Vector-Quantized Codebook Rank Reduction for Low-Memory Neural Networks
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Solution Overview
Problem
The high computational and storage costs of deep neural networks, particularly large language models, due to the large number of parameters, hinder their efficient execution and deployment, especially on devices with limited resources, and existing quantization techniques fail to maintain accuracy at higher compression rates.
Innovation Solution
The use of vector quantization with non-linear quantization and increased dimensionality to compress pre-trained model weights, minimizing output reconstruction error through rank reduction and optimization techniques, allowing for efficient deployment on devices with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of parameters in machine learning models is increased to improve model capability, then model accuracy and performance are improved, but computational cost and memory requirements increase
Solution Approach 1:
The patent applies rank reduction to transform the codebook tensor from its original high-dimensional form into a lower-rank representation. By decomposing the codebook into fewer rank components, the model reduces the number of parameters while maintaining the essential information needed for accurate weight approximation, thus resolving the contradiction between model accuracy and parameter quantity
Solution Approach 2:
The patent changes the parameter representation by quantizing the codebook values to lower precision (e.g., 8-bit integers). This parameter transformation reduces the memory footprint and computational requirements while preserving sufficient accuracy for practical deployment, addressing the contradiction between model performance and resource consumption
2Volume of stationary object
If quantization compression rate is increased to reduce memory footprint, then model size is reduced, but output reconstruction error increases
Solution Approach 1:
By applying rank reduction to the codebook tensor, the patent creates a more efficient parameter space that maintains reconstruction accuracy even at higher compression rates. The reduced rank representation captures the essential variance in the codebook, allowing aggressive quantization without proportionally increasing reconstruction error
Solution Approach 2:
The patent performs rank reduction on the codebook before quantization, preparing a more compression-friendly representation in advance. This preliminary transformation ensures that the subsequent quantization process operates on an already optimized structure, minimizing the impact on reconstruction accuracy while achieving higher compression rates
3Ease of operation
If codebook size is reduced to enable deployment on resource-constrained devices, then device requirements are lowered, but quantization accuracy may deteriorate
Solution Approach 1:
The patent reduces codebook size by applying rank reduction, which transforms the codebook into a lower-dimensional representation. This allows the compressed model to be deployed on resource-constrained mobile devices while maintaining quantization accuracy through the efficient rank-based parameter space that preserves essential information with fewer parameters
Data Source
AI summary
Systems and techniques are described herein for quantizing a codebook used in the context of quantizing post-training parameters (e.g., vectors of weights) of a pre-trained model. For example, a device can perform rank reduction on a tensor of a codebook associated with parameters of a layer of a pre-trained machine learning model to generate a first tensor factor having a first shape and a second tensor factor having a second shape. The device can perform an optimization technique on the first tensor factor and the second tensor factor to minimize an output reconstruction error of the layer. The device can quantize the first tensor factor to generate a reduced size codebook.