Block-Diagonal Neural Network Finetuning for Parallel Computation
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Solution Overview
Problem
Finetuning large neural networks, such as foundation models, requires significant computational resources and time, even when using existing methods like Orthogonal Finetuning (OFT), which can be costly and inefficient.
Innovation Solution
The method employs a block-diagonal matrix to parallelize the finetuning process across threads or cores, utilizing Householder transformations to maintain performance while minimizing parameter updates, allowing for parallel computation and speeding up the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional finetuning methods are used on large neural networks, then the neural network can be adapted to downstream tasks, but the computational resources and training time required remain excessively high
Solution Approach 1:
The weight matrix is decomposed into a block-diagonal matrix with multiple smaller blocks, allowing the finetuning process to be segmented into independent operations on each block. This segmentation enables parallel computation across multiple threads or cores, significantly reducing the computational resources and time required for finetuning while maintaining the ability to adapt the neural network to downstream tasks.
2Reliability
If the neural network is trained from scratch on vast amounts of data, then the model achieves high performance, but the training process is extremely expensive in terms of time and energy
Solution Approach 1:
A pre-trained neural network is used as the starting point, with its weights initialized from the pre-training phase. The block-diagonal finetuning method then applies preliminary updates only to the necessary blocks of the weight matrix, leveraging the pre-trained knowledge while adapting to new tasks. This approach maintains high model performance while dramatically reducing the time and energy required compared to training from scratch.
3Adaptability or versatility
If finetuning is performed on large foundation models, then the model can be adapted to specific tasks with minimal additional training, but the process still requires considerable computational resources
Solution Approach 1:
The weight matrix is segmented into a block-diagonal structure with multiple independent blocks, each corresponding to a specific task or feature set. This segmentation allows the finetuning process to independently update only the relevant blocks for a given task, reducing the computational complexity and resource requirements while maintaining the model's ability to adapt to diverse tasks through the shared architecture.
4Stability of the object's composition
If Orthogonal Finetuning (OFT) is used to preserve hyperspherical energy, then the pairwise neuron relationship is maintained, but the finetuning process remains computationally expensive
Solution Approach 1:
The orthogonal finetuning process is segmented by decomposing the weight matrix into a block-diagonal structure. Each block can be updated independently while preserving the orthogonal properties and hyperspherical energy characteristics. This segmentation reduces the computational energy required by limiting the scope of updates to smaller block dimensions while maintaining the stability of neuron relationships through the block-diagonal constraint structure.
Data Source
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AI summary
Computer-implemented method for finetuning a neural network (60) comprising the steps of: • Providing an input (xl) to a layer (l, l1, l2, lk) of the neural network (60), wherein the input (xl) is an input datum (xi) of the neural network (60) or a representation of the input datum (xi); • Determining a block-diagonal matrix (Q); • Determining a first matrix (M1) by multiplying the block-diagonal matrix (Q) with a weight matrix (W) of the layer, wherein the result of the multiplication is obtained by multiplying at least a plurality, preferably all, blocks (B1, B2, Bn) of the block-diagonal matrix (Q) with a respective part (W1, W2, Wn) of the weight matrix (W) in parallel computing operations and combining the result to form the first matrix (M1); • Determining an output (ol) of the layer (l, l1, l2, lk) by multiplying the first matrix (M1) with the input (xl) of the layer (l, l1, l2, lk); • Determining an output (yi) of the neural network (60) based on the output (ol) of the layer (l, l1, l2, lk); • Adapting elements of the block-diagonal matrix (Q) based on a difference of the output (yi) of the neural network (60) and a desired output (ti) with respect to the input datum (xi).