PetS: A Scalable Inference Serving System for Parameter-Efficient Transformers

Deploying Transformer models under the conventional pre-train-then-fine-tune paradigm is challenging for multi-task serving, because a full model copy for each downstream task must be maintained, quickly exhausting the storage budget. Recent algorithmic advances in Parameter-Efficient Transformers (PETs) have shown enormous potential to mitigate the storage overhead. They share the pre-trained model among tasks and only fine-tune a small portion of task-specific parameters. Unfortunately, existing serving systems neither have flexible PET task management mechanisms nor can efficiently serve queries to different tasks in batches. Therefore, we propose PetS, a unified framework for multi-task PETs serving. Specifically, different PET tasks are expressed by a unified representation in the same framework, which enables flexible PET task management. Based on the unified representation, we design a specialized PET inference engine to batch different tasks' queries together and execute them with task-agnostic shared operators and task-specific PET operators. Equipped with the PET inference engine, PetS is more scalable with respect to the number of tasks on a single GPU device. To further improve system throughput, we propose a coordinated batching strategy taking query length, PET task type as well as system load balancing together into consideration. To improve the throughput on multiple GPU instances, we also propose a PET-migration based load balancing strategy. We evaluate PetS on platforms with single GPU, including Edge/Desktop/Server GPUs. Comprehensive experiments demonstrate that PetS supports up to 26x more concurrent tasks and improves the serving throughput by 1.53x and 1.63x on Desktop and Server GPU nodes, respectively. On multiple GPUs, our load-balancing strategy also provides up to 29% speedup.