Neural Network Adapter Approximation for Accelerator-Efficient Tuning
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Solution Overview
Problem
Existing neural networks require significant computational resources and time for adaptation to various machine learning tasks, especially when using hardware accelerators like GPUs, FGPAs, and ASICs, due to the high number of parameters and operations involved.
Innovation Solution
The technique involves approximating the adapter parameter matrix with multiple low-rank dense matrices and a dense matrix block to reduce the number of parameters, making it suitable for execution on hardware accelerators and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adapter parameter matrices are used for neural network adaptation, then the neural network can be adapted to various machine learning tasks, but the computational resource consumption and time increase significantly
Solution Approach 1:
The adapter parameter matrix is segmented into multiple smaller low-rank dense matrices and a dense matrix block. This segmentation reduces the computational burden by breaking down the large matrix into manageable components that can be processed more efficiently on hardware accelerators while preserving the adaptation capability.
Solution Approach 2:
The invention changes the parameter representation from a full-rank adapter parameter matrix to a low-rank approximation composed of multiple smaller matrices and a dense matrix block. This parameter transformation maintains the essential adaptation functionality while significantly reducing the number of parameters and computational operations required.
2Reliability
If full-rank adapter parameter matrices are used, then the neural network maintains high expressiveness, but the memory footprint and computational operations increase from Θ(n²) to Θ(ns+nb)
Solution Approach 1:
The invention transforms the parameter structure from a full-rank n×n adapter parameter matrix to a low-rank approximation using multiple smaller matrices of size n×s and n×b. This parameter transformation reduces the total number of parameters from n² to ns+nb, significantly decreasing memory footprint while maintaining model performance through the low-rank structure.
Solution Approach 2:
The invention uses multiple smaller low-rank dense matrices and a dense matrix block to collectively represent the adapter parameter matrix. This approach creates a simplified copy or approximation of the original full-rank matrix that captures the essential information with fewer parameters.
3Quantity of substance
If sparse matrices are used for adaptation, then the parameter count is reduced, but hardware utilization rate and efficiency on hardware accelerators decrease
Solution Approach 1:
The invention changes the matrix structure from sparse to low-rank dense format. By representing the adapter parameters as multiple low-rank dense matrices and a dense matrix block, the invention maintains high hardware utilization rates on accelerators like GPUs and FPGAs while still reducing the total parameter count compared to full-rank dense matrices.
Solution Approach 2:
The invention creates a composite structure combining multiple low-rank dense matrices and a dense matrix block. This composite approach leverages the strengths of different matrix types to achieve both parameter reduction and hardware efficiency, optimizing for the specific capabilities of hardware accelerators.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes obtaining data specifying a trained neural network that includes a plurality of layers that include a particular layer; generating an adapted neural network, comprising generating, for the particular layer, an approximation of an adapter parameter matrix that includes fewer parameters than the adapter parameter matrix; and training the adapted neural network on a machine learning task, wherein the adapting comprises learning fine-tuned values of parameters of the approximation using training data while holding the trained values in the base parameter matrix fixed.