Transformer Adapter Switching Using Key-Value Cache Reuse
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Solution Overview
Problem
Training multiple machine learning models for related tasks is computationally expensive and impractical for resource-constrained systems, leading to inefficiencies and high computational overhead during context switching between adapters.
Innovation Solution
Utilizing batch processing techniques with a base machine learning model and loadable adapters, where outputs from both are generated in parallel, and key-value caches are maintained independently to preserve context across adapters, reducing the need for reprocessing inputs and minimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models are trained to perform related tasks, then task performance is improved, but computational expense increases
Solution Approach 1:
A single pre-trained machine learning model serves multiple downstream tasks through adapter modules. The base model is trained once on large-scale datasets and can be adapted to various tasks (e.g., classification, generation, translation) by loading different adapter weight sets, eliminating the need to train separate models for each task.
Solution Approach 2:
The model adaptation mechanism is segmented into a static base model and dynamic adapter components. The base model contains general knowledge from pre-training, while adapters contain task-specific parameters. This segmentation allows the expensive base model to be trained once and reused, while only lightweight adapters need to be maintained for each task.
2Reliability
If finetuning is performed for each downstream task, then task-specific performance is improved, but computational expense and model maintenance complexity increase
Solution Approach 1:
Task-specific knowledge is extracted from the full model finetuning process and isolated into separate adapter modules. These adapters contain only the task-specific parameters needed for adaptation, while the base model remains frozen. This extraction reduces maintenance complexity by separating general knowledge from task-specific knowledge.
Solution Approach 2:
Instead of creating multiple full model copies through finetuning, the system creates lightweight adapter copies that can be loaded into a single base model. Each task has its own adapter weight set, but all share the same base model parameters, dramatically reducing storage and maintenance requirements.
3Adaptability or versatility
If context is reprocessed during adapter switching, then adapter flexibility is improved, but inference time increases
Solution Approach 1:
The key-value cache stores processed context information in advance during the first adapter's inference. When switching to a second adapter, the cached context is reused directly without reprocessing, allowing rapid adapter switching while maintaining contextual understanding. The context is prepared beforehand and made available for immediate use by any adapter.
Data Source
AI summary
Techniques and apparatus for efficiently adapting a machine learning model to perform a variety of tasks using different adapters are provided. An example method generally includes receiving an input including a sequence of tokens associated with at least an input prompt into a neural network. The sequence of tokens is generated by a transformer block and a first set of adapters associated with the transformer block. A second set of adapters associated with the transformer block is loaded. An output of the transformer block is generated based on a key-value cache associated with the input and on weights associated with the transformer block. An output of the second set of adapters associated with the transformer block is generated based on the key-value cache associated with the input and on adapter weights associated with the second set of adapters.