Neural Network Fine-Tuning With Quantized Base and Adapter Weights
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Solution Overview
Problem
Large machine learning models require significant operation and memory resources during fine-tuning, which can be inefficient and costly.
Innovation Solution
The solution involves quantizing a base weight from a pre-trained model to low precision and combining it with an adapter weight in high precision to determine a final weight, performing mixed precision operations during forward and back propagation to reduce computational and memory overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high precision weights are used for fine-tuning large machine learning models, then model accuracy is maintained, but operation resources and memory resources are significantly increased
Solution Approach 1:
The patent applies different precision levels to different components of the weight system. Specifically, the base weight from the pre-trained model is quantized to low precision (e.g., INT8), while the adapter weight is maintained in high precision (e.g., FP16). This local quality differentiation allows the system to reduce computational resources for the majority of weight operations while preserving accuracy through the high-precision adapter component that captures task-specific adjustments.
Solution Approach 2:
The patent creates a composite weight representation by combining quantized base weight and adapter weight. The final weight is computed as the sum of the low-precision quantized base weight and high-precision adapter weight. This composite approach leverages the memory efficiency of quantization while maintaining the accuracy benefits of high-precision representations for the adaptive portions of the model.
2Measurement precision
If high precision weights are used for fine-tuning large machine learning models, then model accuracy is maintained, but memory resources are significantly increased
Solution Approach 1:
The patent applies different precision levels to different components of the weight system. Specifically, the base weight from the pre-trained model is quantized to low precision (e.g., INT8), while the adapter weight is maintained in high precision (e.g., FP16). This local quality differentiation allows the system to reduce memory usage for the majority of weight operations while preserving accuracy through the high-precision adapter component that captures task-specific adjustments.
Solution Approach 2:
The patent changes the precision parameter of the base weight through quantization operations. By transforming the base weight from high precision to low precision representation and only maintaining high precision for the adapter weight, the system achieves significant memory reduction while the accuracy is preserved through the adapter component that adapts to the specific task requirements.
3Use of energy by moving object
If quantization is applied to base weight, then operational costs are reduced and memory usage is optimized, but quantization errors may affect model accuracy
Solution Approach 1:
The adapter weight acts as an intermediary component that compensates for the quantization errors in the base weight. The final weight is computed by adding the high-precision adapter weight to the low-precision quantized base weight. The adapter weight, being in high precision, serves as a mediator that restores the necessary precision information lost during quantization, thereby maintaining model accuracy while benefiting from the operational cost reductions of quantization.
Solution Approach 2:
The patent creates a composite weight representation by combining quantized base weight and adapter weight. The final weight is computed as the sum of the low-precision quantized base weight and high-precision adapter weight. This composite approach leverages the memory efficiency of quantization while maintaining the accuracy benefits of high-precision representations for the adaptive portions of the model.
Data Source
AI summary
An electronic device for fine-tuning a machine learning model and a method of operating the electronic device are provided. The electronic device includes at least one processor and a memory configured to store instructions executable by the at least one processor. When at least some of the instructions are executed by the at least one processor, the at least some of the instructions executed control the electronic device to determine a final weight of a current layer of a neural network by quantizing an addition result of combining a quantized base weight in low precision to an adapter weight in high precision, generate a product result based on the final weight and an activation input of the current layer, and transmit the multiplication result to a next layer of the neural network.