TL;DR:
- FastServe introduces a unique skip-join Multi-Level Feedback Queue (MLFQ) scheduler for efficient LLM inference serving.
- MLFQ is a well-known method for minimizing average job completion time (JCT) in information-free environments.
- LLM inference is semi-information agnostic, with known input length but unknown output length.
- FastServe incorporates skip-join functionality to assign tasks to appropriate queues based on the execution time of the first output token.
- Preemptive scheduling with MLFQ introduces memory overhead to manage incomplete jobs and utilizes a key-value cache for intermediate states.
- FastServe employs a proactive GPU memory management system to handle cache overflow and minimize head-of-line blocking.
- Parallelization techniques like tensor and pipeline parallelism are utilized to enable distributed inference serving across multiple GPUs.
- FastServe outperforms the state-of-the-art solution Orca, enhancing average and tail JCT by up to 5.1 and 6.4, respectively.
- The innovative MLFQ-based scheduler and memory management techniques position FastServe as a leading solution for efficient LLM inference serving.
Main AI News:
The rapid advancements in large language models (LLMs) have opened up new opportunities in various fields, fueling the development of interactive AI applications. Notably, ChatGPT has emerged as a groundbreaking program, allowing people to engage with AI agents in solving a wide range of problems, from software engineering to language translation. Recognizing the immense potential, numerous companies, including Microsoft, Google, Meta, Stanford, Databricks, and UC Berkeley, are jumping on the bandwagon and releasing their own LLMs and ChatGPT-like products.
While LLM inference shares similarities with other deep neural networks (DNN) models like ResNet, it possesses distinct characteristics. Interactive AI applications built on LLMs require efficient inference to deliver seamless user experiences. Job completion time (JCT) plays a vital role in ensuring timely responses and engaging interactions. However, the increasing number and complexity of LLMs pose challenges for the inference serving infrastructure. Businesses have resorted to costly clusters equipped with accelerators such as GPUs and TPUs to handle LLM inference operations effectively.
Unlike deterministic and predictable DNN inference jobs, LLM inference follows an autoregressive pattern, where each iteration produces one output token that influences the subsequent iterations. The variable output length directly affects execution time and input length. Existing inference serving systems like Clockwork and Shepherd, designed for deterministic models such as ResNet, rely on precise execution time profiling and are ill-suited to handle LLM inference with its inherent variability.
Orca, the most advanced method for LLM inference, introduced iteration-level scheduling, allowing for dynamic job management. However, it still employs first-come, first-served (FCFS) processing, which leads to head-of-line blocking and limited scalability due to GPU memory constraints and the low JCT requirements of inference jobs.
To address these challenges, researchers at Peking University have developed FastServe, a distributed inference serving solution specifically tailored for LLMs. Leveraging iteration-level scheduling and the autoregressive nature of LLM inference, FastServe introduces token-level preemption, enabling the system to make informed decisions on whether to continue or preempt a scheduled task after generating an output token. By implementing preemptive scheduling, FastServe significantly reduces JCT and mitigates head-of-line blocking issues.
FastServe: Leveraging Unique MLFQ-Based Scheduler for Efficient LLM Inference
FastServe, a distributed inference serving system for large language models (LLMs), builds upon a distinctive skip-join Multi-Level Feedback Queue (MLFQ) scheduler to optimize job completion time (JCT) and enhance overall efficiency. MLFQ, a renowned method for minimizing average JCT, forms the foundation of FastServe’s scheduler.
Traditionally, MLFQ operates by assigning tasks to queues based on their priorities and demoting them to lower priority queues if they exceed a certain time limit. However, LLM inference, while semi-information agnostic, differs from conventional scenarios. While the input length is known, the output length remains uncertain. This distinction influences the execution time required to generate the initial output token, which can be significantly longer than subsequent tokens due to the autoregressive nature of LLM inference.
FastServe incorporates skip-join functionality into the MLFQ scheduler to optimize the handling of LLM inference tasks. Instead of always entering the highest priority queue, each task joins an appropriate queue based on the execution time of the first output token compared to the demotion thresholds. This approach bypasses higher-priority queues to minimize downgrades and improve efficiency.
Preemptive scheduling with MLFQ introduces additional memory overhead to manage incomplete jobs. LLMs utilize a key-value cache for each Transformer layer to store intermediate states. While the first-come, first-served (FCFS) cache manages the intermediate states of scheduled jobs as long as the batch size permits, additional jobs initiated in MLFQ are assigned to lower-priority queues but still require the cache to maintain their interim states. This poses challenges due to limited GPU memory space, potentially leading to cache overflow and head-of-line blocking.
To address these challenges, FastServe incorporates a proactive GPU memory management system that uploads the state of processes in low-priority queues when scheduled and offloads the state when the cache approaches its capacity. Parallelization techniques such as tensor and pipeline parallelism are employed to enable distributed inference serving across multiple GPUs for large models that do not fit in a single GPU. To minimize pipeline bubbles, the scheduler performs concurrent batches of jobs. Additionally, a distributed key-value cache manager organizes the key-value cache and manages memory swapping between GPU and host memory.
The implementation of FastServe, based on NVIDIA FasterTransformer, demonstrates remarkable improvements compared to the state-of-the-art solution Orca. The results reveal that FastServe enhances both the average and tail JCT by up to 5.1 and 6.4, respectively, showcasing its superior performance in LLM inference serving.
FastServe’s innovative MLFQ-based scheduler and advanced memory management techniques position it as a leading solution for efficient and scalable LLM inference, offering significant improvements in job completion time and overall system performance.
Conlcusion:
The introduction of FastServe, with its unique MLFQ-based scheduler and advanced memory management techniques, represents a significant advancement in the market of large language model (LLM) inference serving. This innovation addresses the challenges of efficient LLM inference, optimizing job completion time and system performance.
FastServe’s improved capabilities offer businesses a competitive edge in providing seamless and responsive AI-powered services. As the demand for interactive AI applications and LLM-based products continues to grow, FastServe’s efficiency and scalability make it a valuable solution for businesses looking to leverage the power of LLMs and deliver enhanced user experiences.
