Hierarchical Model Quantization for Low-Bit Performance Loss
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Solution Overview
Problem
Existing model quantization methods face challenges in reducing performance loss when converting floating-point parameters to fixed-point parameters, especially at lower bits, due to varying sensitivity across neural network layers and device compatibility issues.
Innovation Solution
A hierarchical quantization method is employed, where model layers are sorted by performance deviations and quantized in descending order, with parameter adjustments using gradient derivation to minimize performance loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If model quantization is performed to reduce storage consumption and computation amount, then storage efficiency and device compatibility are improved, but performance loss increases especially at lower bits
Solution Approach 1:
The patent applies different quantization bit depths to different model layers based on their sensitivity characteristics. Important layers use higher bit depths (e.g., 16-bit or 32-bit) to maintain performance, while less sensitive layers use lower bit depths (e.g., 8-bit or 4-bit) to reduce storage. This local differentiation resolves the contradiction by optimizing storage efficiency without uniformly sacrificing performance across all layers.
Solution Approach 2:
The patent dynamically adjusts quantization parameters (bit depth, precision levels) based on layer-specific sensitivity analysis. By changing the quantization parameter settings according to each layer's importance and sensitivity to quantization error, the system achieves better performance preservation at lower overall storage consumption compared to uniform quantization approaches.
2Adaptability or versatility
If uniform quantization is applied to all model layers, then device compatibility is improved, but performance loss varies significantly across layers with some layers being more sensitive than others
Solution Approach 1:
The patent implements layer-specific quantization strategies where each model layer is analyzed for its sensitivity to quantization and assigned appropriate quantization parameters. This local quality approach ensures that critical layers maintain high precision while less sensitive layers can use lower precision, achieving consistent performance across devices without uniform quantization penalties.
Solution Approach 2:
The patent segments the model into different layers and applies different quantization schemes to each segment. By dividing the model and treating each layer individually based on its characteristics, the system achieves both device compatibility (through quantization) and performance consistency (through tailored quantization strategies for each segment).
3Use of energy by moving object
If low-bit quantization is used to reduce computation amount, then resource consumption is reduced, but decoding performance deteriorates due to sensitivity variations across layers
Solution Approach 1:
The patent applies different quantization bit depths to different model layers based on their sensitivity characteristics. Important layers use higher bit depths (e.g., 16-bit or 32-bit) to maintain performance, while less sensitive layers use lower bit depths (e.g., 8-bit or 4-bit) to reduce storage. This local differentiation resolves the contradiction by optimizing storage efficiency without uniformly sacrificing performance across all layers.
Solution Approach 2:
The patent dynamically adjusts quantization parameters (bit depth, precision levels) based on layer-specific sensitivity analysis. By changing the quantization parameter settings according to each layer's importance and sensitivity to quantization error, the system achieves better performance preservation at lower overall storage consumption compared to uniform quantization approaches.
Data Source
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AI summary
A model quantization method and apparatus, and a device and a storage medium, which belong to the technical field of model quantization. The method comprises: acquiring a performance deviation corresponding to each model layer in a model to be quantized; constructing a hierarchical quantization sequence according to the performance deviations corresponding to the respective model layers; and performing hierarchical quantization processing on the model layers in said model on the basis of the hierarchical quantization sequence. A hierarchical quantization sequence is constructed according to performance deviations corresponding to respective model layers in a model to be quantized, hierarchical quantization processing is respectively performed on the model layers in said model according to the sequence order of the hierarchical quantization sequence, and after quantization during the processing parameter adjustment and correction are used to reduce a performance loss of the model to the greatest possible extent, which performance loss is generated by low-bit quantization.