LLM Adapter Sharing for Multi-Task Inference With Lower Latency
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Solution Overview
Problem
Conventional systems for deploying large language models (LLMs) face inefficiencies in handling multiple tasks simultaneously due to the resource-intensive and time-consuming process of loading and unloading adapters, leading to increased response times.
Innovation Solution
A method and system that extracts and initializes a set of shared layers from a pretrained LLM, creating task-specific models using pretrained adapters, allowing for efficient parallel or sequential inferencing to manage multiple tasks without repeated loading and unloading, thereby optimizing memory usage and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If adapters are loaded and unloaded sequentially for each task, then memory usage is reduced, but response time increases due to loading and unloading overhead
Solution Approach 1:
The system pre-loads multiple adapters into memory before they are needed. When a task requires a specific adapter, it is already available in memory, eliminating the need for loading operations during task execution and reducing response time while maintaining efficient memory usage.
Solution Approach 2:
The system segments the adapter management process by maintaining a separate adapter library in memory and creating a mapping between tasks and their corresponding adapters. This allows independent management of adapters and tasks, enabling efficient retrieval without sequential loading operations.
2Loss of time
If multiple adapters are loaded simultaneously for multiple tasks, then response time is reduced, but memory footprint increases
Solution Approach 1:
The system dynamically manages adapter loading based on actual task requirements. Adapters are loaded into memory only when needed and can be unloaded when no longer required, allowing the system to adapt memory usage to current workload demands while maintaining fast access to frequently used adapters.
Solution Approach 2:
The system maintains a pre-loaded adapter library that contains multiple adapters ready for immediate use. This preliminary preparation allows the system to respond quickly to multiple tasks without requiring simultaneous loading of all adapters, balancing memory usage with response time.
3Reliability
If the entire LLM is retrained for each task, then task-specific performance is optimized, but resource consumption and deployment complexity increase
Solution Approach 1:
The system segments the LLM into a base model and separate task-specific adapters. The base model remains unchanged and handles general language understanding, while adapters provide task-specific capabilities. This segmentation allows training only the lightweight adapters rather than the entire LLM for each task, significantly reducing deployment complexity while maintaining task-specific performance.
Solution Approach 2:
The base LLM serves as a universal foundation that can be combined with multiple different adapters to handle various tasks. This multi-functionality approach allows a single base model to support multiple tasks without requiring separate trained models for each task, reducing overall deployment complexity while maintaining optimized task-specific performance through adapter specialization.
Data Source
AI summary
A method and a system for inferencing large language model (LLM) is disclosed. A processor receives a pretrained LLM, a plurality of pretrained adapters corresponding to a plurality of tasks, one or more required tasks, and a user input for each of the one or more required tasks. A set of layers are extracted from the pretrained LLM based on an identification of a set of target layers from the plurality of pretrained adapters. The set of layers are initialized as a set of shared layers for each of the plurality of pretrained adapters. One or more task specific models are created based on the one or more required tasks. The user input is inferenced for each of the one or more required tasks using the one or more task specific models.