Post-Training Quantization Model Selection via Indirect Metrics
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Solution Overview
Problem
The process of finding optimal post-training quantization (PTQ) options for neural network models is time-consuming and requires significant effort due to the need for manual exploration of different precision settings, quantization error minimization algorithms, and calibration schemes, which vary across models and datasets.
Innovation Solution
A method that converts and optimizes a floating-point machine learning model, applies multiple PTQ settings to generate various PTQ models, and evaluates them using predetermined indirect metrics to automatically find the optimal PTQ model, thereby reducing the need for manual exploration and minimizing redundant operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual exploration is used to find optimal PTQ options, then model accuracy can be optimized, but time consumption and effort increase significantly
Solution Approach 1:
The system performs self-evaluation by automatically computing indirect metrics (SQNR, MAE, cosine similarity) for multiple PTQ configurations and selecting the optimal model without requiring manual exploration or external evaluation, thereby reducing time consumption while maintaining accuracy optimization
Solution Approach 2:
The system changes evaluation parameters by using indirect metrics (signal-to-quantization-noise ratio, mean absolute error, cosine similarity) instead of direct model accuracy evaluation, enabling automated comparison and selection of PTQ models without time-consuming manual testing
2Manufacturing precision
If multiple PTQ settings are evaluated to find optimal settings, then quantized model quality improves, but the complexity of the process increases
Solution Approach 1:
The evaluation process is segmented into three independent categories (precision setting, quantization error minimization algorithm, calibration scheme), allowing systematic exploration of PTQ options while maintaining organized and manageable complexity through structured comparison
Solution Approach 2:
Indirect metrics serve as intermediaries that simplify the comparison of different PTQ models by providing computable proxies (SQNR, MAE, cosine similarity) that correlate with model quality without requiring complex direct accuracy evaluation for each configuration
3Productivity
If automated evaluation is implemented, then time and effort are reduced, but the need for accurate indirect metrics increases
Solution Approach 1:
The indirect metrics (signal-to-quantization-noise ratio, mean absolute error, cosine similarity) serve multiple functions simultaneously: they are computationally efficient for automated evaluation, provide accurate proxies for model quality, and work across different PTQ configurations and model types, enabling universal application
Data Source
AI summary
A method for finding at least one optimal post-training quantization model includes converting and optimizing a floating-point machine learning model into a converted machine learning model, applying a plurality of PTO settings to generate a plurality of PTO models, and evaluating the plurality of PTO models based on at least one predetermined indirect metric to find at least one optimal PTO model.