In the vast domain of artificial intelligence, the deployment of Large Language Models (LLMs) is not merely about the sheer might of these models; it equally hinges on efficiency, speed, and real-time applicability. Addressing these critical facets is where vLLM, an open-source library developed at UC Berkeley, takes center stage. It stands as a remarkable solution optimized for high throughput serving of LLMs while ensuring memory efficiency – a crucial factor for modern AI applications.
The Essence of vLLM
At its core, vLLM distinguishes itself by prioritizing speed, efficiency, and adaptability. Unlike conventional LLM optimization platforms, vLLM's design philosophy revolves around the principles of versatility and practical utility. Its foundation is grounded in groundbreaking research, with a practical approach geared toward maximizing efficiency.
Moreover, vLLM's seamless integration with popular Hugging Face models not only simplifies the deployment process but also allows users to explore a wide array of architectures. The platform's dynamic memory allocation, facilitated by intelligent continuous batching, demonstrates its commitment to optimizing GPU memory usage. By making astute real-time decisions on memory allocation, vLLM minimizes waste, ensuring the most efficient utilization of available resources.
Dynamic Memory Management
One of the key features of vLLM lies in its dynamic memory management approach. It differentiates between logical and physical key-value (KV) blocks, allowing for dynamic allocation of memory. By allocating memory only when necessary for decoding operations, vLLM eliminates waste and maximizes resource utilization. This separation enables the system to grow the KV cache memory dynamically, avoiding the need for upfront allocation for all positions, thereby optimizing memory usage.
Support for Diverse Decoding Algorithms
vLLM stands out for its support of various decoding algorithms such as parallel sampling, beam search, and shared prefix, making it highly adaptable to different decoding requirements. The system incorporates methods like fork, append, and free to efficiently create, expand, and manage sequences. For instance, in parallel sampling, vLLM can generate multiple output sequences from a single input, optimizing memory sharing and usage among these sequences.
Optimized Implementation and Kernel-Level Operations
Behind the scenes, vLLM utilizes specialized GPU-based inference engines and custom CUDA kernels for essential operations like PagedAttention. These optimized kernels reduce kernel launch overheads and ensure efficient memory access, enhancing overall system performance. These optimizations are crucial in handling the intricate memory access patterns that arise during language model serving.
Distributed Execution for Scalability
For larger language models, vLLM can distribute the workload across multiple GPUs, employing an SPMD execution schedule to synchronize execution and manage memory efficiently. This distributed set-up enables model parallelism, allowing synchronized execution across GPU workers and effective memory management, thereby enhancing overall performance.
Pre-emption and Recovery Strategies
In scenarios where system capacity is exceeded, vLLM employs preemptive strategies like swapping and recomputation to handle memory constraints efficiently. These strategies aim to optimize memory usage by swapping portions of memory to CPU RAM or recomputing the KV cache when sequences are rescheduled, ensuring efficient operation even under heavy loads.
PagedAttention
PagedAttention stands out as the core strength of vLLM, revolutionizing LLM memory management. Unlike conventional LLMs that use contiguous memory allocation, PagedAttention employs a non-contiguous memory storage approach, facilitating dynamic, on-the-fly allocations. This method significantly reduces memory wastage and enables efficient attention computations over varied memory ranges.
Continuous Batching and Iteration-Level Scheduling
Continuous batching and iteration-level scheduling are pivotal to vLLM's optimized LLM serving. Unlike static batching, vLLM's dynamic batching adjusts based on real-time requirements, ensuring maximum compute resource utilization. This approach results in faster response times and enhanced scalability for LLMs, particularly in scenarios demanding high throughput and low latency.
Tutorial - Using vLLM on E2E Cloud
If you require extra GPU resources for the tutorials ahead, you can explore the offerings on E2E CLOUD. We provide a diverse selection of GPUs, making them a suitable choice for more advanced LLM-based applications.
To get one, head over to MyAccount, and sign up. Then launch a GPU node as is shown in the screenshot below:
Make sure you add your ssh keys during launch, or through the security tab after launching. Once you have launched a node, you can use VSCode Remote Explorer to ssh into the node and use it as a local development environment.
This tutorial guides you through using the vLLM library with the GPT-2 model to generate text based on provided prompts.
Step 1: Install the Required LibraryIf you haven't installed the vLLM library yet, you can install it using
Step 2: Import the Necessary LibrariesImport the LLM class from the vLLM library to use the language model.
Step 3: Define the PromptsCreate a list of prompts for which you want the language model to generate text. For example:
Step 4: Load the Language ModelInitialize an instance of the LLM class, specifying the GPT-2 model.
Step 5: Generate the Text Based on PromptsUse the loaded language model (llm) to generate text corresponding to the provided prompts.
Step 6: Display the Generated TextLoop through the generated outputs to display the texts corresponding to each prompt.
This loop retrieves and prints the original prompt along with the generated text for each prompt provided. Exploring other Hugging Face models is a recommended endeavor. vLLM seamlessly integrates with a wide array of Hugging Face models, encompassing various architectures such as:
- Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
- Baichuan (baichuan-inc/Baichuan-7B, baichuan-inc/Baichuan-13B-Chat, etc.)
- BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
- ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
- Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
- GPT-2 (gpt2, gpt2-xl, etc.)
- GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
- GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
- InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
- LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
- Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
- MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
- Phi-1.5 (microsoft/phi-1_5, etc.)
- Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
- Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
This tutorial covered the usage of the Langchain and vLLM libraries to generate text from prompts, employing a language model and advanced manipulation techniques to produce informative responses. You can further customize and expand these functionalities for your specific needs.
Prerequisites
Python Installation: Ensure you have Python installed on your system.
Installation of the Required Libraries: Run the following commands to install the necessary libraries:
Step 1: Import the Libraries
Step 2: Load Language Model and Generate Text
This code segment demonstrates loading the language model, defining prompts, specifying sampling parameters, generating text based on the prompts, and displaying the generated text.
The ‘sampling_params’ variable in this code snippet defines parameters used during text generation. These parameters control the style and randomness of the generated text.
- Temperature: It determines the level of randomness in the generated text. A lower temperature makes the model more conservative and likely to generate more predictable text, while a higher temperature introduces more randomness and diversity in the generated output.
- Top-p sampling: It's a technique used to control the diversity of the generated text. It restricts the choice of tokens by considering only the most probable tokens whose cumulative probability exceeds a certain threshold (‘top_p’). This helps in preventing the generation of overly diverse or improbable text.
Step 3: Advanced Prompt Generation and Text Manipulation
This part of the code includes functions to generate text based on more complex prompts and manipulate the generated text.
- get_prompt: Constructs a system and user conversation prompt.
- cut_off_text: Cuts off text based on a provided phrase.
- remove_substring: Removes a specified substring from a string.
- generate: Generates text based on a given prompt.
- parse_text: Formats and displays the generated text.
Real-World Implications and Future Prospects
The implications of vLLM extend far beyond theoretical advancements. In practical terms, the adoption of vLLM can lead to substantial improvements in various industries. Businesses relying on NLP-driven applications can benefit from enhanced performance, reduced infrastructure costs, and improved scalability.
Looking ahead, the evolution of vLLM holds promise for further innovations in language model serving. Continued research and development in vectorized computation techniques could lead to even greater efficiency gains, making sophisticated language models more accessible and practical for diverse applications.
Benchmarks: An Overview
In the vLLM blog on GitHub, the efficiency of vLLM is measured concerning two benchmarks: Hugging Face Transformers (HF), a widely used LLM library, and Hugging Face Text Generation Inference (TGI), the previous leading technology. The evaluations were conducted under two configurations: LLaMA-7B using an NVIDIA A10G GPU and LLaMA-13B employing an NVIDIA A100 GPU (40GB). The input/output lengths of the requests are randomly chosen from the ShareGPT dataset. The experimentation reveals that vLLM demonstrates substantially superior performance, achieving up to a 24-fold increase in throughput compared to HF and up to 3.5 times higher throughput than TGI.
Conclusion
In the dynamic landscape of Large Language Models, vLLM emerges not only as a technical marvel but also as an indispensable asset for real-world applications. Its unique features, from PagedAttention to dynamic batching, underscore its technical prowess and practical relevance. The success stories and benchmarks highlight vLLM's efficacy in optimizing LLM throughput, making it a compelling choice for anyone keen on leveraging the true potential of Large Language Models in practical scenarios. With the backing of platforms like E2E Cloud, vLLM's capabilities can be harnessed seamlessly, ensuring optimal LLM serving performance without the complexities of infrastructure management.
References
Research Paper: Efficient Memory Management for Large Language Model Serving with PagedAttention
Github Repository: vLLM
Documentation: vLLM
Blog: vLLM Blog
Blog: Understanding vLLM for Increasing LLM Throughput
