Foundation Model Distillation for Embedding Size Mismatch
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Solution Overview
Problem
Deploying large Foundation Models (FMs) on resource-constrained edge devices is challenging due to their size and the mismatch in embedding sizes between teacher and student models, making existing knowledge distillation methods non-scalable and resource-intensive.
Innovation Solution
Transform the teacher model by augmenting a linear layer and projector network to match the embedding size of the student model, using self-supervised learning, and calculate a similarity loss to distill knowledge into the student model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If knowledge distillation is used to reduce model size for edge deployment, then device resource consumption is reduced, but embedding size mismatch between teacher and student models creates deployment challenges
Solution Approach 1:
The patent transforms the teacher model by modifying its embedding layer parameters to match the student model's embedding size. This parameter transformation enables compatibility between models of different sizes while preserving the knowledge distillation benefits, allowing the teacher model's learned representations to be effectively transferred to the smaller student model suitable for edge devices.
2Measurement precision
If traditional knowledge distillation methods are used, then student model accuracy is improved, but multiple retraining cycles are required which are resource-intensive
Solution Approach 1:
The patent performs preliminary transformation of the teacher model's embedding layer before the knowledge distillation process. By pre-aligning the embedding sizes and preparing the teacher model in advance, the method eliminates the need for multiple iterative retraining cycles, achieving both high accuracy and training efficiency in a single distillation pass.
3Reliability
If large Foundation Models are deployed on edge devices, then task performance is improved, but device power consumption and computational load increase
Solution Approach 1:
The patent creates a compressed copy of the teacher model's knowledge through knowledge distillation, transferring essential learned representations to a smaller student model. This copied knowledge enables the student model to perform tasks with high accuracy while consuming significantly less power and computational resources on edge devices.
4Loss of energy
If model size is reduced for edge deployment, then energy consumption is reduced, but handling data variability and diversity becomes challenging
Solution Approach 1:
The teacher model is pre-trained on diverse and varied data distributions before knowledge distillation. This preliminary exposure to data variability allows the teacher model to learn robust representations that are then transferred to the student model, enabling the compact model to handle diverse input data effectively while maintaining low energy consumption.
Data Source
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AI summary
The present invention generally relates to the field of deep learning, and, more particularly, to a method and system for task agnostic distillation in foundation models. Conventional distillation methods are not scalable and also does not handle mismatches between embedding sizes of teacher and student models. Thus, embodiments of present disclosure first transforms the teacher model in such a way that its embedding size matches that of the student model. This is done by augmenting a linear layer having dimensions equal to the embedding size of the student model and a projector network to the teacher model. Then the augmented layers are trained using a self-supervised learning technique by freezing the teacher model. Finally, the projector network is discarded to obtain a transformed teacher model. The student model is then trained using transformed teacher model by performing knowledge distillation based on similarity loss.