a novel dual-sensitivity approach for mixed-precision quantization
- ▸ The framework decomposes layers into distinct operational roles and quantifies sensitivity from numerical and structural perspectives
- ▸ NSDS achieves superior performance across diverse models and tasks without calibration data
Merits
Strength in Addressing Complexity
NSDS effectively tackles the complexity of mixed-precision quantization by considering both numerical and structural sensitivity, providing a more comprehensive understanding of layer behavior.
Robustness and Adaptability
The framework's use of MAD-Sigmoid and Soft-OR aggregation schemes enhances its robustness and adaptability, allowing it to handle a wide range of models and tasks.
Demerits
Scalability and Computational Costs
The decomposition process and dual-sensitivity calculations may introduce additional computational overhead, potentially limiting NSDS's scalability in large-scale applications.
Expert Commentary
The article presents a significant contribution to the field of mixed-precision quantization, offering a novel and robust framework that addresses the complexities of layer-wise sensitivity. The use of dual-sensitivity metrics and aggregation schemes enhances the framework's adaptability and scalability. However, the computational overhead associated with the decomposition process and dual-sensitivity calculations may limit the framework's scalability in large-scale applications. Future research should focus on optimizing these aspects while maintaining the framework's robustness and accuracy.
Recommendations
- ✓ Further research should explore the application of NSDS in large-scale deployment scenarios, addressing the potential scalability limitations.
- ✓ The development of optimized decomposition and dual-sensitivity calculation techniques could enhance the framework's efficiency and adaptability.
