RadixAttention是在SGLang的论文(《Efficiently Programming Large Language Models using SGLang》)中被提出的,其目的是为了实现Automatic KV Cache Reuse。本文只关注RadixAttention,暂时不关注SGLang的其他内容。RadixAttention使用radix tree(基数树,说实话,这棵树我也不太熟悉)而不是prefix tree。Radix Tree最大的特点...
由前面的分析我们知道,RadixAttention算法中的Prefix Caching是包括Prefix和Generated KV Cache,并且如果Generated KV Cache如果也能被缓存,那么在多轮对话的场景中,显然具有更大的首Token时延优势。因此,我也比较关注vLLM实际的实现是否和RadixAttention算法描述的一致。我提了issue咨询vLLM团队,他们的回复是: yes! 也就...
后端:使用 RadixAttention 自动 KV 缓存重用 前端:使用 SGLang 轻松进行 LLM 编程 基准 对比 应用 结论 链接 大型语言模型 (LLM) 越来越多地用于需要多个链式生成调用、高级提示技术、控制流以及与外部环境交互的复杂任务。然而,用于编程和执行这些应用程序的高效系统存在显着的缺陷。为了解决这一差距,开源社区的研究...
在这篇博文中,我们将首先介绍我们在后端实现的关键优化,然后继续解释前端 API。 后端:使用 RadixAttention 自动 KV 缓存重用 在SGLang 运行时的开发过程中,我们发现了复杂 LLM 程序的一个关键优化机会,而当前系统对此处理不佳:KV 缓存重用。 KV缓存复用意味着具有相同前缀的不同提示可以共享中间KV缓存,避免冗余的内...
后端:使用 RadixAttention 自动 KV 缓存重用 在SGLang 运行时的开发过程中,我们发现了复杂 LLM 程序的一个关键优化机会,而当前系统对此处理不佳:KV 缓存重用。 KV缓存复用意味着具有相同前缀的不同提示可以共享中间KV缓存,避免冗余的内存和计算。 在涉及多个 LLM 调用的复杂程序中,可能存在各种 KV 缓存重用模式。
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