Post-Training Vector Quantization for Low-Error Weight Compression
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Solution Overview
Problem
The high computational and storage costs of deep neural network models, particularly large language models, due to the large number of parameters, make efficient inference challenging, and existing compression techniques often result in accuracy loss or require costly retraining.
Innovation Solution
A method for post-training vector quantization that involves obtaining a codebook, determining a compression ratio, and quantizing groups of weights using a vector quantization engine to minimize output error, allowing for efficient compression without retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If weights are compressed using existing quantization techniques, then memory footprint is reduced, but model accuracy deteriorates due to quantization error
Solution Approach 1:
The patent applies preliminary action by computing and storing the inverse Hessian matrix before quantization. This pre-computed second-order information is then used to guide the quantization process, allowing the method to anticipate and compensate for quantization errors in advance, thereby preserving model accuracy while achieving compression.
Solution Approach 2:
The patent implements feedback by using the inverse Hessian matrix to compute correction terms based on quantization errors. These error signals are fed back to update the weight quantization decisions, creating a closed-loop system that continuously refines the quantization to minimize accuracy loss while maintaining compression ratios.
2Volume of stationary object
If compression techniques are applied to reduce parameter storage, then storage cost decreases, but computational cost increases due to retraining requirements
Solution Approach 1:
The patent performs preliminary computation of the inverse Hessian matrix and its diagonal elements before the quantization process. This pre-computation caches essential second-order information that would otherwise require expensive retraining, allowing the method to achieve compression without full model retraining and thus reducing computational costs.
Solution Approach 2:
The patent uses approximate second-order information from the inverse Hessian diagonal as a computationally inexpensive surrogate for full Hessian computations. This approximation provides sufficient guidance for accurate quantization without the prohibitive computational cost of exact second-order methods or complete retraining, achieving a favorable trade-off between accuracy and computational expense.
3Quantity of substance
If vector quantization is applied to compress weights, then memory bandwidth requirement decreases, but quantization error increases affecting inference quality
Solution Approach 1:
The patent uses feedback from quantization errors combined with inverse Hessian information to iteratively refine weight assignments. The error signals are weighted by the inverse Hessian diagonal elements, which indicate the sensitivity of each weight to quantization, allowing the system to prioritize minimizing errors in critical weights while accepting larger errors in less sensitive weights, thus reducing overall information loss.
Solution Approach 2:
The patent changes the quantization parameters dynamically based on the inverse Hessian information. Weights with larger inverse Hessian diagonal values (indicating higher sensitivity) are assigned to more accurate quantization levels, while less sensitive weights use coarser quantization. This adaptive parameter assignment optimizes the balance between compression ratio and quantization error.
Data Source
AI summary
Systems and techniques are described for quantizing parameters (e.g., post-training vectors) associated with a pre-trained model. For example, a device can obtain a codebook for a group of weights of a pre-trained machine learning model. The device can determine a compression ratio based on the codebook and at least one of a vector quantization dimensionality, a group size, a codebook bit-width, or a scale group size. The device can quantize, via a vector quantization engine, the group of weights of the pre-trained machine learning model a plurality of columns at a time according to the compression ratio to generate a quantized pre-trained model.