Sep 12, 2023 · When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems.

vLLM 的核心是 PagedAttention ，而 PagedAttention 核心则是 attention_ops.single_query_cached_kv_attention op，下面首先了解该 op 的使用方法并验证其正确性，完整代码参见vllm/tests/kernels/test_attention.py 。

May 15, 2024 · PagedAttention is the heart of vLLM’s performance enhancements. It addresses the critical issue of memory management in LLM serving by partitioning the KV cache into blocks, allowing...

Oct 23, 2024 · Two key techniques that have emerged to optimize LLM inference are KV (Key-Value) caching and paged attention. In this post, we’ll break down these concepts, understand why they matter, and...

Attention计算时使用页式（paged）管理KVCache用于增加服务吞吐率，但对延迟有负面影响，因此高效的PA实现方法，利用页式内存管理同时尽量降低其负面影响，对框架的综合性能表现至关重要。 本文章将描述PA CUDA Kernel的实现细节，这些细节是公开的论文和博客所不涉及的，但却对框架的速度至关重要。 另外，PA实现改编自 FasterTransformers 某个版本的MHA实 …

PagedAttention attempts to optimize memory use by partitioning the KV cache into blocks that are accessed through a lookup table. Thus, the KV cache does not need to be stored in contiguous memory, and blocks are allocated as needed.

Jan 4, 2025 · This paper proposes PagedAttention, a new attention algorithm that allows attention keys and values to be stored in non-contiguous paged memory, and presents vLLM, a high-throughput LLM serving system with efficient memory management enabled by PagedAttention.

The core idea behind Paged Attention is to partition the KV cache of each sequence into smaller, more manageable "pages" or blocks. Each block contains key-value vectors for a fixed number of tokens. This way, the KV cache can be loaded and accessed more …

Dec 13, 2024 · • We propose PagedAttention, an attention algorithm that operates on KV cache stored in non-contiguous paged memory, which is inspired by the virtual memory and paging in OS. • We design and implement vLLM, a distributed LLM serving engine built on top of PagedAttention.

To achieve high performance, this kernel relies on a specially designed memory layout and access method, specifically when threads read data from global memory to shared memory.