Boosting LM Serving Efficiency with vLLM & PagedAttention

Table of Contents

1. Introduction
2. The Era of Language Models
3. The Importance of LM Serving
4. The Problem with Serving LMs
5. The Inference Process of LMs
6. The Key Insight: Efficient Management of the KV Cache
7. Memory Inefficiencies in Previous Systems
8. Introducing VLM: Virtual Memory and Paging for LMs
9. Dynamic Block Mapping and Page Attention
10. Benefits of VLM: Memory Efficiency and Sharing
11. System Architecture and Implementation of VLM
12. Performance Results and Adoption of VLM
13. How to Get Started with VLM
14. Conclusion

Introduction

In today's era of language models, LM serving has become crucial for various applications and startups. The performance and cost of serving these models largely depend on the speed and efficiency of the serving process. However, traditional methods of serving LMs have been slow and expensive, requiring a large number of GPUs to achieve production-Scale serving. In this article, we will explore the concept of Virtual Language Models (VLM) and how they address the limitations of traditional methods.

The Era of Language Models

Language models have revolutionized many industries, with applications such as chatbots, programming assistants, and business operations. These models, powered by algorithms like Chachi PT and co-pilot, have opened up exciting opportunities in various fields. As a result, there has been a surge in applications and startups leveraging LMs across different domains. In this article, we will focus on serving LMs that are reliant on the efficient management of the KV cache.

The Importance of LM Serving

LM serving plays a crucial role in the performance and cost-effectiveness of LM applications. The speed and efficiency of serving LMs greatly impact the overall user experience and operational costs. As more applications rely on LMs for their Core functionality, the need to serve LMs fast and cost-effectively becomes increasingly important. Efficiently managing the KV cache, a unique component of LMs, is key to achieving high throughput and reducing costs in serving LMs.

The Problem with Serving LMs

Serving LMs has been a major pain point for many large and small companies. Despite advancements in hardware, traditional methods of serving LMs have proven to be slow and expensive. Even with high-end GPUs, a single GPU can only serve a limited number of requests per Second. This limitation becomes even more pronounced for large models with moderate input sizes. As a result, building production-scale services using LMs requires a significant investment in GPUs, making it cost-prohibitive for many companies.

The Inference Process of LMs

To understand the problem with serving LMs, let's Recap the inference process of LMs. In this process, a user provides a prompt consisting of multiple tokens. The prompt is then fed into the LM, which generates the next token Based on the input. This process is repeated until a predefined maximum length is reached or a stopping token is generated. A unique component of LMs is the KV cache, which stores the representations of all previous tokens. Efficiently managing the KV cache is crucial for high throughput LM serving.

The Key Insight: Efficient Management of the KV Cache

The key insight in addressing the problem of slow and expensive LM serving is efficient management of the KV cache. Previous systems have used the KV cache inefficiently, resulting in significant memory waste. This memory waste can be categorized into three types: internal fragmentation, reservation, and external fragmentation. To overcome these memory inefficiencies, a new technique called page retention is introduced in Virtual Language Models (VLM).

Memory Inefficiencies in Previous Systems

Previous systems suffered from internal fragmentation, where slots were allocated for a sequence but Never used. Additionally, reservation wasted memory as certain slots were reserved but not utilized at the moment. Moreover, external fragmentation occurred due to different requests having different sequence lengths. Profiling results showed that only a fraction of the KV cache space was actually utilized, leading to significant memory waste.

Introducing VLM: Virtual Memory and Paging for LMs

VLM introduces the concept of virtual memory and paging, similar to operating systems. By partitioning the KV cache into KB blocks, VLM efficiently manages the cache using page attention. Page attention operates on the non-contiguous blocks, efficiently patching the blocks located in arbitrary positions in the KV cache space. This virtualization of the KV cache allows for efficient space sharing between requests and reduces memory waste.

Dynamic Block Mapping and Page Attention

Dynamic block mapping is a key technique in VLM that enables efficient memory management. It involves mapping logical and physical KB blocks, allowing for the sharing of memory between requests. This enables efficient sharing of memory for scenarios like Parallel sampling and Beam search. Page attention, a feature of VLM, further enhances memory efficiency by reducing memory fragmentation and enabling even more complex sharing scenarios.

Benefits of VLM: Memory Efficiency and Sharing

VLM offers significant benefits in terms of memory efficiency and sharing. By reducing memory fragmentation, VLM improves memory utilization by three to five times compared to previous systems. This efficient management of the KV cache allows for larger batch sizes with the same amount of memory, resulting in increased throughput and reduced cost per request. Additionally, VLM enables memory sharing across different requests, further optimizing resource allocation.

System Architecture and Implementation of VLM

VLM is implemented as an end-to-end LM serving engine with a distributed model executor and scheduler. The system architecture consists of a centralized engine that manages the block table and GPU workers that handle memory allocation and model execution. VLM is built on top of popular LM frameworks like Hugging Face and uses Ray for cluster management and communication. With its robust system architecture and implementation, VLM delivers high-performance LM serving.

Performance Results and Adoption of VLM

VLM has demonstrated impressive performance results, outperforming existing LM serving solutions. Compared to traditional methods, VLM achieves up to 24x higher throughput and up to 3.5x higher throughput compared to Hugging Face's text generation inference. Its open-source nature has led to widespread adoption, with many companies and projects utilizing VLM for their LM inference workloads. VLM's popularity and mature codebase make it a reliable choice for serving LMs at scale.

How to Get Started with VLM

Getting started with VLM is easy, as it is a Python-based project available for pip installation. By importing the VLM class, You can initialize an LM and feed in Prompts for batch inference. VLM will automatically handle the batching and memory management, providing efficient and high-throughput serving of LMs. Additionally, VLM provides an OpenAI-compatible server, allowing you to easily launch a VLM server and query it like an OpenAI API.

Conclusion

In conclusion, VLM addresses the limitations of traditional LM serving methods by introducing efficient management of the KV cache through virtual memory and paging. VLM provides significant benefits in terms of memory efficiency and sharing, resulting in higher throughput and reduced costs per request. With its robust system architecture, superior performance, and widespread adoption, VLM is a valuable tool for serving LMs at scale.