Foundation Model Distillation With Embedding-Matched Teacher Transfer
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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, necessitating repeated and resource-intensive distillation processes for each new dataset.
Innovation Solution
Transform the teacher model by augmenting a linear layer and projector network to match the student model's embedding size, using self-supervised learning, and discard the projector network to obtain a transformed teacher model, then process data through both models to calculate similarity loss and train the student model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If knowledge distillation is performed for each new dataset, then the student model achieves task-specific accuracy, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The patent applies preliminary action by performing embedding size transformation on the teacher model in advance, before actual knowledge distillation for specific datasets. This preprocessing step aligns the teacher's embedding dimensions with the student model's dimensions, enabling direct distillation without repeated transformation processes for each new dataset, thus resolving the contradiction between accuracy and efficiency
Solution Approach 2:
The transformed teacher model achieves universality by being able to distill knowledge to multiple different student models with various embedding sizes through a single transformation process. The transformed teacher can serve multiple downstream tasks and datasets without requiring re-transformation, making the distillation process more efficient while maintaining task-specific accuracy
2Adaptability or versatility
If the teacher model's embedding size is transformed to match the student model, then knowledge distillation becomes feasible, but additional transformation steps are required
Solution Approach 1:
The patent extracts the embedding size transformation as a separate, standalone preprocessing step that is performed once on the teacher model. By taking out the transformation logic from the repeated distillation loop and performing it independently in advance, the process becomes more manageable and the transformed teacher can be reused across multiple distillation tasks, reducing overall complexity
Solution Approach 2:
The embedding transformation is performed as a preliminary action before actual knowledge distillation. This upfront transformation prepares the teacher model in advance, ensuring compatibility with the student model's embedding dimensions, and eliminates the need for repeated transformation steps during subsequent distillation processes for different datasets
3Measurement precision
If large Foundation Models are deployed on edge devices, then high accuracy is achieved, but the devices exceed their computational and memory constraints
Solution Approach 1:
The patent applies copying by creating a compact student model that replicates the essential knowledge and representation capabilities of the large foundation model (teacher). Through knowledge distillation, the student model learns to mimic the teacher's behavior and embeddings, achieving comparable accuracy while being small enough to deploy on resource-constrained edge devices
Solution Approach 2:
The patent changes the parameters of the model by transforming the teacher model's embedding dimensions to match the student model's smaller embedding size. This parameter transformation enables the creation of a compact model that fits within edge device constraints while maintaining the essential functional characteristics of the larger foundation model through the knowledge distillation process
Data Source
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.