Dynamic Large Model Compression via Loss-Based Node Removal
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Solution Overview
Problem
Complex machine learning models, such as large language models constructed via artificial neural networks, require excessive computing resources and time for execution, even when performing subset tasks, leading to resource wastage and inefficiency.
Innovation Solution
A model compression framework that identifies and removes functionally non-contributing nodes and layers based on an application-dependent dataset, using a loss-based evaluation scheme to create a compressed model that optimizes resource usage without impacting performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a comprehensive large language model is used to handle subset tasks, then task coverage is improved, but computational resource consumption increases excessively
Solution Approach 1:
The patent segments the comprehensive large language model into multiple specialized sub-models, each trained to handle specific task types (e.g., classification, generation, translation). This segmentation allows the system to deploy only the necessary sub-model for each specific task, reducing computational resource consumption while maintaining task coverage across multiple domains.
Solution Approach 2:
The patent implements dynamic model selection and compression mechanisms that adapt the model architecture based on the specific task requirements. The system dynamically determines which sub-models to activate and applies compression techniques selectively, transforming the static comprehensive model into a dynamic system that optimizes resource usage according to actual task demands.
2Adaptability or versatility
If a comprehensive large language model is used, then model functionality is improved, but execution time increases
Solution Approach 1:
By dividing the comprehensive model into specialized sub-models, the patent enables parallel processing and faster inference for specific tasks. Each sub-model is optimized for its designated function, reducing the computational path length and execution time compared to running the entire comprehensive model for every task.
Solution Approach 2:
The patent applies compression techniques selectively to sub-models based on their importance and usage frequency. Less critical sub-models are compressed more aggressively, while essential sub-models maintain higher fidelity. This partial action approach reduces overall execution time while preserving critical functionality.
3Adaptability or versatility
If a comprehensive large language model is deployed, then task capability is improved, but device complexity increases
Solution Approach 1:
The patent reduces device complexity by segmenting the comprehensive model into independent, modular sub-models. Each sub-model has a simplified architecture tailored to its specific function, making the overall system more manageable and easier to deploy on resource-constrained devices while maintaining comprehensive task capability through the collection of specialized components.
Data Source
AI summary
The present teaching relates to compressing a model for an application to generate a compressed model. The model has multiple layers, each of which has multiple nodes. Operating the model utilizing an application-dependent dataset, redundant nodes/layers in the model are identified via a loss-based assessment. The loss-based assessment using aggregated output vectors computed based on output vectors produced by the nodes/layers of the model in response to the data samples of the application-dependent dataset. Removing the redundant nodes/layers yields the compressed model.