vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

• URL: http://arxiv.org/abs/2405.04437v2
• Date: Fri, 12 Jul 2024 10:33:31 GMT
• Title: vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention
• Authors: Ramya Prabhu, Ajay Nayak, Jayashree Mohan, Ramachandran Ramjee, Ashish Panwar,
• Abstract summary: VLLM proposed PagedAttention to enable dynamic memory allocation for KV-cache. PagedAttention changes the layout of KV-cache from contiguous virtual memory to non-contiguous virtual memory. We propose vAttention, a new approach for dynamic KV-cache memory management.
• Score: 8.20523619534105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient management of GPU memory is essential for high throughput LLM inference. Prior systems used to reserve KV-cache memory ahead-of-time that resulted in wasted capacity due to internal fragmentation. Inspired by demand paging, vLLM proposed PagedAttention to enable dynamic memory allocation for KV-cache. This approach eliminates fragmentation and improves serving throughout. However, to be able to allocate physical memory dynamically, PagedAttention changes the layout of KV-cache from contiguous virtual memory to non-contiguous virtual memory. As a consequence, one needs to rewrite the attention kernels to support paging, and implement a memory manager in the serving framework. This results in both performance and programming overheads, as well as portability challenges in adopting state-of-the-art attention kernels. In this paper, we propose vAttention, a new approach for dynamic KV-cache memory management. In contrast to PagedAttention, vAttention stores KV-cache in contiguous virtual memory and leverages OS support for on-demand allocation of physical memory. vAttention thus enables one to use state-of-the art attention kernels out-of-the-box by adding support for dynamic allocation of physical memory without having to re-write their code. We implement vAttention in the vLLM serving stack to show that it also helps improve decode throughput by up to 1.99x over vLLM, and the end-to-end serving throughput by up to 1.22x and 1.29x, compared to using the state-of-the-art PagedAttention based kernels of FlashAttention and FlashInfer.

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