LLM Post-Training Quantization With Response-Adaptive Calibration
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Solution Overview
Problem
Existing post-training quantization (PTQ) techniques for large language models (LLMs) suffer from significant performance degradation, especially at extremely low-bit quantization, due to their response-agnostic approach, which fails to accurately match the final output of the model, leading to sub-optimal accuracy and computational challenges.
Innovation Solution
A response-adaptive calibration method for PTQ that computes an approximated Hessian matrix based on gradient matrices to correct the final output of the model, iteratively quantizing and calibrating linear layers to minimize the difference between the quantized and original model outputs, thereby improving accuracy at extreme low-bit quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If post-training quantization is applied to reduce bit precision, then model size and computational requirements are reduced, but accuracy deteriorates significantly
Solution Approach 1:
The patent applies preliminary calibration actions before final quantization. It computes Hessian matrices and identifies critical parameters that need special handling during quantization. This preliminary analysis enables the method to preserve accuracy by focusing calibration efforts on the most important parameters rather than uniformly quantizing all parameters
Solution Approach 2:
The patent dynamically adjusts quantization parameters based on the computed Hessian matrices. It changes the quantization precision for different parameters according to their importance, using the Hessian information to determine which parameters can be aggressively quantized and which need higher precision. This adaptive parameter adjustment resolves the contradiction between compression and accuracy
2Device complexity
If response-agnostic calibration is used to simplify the calibration process, then computational complexity is reduced, but final output accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the calibration process continuously monitors the final model output and adjusts quantization parameters accordingly. The Hessian matrix computation provides feedback about parameter importance, and the calibration process uses this feedback to iteratively improve quantization accuracy. This feedback loop ensures that the simplified calibration process still achieves high output accuracy
Solution Approach 2:
The patent changes calibration parameters dynamically based on Hessian matrix analysis. Instead of using fixed calibration settings, it adjusts the calibration strength and focus for different parameters based on their computed importance. This adaptive approach maintains output accuracy while managing calibration complexity
3Ease of manufacture
If uniform quantization is applied to all parameters, then implementation simplicity is improved, but performance degradation increases
Solution Approach 1:
The patent applies different quantization qualities to different parameters based on their local importance. Using Hessian matrix analysis, it identifies which parameters are critical to model performance and applies higher precision quantization to those specific parameters while using lower precision for less important parameters. This local differentiation maintains overall model performance while enabling compression
Solution Approach 2:
The patent performs preliminary analysis of parameter importance before applying quantization. By computing Hessian matrices and identifying critical parameters in advance, it prepares a customized quantization strategy for each parameter. This preliminary action enables the system to avoid uniform quantization pitfalls while maintaining implementation feasibility
Data Source
AI summary
Post-training quantization of Large Language Models and other neural networks. A plurality of quantization iterations are applied during which each of the linear network layers of a block are processed as follows: a respective approximated Hessian matrix is computed for the linear network layer using a set of layer-specific network weights for the linear network layer as computed in a preceding quantization iteration and sets of layer-specific network weights for each of the remaining linear network layers set to the values of such weights prior to the preceding quantization iteration; then an updated set of quantized set of layer-specific network weights for the linear network layer is computed based on the respective approximated Hessian matrix for the linear network layer. The updated quantized sets of layer-specific network weights computed in a final quantization iteration are stored as a final quantized set of parameters for the block.