Machine Learning Model Adaptation Using Frozen Matrices
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Solution Overview
Problem
Training and maintaining multiple machine learning models for related tasks is computationally expensive, especially for memory-constrained systems, as existing finetuning methods require separate copies of pre-trained model parameters for each task, leading to inefficiencies in computational resources and memory usage.
Innovation Solution
The method involves adapting machine learning models using frozen matrices and learned scaling vectors, where frozen matrices are fixed across tasks and scaling vectors are learned for each task, reducing the number of trainable parameters and computational resources needed, allowing for efficient adaptation and deployment on devices with limited capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If finetuning is performed to adapt a pre-trained model to multiple downstream tasks, then the model can perform various tasks, but the computational expense and memory requirements increase significantly
Solution Approach 1:
The patent segments the model adaptation process by introducing task-specific adapter modules that are separate from the main pre-trained model. These adapters are inserted at specific layers and only contain the trainable parameters needed for each task, while the main model parameters remain frozen. This segmentation allows multiple tasks to be handled with minimal computational overhead since only the small adapter modules are trained and stored, not the entire model.
Solution Approach 2:
The patent extracts only the necessary trainable parameters from the full model by using low-rank adaptation. Instead of finetuning the entire pre-trained model, the invention extracts and trains only small adapter modules with a small number of parameters (e.g., 4-16 parameters per layer). These extracted adapters are then combined with the frozen pre-trained model to achieve task-specific functionality, dramatically reducing computational expense.
2Reliability
If separate copies of pre-trained model parameters are generated for each task, then each task can be performed accurately, but memory consumption increases
Solution Approach 1:
The patent merges the pre-trained model parameters across all tasks by keeping them frozen and shared. Instead of creating separate copies for each task, the invention combines the single pre-trained model with multiple small task-specific adapters. This merging approach allows accurate performance across all downstream tasks while using minimal memory, as only the small adapter parameters (not the entire model) need to be stored simultaneously.
Solution Approach 2:
The pre-trained model serves as a universal base that can be adapted to multiple downstream tasks through shared frozen parameters. The universal nature of the pre-trained model allows it to serve as the foundation for all tasks, with only small task-specific adapters needed to handle different downstream tasks. This multi-functionality eliminates the need for separate full model copies for each task.
3Adaptability or versatility
If the number of downstream tasks increases, then the model becomes more versatile, but the computational resources required for finetuning increase
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the model to switch between different task-specific adapters based on the current task requirements. The system can dynamically load and unload different adapters for different tasks, enabling support for multiple downstream tasks without requiring all adapters to be simultaneously present in memory. This dynamic approach increases versatility while controlling computational resource requirements.
Solution Approach 2:
The patent applies local quality by making only the necessary parts of the model trainable for each specific task. Instead of finetuning the entire model for each task, the invention trains only the local adapter modules at specific layers. This localized training approach allows the model to handle multiple tasks with varying computational resources, as only the relevant local parameters are updated based on the current task.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for efficiently adapting a machine learning model from a base task to a downstream task based on frozen matrices. An example method generally includes receiving an input for processing through a layer of a neural network. An output of the layer of the neural network is generated based on a first product, the first product being based on a first trainable scaling vector, a first frozen matrix, a second trainable scaling vector, a second frozen matrix, and the received input.