LLM Pruning via Local Curvature Estimation
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Solution Overview
Problem
The increasing size of transformer architectures in large language models poses challenges for deployment due to computational, environmental, or device-specific constraints, making it difficult to efficiently prune parameters while maintaining performance.
Innovation Solution
The method involves estimating the local curvature of the loss landscape of a neural network to dynamically allocate parameters for removal and update remaining weights, utilizing modern Fisher approximations and weight strength, activations, and gradient information for accurate pruning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the size of transformer architectures is increased to achieve higher performance on large corpora, then model performance is improved, but deployment difficulty increases due to computational and device constraints
Solution Approach 1:
The patent extracts and removes redundant or less important parameters from the neural network model through pruning techniques. By identifying and eliminating unnecessary connections between neurons, the model size is reduced while attempting to preserve essential functionality, thus resolving the contradiction between model performance and deployment feasibility.
Solution Approach 2:
The patent applies different pruning strategies to different parts of the neural network based on local importance metrics. By analyzing local curvature of the loss landscape and identifying specific parameters with lower impact on performance, the method selectively prunes parameters in a localized manner, maintaining overall model performance while reducing size for deployment.
2Device complexity
If parameters are removed from the neural network to reduce model size, then deployment feasibility is improved, but model performance deteriorates
Solution Approach 1:
The patent performs preliminary analysis of the loss landscape curvature before removing parameters. By pre-identifying which parameters have minimal impact on the loss function through curvature analysis, the method ensures that subsequent parameter removal will not significantly degrade model performance, thus resolving the contradiction between size reduction and performance maintenance.
Solution Approach 2:
The patent uses gradient information and curvature estimates as feedback to guide the parameter pruning process. By continuously monitoring how parameter removal affects the loss landscape and adjusting pruning decisions based on this feedback, the method maintains model performance while achieving size reduction for feasible deployment.
3Productivity
If traditional pruning methods are used to compress the model, then computation time is reduced, but pruning accuracy is insufficient leading to performance loss
Solution Approach 1:
The patent employs dynamic pruning strategies that adapt to the local geometry of the loss landscape. By using curvature information and gradient-based metrics that capture the dynamic relationships between parameters, the method achieves more accurate pruning decisions compared to static traditional methods, resolving the contradiction between computation efficiency and pruning accuracy.
Solution Approach 2:
The patent changes the parameters used for pruning decisions from simple magnitude-based metrics to curvature-based metrics that incorporate second-order information. By using Hessian-based curvature estimates and gradient information, the method achieves more precise identification of removable parameters, improving pruning accuracy while maintaining computational efficiency through clever algorithmic design.
Data Source
AI summary
An apparatus has one or more memories and one or more processor(s) coupled to the memories. The processor(s) is configured to estimate a local curvature of a loss landscape of a neural network. The processor(s) is also configured to dynamically allocate parameters to be removed from the neural network based on the local curvature. The processor(s) is further configured to update remaining weights of the neural network based on the parameters to be removed.