Dynamic Low-Rank Estimation for Transformer Models
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Solution Overview
Problem
Existing methods for compressing transformer-based language models, such as singular value decomposition (SVD) and its variants, often prioritize minimizing reconstruction errors over preserving task performance, as they treat all parameters equally significant without considering their varying influences on overall performance.
Innovation Solution
The method involves accessing weight matrices of a machine learning model, decomposing them using singular value decomposition (SVD) into U, S, and V matrices, determining importance scores for each singular group based on the change in loss if the group is removed, ranking these groups across weight matrices, and pruning the least important groups to reduce the model's parameter budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard SVD is used to compress transformer models by minimizing reconstruction errors, then parameter reduction is achieved, but task performance deteriorates because all parameters are treated equally without considering their varying influences on performance
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different parameters through importance weighting. Instead of treating all parameters equally as in standard SVD, the method assigns different weights to parameters based on their importance scores, which are derived from their contribution to loss. This allows the compression process to preserve important parameters while removing less important ones, thereby maintaining task performance while achieving parameter reduction.
Solution Approach 2:
The patent changes the parameter of importance weighting in the SVD process. By introducing importance weights that vary across different parameters, the method transforms the uniform SVD approach into an importance-weighted SVD. This parameter change enables the model to prioritize parameters based on their actual impact on task performance, resolving the contradiction between parameter reduction and performance preservation.
2Reliability
If importance-weighted SVD is used to preserve task performance, then parameter relevance is considered, but computational complexity increases due to additional calculations required to determine importance scores
Solution Approach 1:
The patent applies preliminary action by pre-computing importance scores for parameters before performing the SVD compression. The importance scores are calculated in advance based on the model's current state, allowing the subsequent compression process to use these pre-determined weights. This preliminary computation enables the system to make informed decisions about which parameters to preserve during compression without increasing the complexity of the main compression algorithm.
Solution Approach 2:
The patent implements feedback by using the loss function to evaluate and adjust importance scores. The importance of each parameter is determined by its contribution to the loss, creating a feedback loop where the model's performance on task data directly informs the compression process. This feedback mechanism ensures that parameters contributing most to performance are preserved, while the computational overhead is managed through efficient loss calculation and importance score updates.
Data Source
AI summary
A method includes accessing a plurality of weight matrices of a machine learning model. The method also includes, for each weight matrix, decomposing the weight matrix into a U matrix, an S matrix, and a V matrix using singular value decomposition. The S matrix is a diagonal matrix, and a singular group corresponds to each element in the S matrix. The method further includes, for each weight matrix, determining an importance score of each singular group. The importance score of the singular group represents a change in loss if the singular group is removed from the machine learning model. The method also includes, for each weight matrix, ranking the singular groups across the plurality of weight matrices based on the importance scores. In addition, the method includes, for each weight matrix, identifying one or more of the singular groups to prune based on the ranking of the singular groups.