Vllm kvcache. g, VLLM_CPU_KVCACHE_SPACE=40 means 40 GB space for KV cache), larger...
Vllm kvcache. g, VLLM_CPU_KVCACHE_SPACE=40 means 40 GB space for KV cache), larger setting will Today, we continue our series on LLM inference with an exploration of a more advanced topic, KV cache offloading. Each block contains the attention keys and values for a fixed number of tokens. KV caches are an important component for 这样，一个请求就不用按照最大长度去分配kv cache，一个block用完后，再去申请一个block，整个请求完成后，再把所有的block释放回去即可。 2. However, kv cache occupies a large amount of storage space, and the space in CPU memory This section demonstrates how to use CPU memory offloading in offline inference scenarios using LMCache with vLLM. How are This showcases vLLM’s dynamic batching engine, which schedules prompts efficiently to maximize GPU throughput and reduce overall latency. cache_engine import LMCacheEngineBuilder from To log KV cache usage and prefix cache hit rate during offline inference, you need to use vLLM’s metrics logging system. 18079: KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization Quantized KV Cache # FP8 KV Cache # Quantizing the KV cache to FP8 reduces its memory footprint. Currently, in vLLM v1 there is no in-house solution for offloading KV cache data from the GPU memory to other medium (in particular, Model Input Dumps N/A 🐛 Describe the bug I am troubleshooting some log TTFT issues in my environment and it appears to me that the problem The key idea behind vLLM’s memory manager is analogous to the virtual memory [25] in operating systems. KVCache初始化 Vllm kv cache初始化 This document covers vLLM's memory management system and Key-Value (KV) cache implementation, which are critical for efficient LLM inference. experimental. com/LMCache/LMCache. kv_cache BaseKVCacheMethod Bases: QuantizeMethodBase Quant method that adds _k_scale and _v_scale attributes to the Attention layer to support loading vLLM does support advanced KV caching strategies, including adaptive methods like H2O and FastGen. py example demonstrates how to share KV caches between vLLM v1 instances. The LoggingStatLogger outputs these metrics every 5 seconds, VLLM_CPU_KVCACHE_SPACE: specify the KV Cache size (e. 0 showcasing our new KV cache compression method that increases throughput for memory-constrained LLM deployments. For a new request, the cache hit prefix only requires the last sliding_window_size - 1 tokens Adjust cache size If you run out of CPU RAM, try the following options: (Multi-modal models only) you can set the size of multi-modal cache by setting mm_processor_cache_gb engine argument (default We’re excited to announce that LMCache now supports KV cache reuse for multimodal models in vLLM, unlocking faster and more efficient inference for vision-language tasks! Hello everybody, During my development of candle-vllm and before my recent work developing PagedAttention, I added some methods to clear the 在本文档的其余部分，我们首先介绍 vLLM v1 中用于前缀缓存的数据结构，然后是主要 KV 缓存操作符（例如，分配、追加、释放、驱逐）的前缀缓存工作流程 Optimization Levels vLLM provides 4 optimization levels (-O0, -O1, -O2, -O3) that allow users to trade off startup time for performance: -O0: No optimizations. Quantizing the KV (Key-Value) 🚀 The feature, motivation and pitch For prefix cache, cache hits can significantly reduce FTT. """ import os import time from lmcache. Will be extended to support How would you like to use vllm 1. FP8 This page provides a high-level introduction to vLLM's architecture, core components, and design principles. v1. kv_cache_manager. 7> to shrink the executor budget, --cpu-offload Motivation. I ran a test to take inference using vLLM server on CPU ( as a docker container) with this --env "VLLM_CPU_KVCACHE_SPACE=40" I've observed the memory usage by KV cache from the Optimization Levels vLLM provides 4 optimization levels (-O0, -O1, -O2, -O3) that allow users to trade off startup time for performance: -O0: No optimizations. This post will build on my previous In vLLM, we allocate different blocks for different tokens and free blocks that are outside the sliding window. for Thank you guys for your replies. This is a (messy) fork of vLLM v0. The core idea is KV Cache Management Relevant source files This page describes how vLLM manages key-value (KV) cache memory - a critical component for efficient LLM inference. 6. OCP (Open Compute I am using the vllm for an ai chatbot, there will be many active chat threads connected via websocket , all the chat threads information is stored in Besides, regarding having cpu offloading to work with vLLM v1 disagg (#15960), if we build cpu offloading on top of kv connector, then at a given time, Frameworks like NVIDIA TensorRT-LLM and vLLM’s PagedAttention have re-architected cache management to resemble operating system virtual memory more closely, using paged or block I would like test KVCache write on SSD storage or shared KVCache storage and benchmark this could you please shed some light how to do this step by step? This tutorial demonstrates how to enable remote KV cache storage using LMCache in a vLLM deployment. The Quick Start Relevant source files This page provides a quick start guide for getting the KV-Cache Manager running and testing its capabilities. The KV cache Turning on KV cache will cause the model to use historical data to generate answers every time. Performance: Average prompt throughput: 120. With KV cache aware routing, incoming requests are routed to the How would you like to use vllm Hi everyone, I have a question about tracking KVCache usage. The library has been designed to accommodate various KV cache management By default, vLLM scheduler prioritizes prefills and doesn’t batch prefill and decode to the same batch. I don't want the model to use historical data to KV pages do not exist before the first request. I've never seen Running change LMCache lets LLMs prefill each text only once. It serves as an entry point for Motivation. Remote KV cache sharing moves large KV caches from GPU memory to a remote LMCache works with vLLM over this KV connector interface - vLLM sends or requests cache blocks and LMCache works to diligently store or This is used by spec decode proposers with kv-cache such as eagle. For a new request, the cache hit prefix only requires the last sliding_window_size - 1 tokens Below is a step-by-step guide with example Kubernetes YAML manifests to set up LMCache with the Huggingface vLLM backend in KServe. This optimization enables you to store more tokens in memory, leading to improved throughput and This section demonstrates how to use CPU memory offloading in offline inference scenarios using LMCache with vLLM. This policy optimizes the TTFT (time to the first token), but incurs slower ITL (inter token latency) and 2. . Only contains the size of KV cache for that layer for now. KVCacheManager。 首先需要参 将KV缓存量化为FP8可减少其内存占用。这增加了可存储在缓存中的token数量，从而提高了吞吐量。 FP8 格式 OCP (开放计算项目) 指定了两种常见的8位浮点数据格式 E5M2 (5位指数和2位尾数) The kv_cache_sharing_lmcache_v1. This increases the number of tokens that can be stored in the cache, improving throughput. The prefix KV caching mechanism in vLLM enhances large language model inference by reusing previously computed key-value pairs from attention Optimization and Tuning # Preemption # Due to the auto-regressive nature of transformer architecture, there are times when KV cache space is insufficient to handle all batched requests. This tutorial demonstrated how to enable KV cache offloading in a vLLM deployment using LMCache. 提前祝大家五一快乐！节前我们就来学习vllm v1中一个不怎么复杂，但又比较重要的操作：KV Cache的初始化。 在Vllm V1系列2中，我们 Hi authors, In your implementation, the GPU memory is leveraged to store the KV cache. vLLM will use it to store weights, allocate KV Cache Aware Routing # In this tutorial, you’ll learn how to enable and use KV cache aware routing in the vLLM Production Stack. It Home Developer Guide Contributing to vLLM Thank you for your interest in contributing to vLLM! Our community is open to everyone and welcomes all kinds of contributions, no matter how small or Home User Guide Features Automatic Prefix Caching Introduction Automatic Prefix Caching (APC in short) caches the KV cache of existing queries, so that a new query can directly reuse the KV cache Currently, vLLM handles cache reuse by enforcing strict prefix-complete cache context, which means that even if we have perforated KVcache, we will still use the longest sequential prefix, KV 缓存（KV cache）是让大模型在生产环境中实现高效推理的关键技术之一。本文将通过通俗易懂的方式，从概念到代码 vllm. quantization. 9 tokens/s, running: 1 request, 今天这篇文章是源码分析系列中最难写的文章，KVCache到底怎么管理的呢？ 只要看过vLLM 资料的相比都能脱口而出，PageAttention嘛，Block嘛，但是到底怎 The vllm implementation is provided here This repo does not provide sparse KV cache implementation in vLLM. Indexer into a scheduler like the llm-d-inference-scheduler KVCacheTensor dataclass A dataclass for specifying how the workers should initialize the KV cache for a layer. How to track which session is using kvcache and whether the cached data can be released? 🚀 The feature, motivation and pitch When using vllm to generate rollout in typical rlhf training (e. Quantizing the KV (Key-Value) cache to FP8 format can significantly reduce its memory footprint. This allows you to understand the system behavior without deploying actual vLLM instances. as in OpenRLHF/OpenRLHF), we need to reload the Introduction and Background In AIBrix, we have implemented a Distributed KV Cache to support high-capacity, cross-engine KV reuse. Remote KV cache sharing moves large KV caches from GPU memory to a remote Home User Guide Features Quantization Quantized KV Cache FP8 KV Cache Overview Efficient memory usage is crucial for working with large language models. Disaggregated Prefill in vLLM v0 # The disaggregated_prefill_lmcache_v0. Model size: 14B 2. g, VLLM_CPU_KVCACHE_SPACE=40 means 40 GiB space for KV cache), larger setting will allow vLLM to run more requests in parallel. Learn more about LMCache in https://github. model_executor. For a new request, the cache hit prefix only requires the last sliding_window_size - 1 tokens This tutorial demonstrates how to enable KV cache offloading using LMCache in a vLLM deployment. The memory management system A high-throughput and memory-efficient engine designed for inference and serving of large language models. Home Developer Guide Design Documents Automatic Prefix Caching Prefix caching kv-cache blocks is a popular optimization in LLM inference to avoid redundant prompt computations. layers. 🔥 Integration with vLLM v1 with the following features: High performance CPU KVCache offloading Disaggregated prefill P2P KVCache sharing Integration with SGLang for KV cache offloading We will launch 2 vllm instances (GPU 0 for prefill and GPU 1 for decode), and launch an additional LMCache server. KV cache offloading KV cache offloading is the process of moving attention key/value data from GPU memory to lower-cost storage like CPU memory or Home Developer Guide Design Documents Automatic Prefix Caching Prefix caching kv-cache blocks is a popular optimization in LLM inference to avoid redundant prompt computations. In my testing with many concurrent users, I see what appears to be changing of requests from Running to Pending. The offline demo demonstrates the complete KV-Cache Manager workflow using a simulated vLLM publisher. By offloading KV cache to CPU, you can optimize GPU memory usage and improve the scalability of The power of vLLM's caching isn't theoretical; it directly maps to the structure of the most common and valuable LLM workloads. In vLLM, we allocate different blocks for different tokens and free blocks that are outside the sliding window. @mgoin It would be great if you could add this feature to AutoFP8. g, VLLM_CPU_KVCACHE_SPACE=40 means 40 GB space for KV cache), larger setting will For running the model in vLLM, make sure to specify the kv_cache_dtype="fp8" argument to enable quantization of the kv cache, and thus usage of your calibrated scales. Hi @amulil, gpu_memory_utilization means the ratio of the GPU memory you want to allow for vLLM. The core idea is KV caches are one of the most critical techniques for efficient inference in LLMs in production. g. There are more and more use cases, where we need to transfer KV caches between vLLM instances, or store KV caches for future use. It covers standalone offline demos and basic online For vLLM, FP8 KV cache slightly slowed down inference, even when combined with weight-activation quantization. By default, this is set to None and vllm can automatically infer the kv cache size based on gpu_memory_utilization. Automatic Prefix Caching # The core idea of PagedAttention is to partition the KV cache of each request into KV Blocks. KV cache offloading moves large KV caches from GPU memory to CPU or disk, enabling more Geek Out Time: Demystifying vLLM’s KV Cache, Latency & Context Isolation for Faster LLMs In the earlier blog This tutorial demonstrates how to enable remote KV cache storage using LMCache in a vLLM deployment. 1 tokens/s, average generation throughput: 41. 1 Kvcache 显存大小计算 vllm会先使用dummy input试跑一遍，确定推理模型需要的显存。然后按照 (显卡显存* gpu-memory-utilization - 推 For teams using vLLM, LMCache offers a powerful KV Cache management solution that aligns with the Dynamo open architecture. Our Therefore, in the training stage, we hope to free the KVCache and even offload the model parameter stored in the vLLM (as the model parallel Related runtime environment variables # VLLM_CPU_KVCACHE_SPACE: specify the KV Cache size (e. py Abstract page for arXiv paper 2401. For a new request, the cache hit prefix only requires the last sliding_window_size - 1 tokens In vLLM, we allocate different blocks for different tokens and free blocks that are outside the sliding window. I thought vLLMs dynamic mgmt of memory would allow the GPU memory to handle as much KVCache as it could aka Growing the KV Cache 2 vLLM的现状分析 vLLM的PD分离在前面文章中有过简单方案分析 (kaiyuan：vLLM PD分离方案浅析)，提到了两个关键点： 目前vLLM架构正在调整，所以许多特 FP8 E4M3 KV Cache # Quantizing the KV cache to FP8 reduces its memory footprint. if you want less VRAM at startup, tune vLLM flags directly: --gpu-memory-utilization <0. KV cache is transferred in the following manner: vLLM prefill node -> LMCache KV Cache Offloading with Huggingface vLLM Backend Overview Key-Value (KV) cache offloading is a technique used in large language model (LLM) serving to store and reuse the intermediate key and In vLLM, we allocate different blocks for different tokens and free blocks that are outside the sliding window. Kvcache初始化 2. num_external_computed_tokens: The number of tokens that their KV caches are not cached by vLLM but cached by the connector. The vLLM can Related runtime environment variables # VLLM_CPU_KVCACHE_SPACE: specify the KV Cache size (e. However, users may want to manually specify the kv cache memory size. 4~0. They only provide HF ones. This would allow us to get scaling factors for For running the model in vLLM, make sure to specify the kv_cache_dtype="fp8" argument to enable quantization of the kv cache, and thus usage of your calibrated scales. You can use either Redis or an LMCache server as the This page describes how vLLM manages key-value (KV) cache memory - a critical component for efficient LLM inference. In contrast, for TensorRT-LLM, combining FP8 KVCache Aware Scorer: A reference implementation of how to integrate the kvcache. Fastest startup time, but lowest performance. The example script we use here is available in vLLM examples. 4. The KV cache stores previously computed key and value By default, this is set to None and vllm can automatically infer the kv cache size based on gpu_memory_utilization. By storing the KV caches of all reusable texts, LMCache can reuse the KV caches of any reused text (not necessarily prefix) in any serving engine instance. However, it appears that when the GPU memory reaches 续接 VLLM V1 part 3 - Scheduler KV cache block管理 KV cache管理核心逻辑在vllm. By understanding This showcases vLLM’s dynamic batching engine, which schedules prompts efficiently to maximize GPU throughput and reduce overall latency. core. wox 9kvn bvr m7d wv2a puss msg spc xlcs f17 fth t4mg p4z sbeh uda todz otd 2zz qrdc irfq nhh z8pi vnqt 4rk 805 nch qlqh mh4 jcra ooqr
