What is vLLM? 

Nowadays large language models (LLMs) have revolutionized various domains. However, deploying these models in real-world applications can be challenging due to their high computational demands. This is where vLLM steps in. vLLM stands for Virtual Large Language Model and is an active open-source library that supports LLMs in inferencing and model serving efficiently.

vLLM was first introduced in a paper - Efficient Memory Management for Large Language Model Serving with PagedAttention, authored by Kwon et al. The paper identifies that the challenges faced when serving LLMs are memory allocation and measures their impact on performance. Specifically, it emphasizes the inefficiency of managing Key-Value (KV) cache memory in current LLM serving systems. These limitations can often result in slow inference speed and high memory footprint.

To address this, the paper presents PagedAttention, an attention algorithm inspired by virtual memory and paging techniques commonly used in operating systems. PagedAttention enables efficient memory management by allowing for non-contiguous storage of attention keys and values. Following this idea, the paper develops vLLM, a high-throughput distributed LLM serving engine that is built on PagedAttention. vLLM achieves near-zero waste in KV cache memory, significantly improving serving performance. Moreover, leveraging techniques like virtual memory and copy-on-write, vLLM efficiently manages the KV cache and handles various decoding algorithms. This results in 2-4 times throughput improvements compared to state-of-the-art systems such as FasterTransformer and Orca. This improvement is especially noticeable with longer sequences, larger models, and complex decoding algorithms.

What is the core idea in vLLM?

PagedAttention

The attention mechanism allows LLMs to focus on relevant parts of the input sequence while generating output/response. Inside the attention mechanism, the attention scores for all input tokens need to be calculated. Existing systems store KV pairs in contiguous memory spaces, limiting memory sharing and leading to inefficient memory management.

PagedAttention is an attention algorithm inspired by the concept of paging in operating systems. It allows storing continuous KV pairs in non-contiguous memory space by partitioning the KV cache of each sequence into KV block tables. This way, it enables the flexible management of KV vectors across layers and attention heads within a layer in separate block tables, thus optimizing memory usage, reducing fragmentation, and minimizing redundant duplication.

What are the other techniques used in vLLM for efficient serving?

vLLM doesn't stop at PagedAttention. It incorporates a suite of techniques to further optimize LLM serving.

• Continuous Batching: Incoming requests are continuously batched together to maximize hardware utilization and reduce computing waste, minimizing idle time.

• Quantization: vLLM utilizes quantization techniques like FP16 to optimize memory usage by representing the KV cache in reduced precision, leading to smaller memory footprints and faster computations.

• Optimized CUDA Kernels: vLLM hand-tunes the code executed on the GPU for maximum performance. For example, for fused reshape and block write, optimized kernels are developed to split the new KV cache into blocks, reshape them for efficient memory access, and save them based on a block table, all fused into a single kernel to reduce overheads. 

How to use vLLM?

vLLM is easy-to-use. Here is a glimpse into how it can be used in Python:

One can install vLLM via pip:

# (Recommended) Create a new conda environment.
conda create -n myenv python=3.9 -y
conda activate myenv

# Install vLLM with CUDA 12.1.
pip install vllm

Offline inference

Then import the vLLM module into your code and do an offline inference with vLLM’s engine. The LLM class is to initialize the vLLM engine with a specific built-in LLM model. The LLM models are by default downloaded from HuggingFace. The SamplingParams class is to set the parameters for inferencing.

from vllm import LLM, SamplingParams

Then we define an input sequence and set the sampling parameters. Initialize vLLM’s engine for offline inference with the LLM class and an LLM model:

prompts = ["The future of humanity is"]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")

Finally, the output/response can be generated by:

Responses = llm.generate(prompts, sampling_params)
print(f"Prompt: { Responses[0].prompt!r}, Generated text: { Responses[0].outputs[0].text!r}")

The code example can be found here.

Online serving

To use vLLM for online serving, OpenAI’s completions and APIs can be used in vLLM. The server can be started with Python:

python -m vllm.entrypoints.openai.api_server --model 
NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key 
token-abc123

To call the server, the official OpenAI Python client library can be used. Alternatively, any other HTTP client works as well.

from openai import OpenAI
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="token-abc123",
)

completion = client.chat.completions.create(
  model="NousResearch/Meta-Llama-3-8B-Instruct",
  messages=[
    {"role": "user", "content": "Hello!"}
  ]
)

print(completion.choices[0].message)

More examples can be found on the official vLLM documentation. 

What are the use cases of vLLM?

vLLM's efficient operation of LLMs opens numerous practical applications. Here are some compelling scenarios that highlight vLLM's potential:

• Revolutionizing Chatbots and Virtual Assistants: With its efficient serving support, vLLM can contribute chatbots and virtual assistants to hold nuanced conversations, understand complex requests, and respond with human-like empathy. By enabling faster response times and lower latency, vLLM ensures smoother interactions. Additionally, vLLM empowers chatbots to access and process vast amounts of information, allowing them to provide users with comprehensive and informative answers. vLLM's ability to handle diverse creative text formats can be harnessed to craft personalized responses that address the user's specific needs and preferences. This combination of speed, knowledge, and adaptability can transform chatbots from simple FAQ machines into invaluable tools for customer service, technical support, and even emotional counseling.

• Democratizing Code Generation and Programming Assistance: The field of software development is constantly evolving, and keeping pace with the latest technologies can be challenging. vLLM can act as a valuable companion for programmers of all experience levels. By leveraging its code-understanding capabilities, vLLM can suggest code completions, identify potential errors, and even recommend alternative solutions to coding problems. This can significantly reduce development time and improve code quality. vLLM's ability to generate documentation can also alleviate a major pain point for developers. Automatically generating clear and concise documentation based on the written code would save developers valuable time and effort, and the quality and consistency of the documentation can also be controlled. vLLM can be used to create educational tools that introduce coding concepts in a fun and interactive way, making programming more accessible to students and aspiring developers.

Summary

vLLM addresses a critical bottleneck in LLM deployment: inefficient inferencing and serving. Using the innovative PagedAttention technique, vLLM optimizes memory usage during the core attention operation, leading to significant performance gains. This translates to faster inference speeds and the ability to run LLMs on resource-constrained hardware. Beyond raw performance, vLLM offers advantages like scalability and cost-efficiency. With its open-source nature and commitment to advancement, vLLM positions itself as a key player in the future of LLM technology.