Quantized LoRA Adapters for Accurate Low-Cost Fine-Tuning
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Solution Overview
Problem
Large machine learning models face challenges in fine-tuning due to overfitting and substantial computational expense, with existing quantization methods either reducing accuracy or requiring excessive resources.
Innovation Solution
A hybrid approach combining post-training quantization (PTQ) and quantization-aware training (QAT) with low-rank adaptation (LoRA) to efficiently train and refine model adapters, using quantization parameters to downcast and checkpoint intermediate results, reducing memory and compute overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Weight of stationary object
If post-training quantization is used to reduce model size, then model size is reduced, but model accuracy is substantially reduced
Solution Approach 1:
The patent applies preliminary action by performing quantization-aware training during the training phase rather than applying quantization after training. The system quantizes weights during training iterations, allowing the model to learn with quantized precision requirements built-in, which prevents accuracy degradation that would occur with post-training quantization alone
Solution Approach 2:
The patent changes the parameter of quantization timing from post-training to during-training. By dynamically adjusting quantization parameters during training iterations and using adaptive quantization scales, the system achieves both reduced model size and maintained accuracy through parameter evolution during the training process
2Measurement precision
If quantization-aware training is used to preserve model accuracy, then model accuracy is preserved, but computational expense and training time are substantially increased
Solution Approach 1:
The patent segments the training process into multiple iterations where quantization is applied selectively. Rather than full quantization throughout all training, the system uses iterative quantization where weights are quantized and trained in cycles, allowing accuracy preservation with reduced overall computational cost through segmented training phases
Solution Approach 2:
The patent applies partial quantization to specific weight components rather than full quantization of all parameters. By selectively quantizing only the necessary portions of the model weights during training, the system maintains accuracy for critical parameters while reducing computational expense for less critical components
3Adaptability or versatility
If full fine-tuning of large models is performed to enable personalization, then model personalization is achieved, but computational expense and overfitting risk are increased
Solution Approach 1:
The patent extracts only the necessary adaptation components from the full model by training quantized adapters that work with the base model. Instead of fine-tuning the entire large model, the system extracts and trains only the adapter layers with quantized weights, achieving personalization with minimal computational expense while avoiding overfitting through the adapter architecture
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a first plurality of weights for a base model and a second plurality of weights for an adapter model associated with the base model are accessed. A quantized plurality of weights is generated based on the first plurality of weights, a first quantization scale for the first plurality of weights, and the second plurality of weights. A loss is generated based on processing training data using the quantized plurality of weights. An updated second plurality of weights is generated based on updating the second plurality of weights based on the loss. A machine learning model comprising quantized versions of the first plurality of weights and the updated second plurality of weights is deployed.