Regularized Calibration with Successive Rounding for Post-Training Quantization
Executive Summary
This paper introduces a novel Post-Training Quantization (PTQ) framework called Regularized Calibration with Successive Rounding. It addresses the challenges of deploying Large Language Models (LLMs) by reducing memory and latency costs without retraining. The core innovation lies in an interpolated calibration objective that regularizes between symmetric and asymmetric approaches, providing robustness to activation mismatch. This objective enables an efficient successive rounding procedure, including a search-enhanced variant (K-SNRQ), which improves quantization quality with controlled computational cost. Experimental results across multiple LLM families demonstrate consistent improvements in perplexity and accuracy over existing PTQ baselines.
Key Performance Metrics
Our analysis highlights the following key improvements achieved through Regularized Calibration with Successive Rounding for Post-Training Quantization:
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Perplexity Reduction (Wiki2)
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Perplexity Reduction (C4)
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Zero-shot Accuracy Improvement
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Quantization Speedup
Deep Analysis & Enterprise Applications
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The proposed method introduces an interpolated calibration objective, blending symmetric and asymmetric approaches. This acts as a regularization mechanism, preserving the quadratic structure crucial for efficient optimization while offering robustness to activation mismatches. This objective is governed by a single parameter, alpha, which controls the strength of asymmetry and significantly influences performance.
Building on the regularized objective, a simple successive rounding procedure is derived. This procedure efficiently solves the discrete optimization problem. It includes a greedy rounding algorithm (SNRQ) and a more advanced bounded-search extension (K-SNRQ) that explores multiple partial assignments. K-SNRQ allows for an explicit trade-off between quantization quality and computational cost.
Extensive experiments across multiple LLM families (LLaMA 2/3, Qwen3-8B, Phi-3 Mini), various quantization bit-widths (3-bit, 4-bit), and diverse benchmarks (WikiText2, C4, commonsense reasoning tasks) demonstrate the effectiveness of the proposed approach. It consistently improves perplexity and accuracy over PTQ baselines like GPTQ and GPTAQ, with modest computational overhead.
5.7%
Wiki2 Perplexity Reduction on LLaMA3-8B (3-bit) vs. GPTAQ
Enterprise Process Flow
Full-Precision (FP) Model
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Quantizing Layer (l-1)
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Quantized Layer (l-1)
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Regularized Activation X_alpha
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Quantizing Layer (l)
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Quantized Layer (l)
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| Avg. Zero-shot Accuracy (↑) |
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LLM Deployment Optimization
Client: Large Enterprise AI Division
Challenge: A major challenge for enterprise AI divisions is deploying large language models (LLMs) in production due to their substantial memory footprint and high inference latency. Traditional post-training quantization (PTQ) methods often compromise accuracy for efficiency, limiting real-world applicability.
Solution: Implemented Regularized Calibration with Successive Rounding (K-SNRQ) to compress LLMs without retraining. The framework’s adaptive calibration objective and search-enhanced rounding preserved model accuracy while significantly reducing model size and improving inference speed.
Results: Achieved up to 5.7% reduction in perplexity and 5.4% improvement in zero-shot accuracy compared to baselines, alongside a 32.2% faster quantization process. This enabled the deployment of higher-quality LLMs on commodity hardware, expanding their application scope within the enterprise.
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Strategic Implementation Roadmap
Our phased approach ensures a smooth integration and optimal performance of advanced quantization techniques within your enterprise AI systems.
01. Initial Model Assessment
Evaluate current LLM performance, identify critical modules for quantization, and establish baseline metrics for memory, latency, and accuracy.
02. Calibration Data Selection
Curate a representative and diverse calibration dataset to ensure robust quantization. Analyze activation distributions to inform initial `alpha` parameter settings.
03. PTQ Implementation (SNRQ/K-SNRQ)
Apply the Regularized Calibration with Successive Rounding framework, iteratively quantizing layers. Utilize K-SNRQ with a bounded search to fine-tune quantization quality versus compute cost.
04. Post-Quantization Validation
Perform comprehensive validation on held-out datasets and downstream tasks to verify perplexity and accuracy, ensuring no significant performance degradation.
05. Deployment & Monitoring
Deploy the quantized LLMs to production environments. Continuously monitor performance and resource utilization to ensure sustained efficiency and accuracy.
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