Enterprise AI Analysis
Intrinsic Structure as a Proxy for Saliency: SVD-Based Weight Preservation for Mixed-Precision Quantization in Large Language Models
This paper introduces a novel, data-free approach to mixed-precision quantization in Large Language Models (LLMs) by leveraging Singular Value Decomposition (SVD). The core hypothesis is that weights identified as Principal Components by SVD are intrinsically important for model performance. By preserving these critical weights in FP32 and aggressively quantizing the rest, the method achieves competitive or superior accuracy compared to data-aware methods like AWQ and SpQR, especially in low-resource settings. This approach eliminates the need for calibration data, crucial for privacy-sensitive or data-unavailable scenarios.
Impact Metrics for Your Enterprise
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Peak Accuracy Achieved on RTE Task
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Overlap with SpQR (Saliency)
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Calibration Data Needed
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Methodology
The paper proposes SVD-based selection to identify crucial weights by reconstructing the 'Principal Structure' of weight matrices. Weights with high magnitude in this reconstruction are preserved in FP32, while others are quantized to 4-bit. This is a data-free approach.
Experimental Setup
Evaluations are performed on GLUE benchmarks (MRPC, RTE, QNLI) using a DistilBERT backbone. Comparisons are made against AWQ (activation-aware) and SpQR (second-order Hessian-based) methods. Protection budgets (k) vary from 1 to 4096 parameters per layer.
Results & Analysis
SVD-based method outperforms AWQ and SpQR on RTE (66.06% vs 65.34%). It is competitive on MRPC and QNLI. A significant overlap (67%) with SpQR's selected weights suggests SVD captures Hessian-like sensitivity without data. The method is computationally efficient, requiring no forward passes or calibration data.
66.06%
Peak Accuracy Achieved on RTE Task (outperforming data-aware methods)
Enterprise Process Flow
Pre-trained Weight Matrix W
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Singular Value Decomposition (SVD)
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Reconstruct Principal Structure W_pri (Top R Singular Values)
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Identify Top-k Weights from |W_pri|
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Preserve Top-k in FP32, Quantize Residual (4-bit)
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Quantized LLM for Deployment
| Feature | Data-Aware Methods (AWQ/SpQR) | SVD-Based (Our Method) |
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| Calibration Data |
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| Computational Cost |
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| Saliency Detection |
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| Privacy Concerns |
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Impact on Edge Device Deployment for RTE Task
On the challenging RTE task, our SVD-based method achieved an accuracy of 66.06%, surpassing both AWQ and SpQR (65.34%). This is crucial for resource-constrained edge devices or private deployments where calibration data is unavailable. The SVD method's ability to identify intrinsic structure without data makes it a robust solution for deploying high-performing LLMs in sensitive environments, proving that structural importance can be a reliable proxy for functional importance.
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