PetS: A Unified Framework for Parameter-Efficient Transformers Serving

Deploying large-scale Transformer models under the conventional pre-train-then-fine-tune paradigm is impractical 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, the first 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 tasks 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. To further improve system throughput, we propose a coordinated batching strategy to deal with arbitrary input queries. We also develop a PET operator scheduling strategy to exploit parallelism between PET tasks. Comprehensive experiments on Edge/Desktop/Server GPUs demonstrate that PetS supports up to 25 times more concurrent tasks and improves the serving throughput by 1.53 times and 1.63 times on Desktop and Server GPUs, respectively.