itsafire/worker-vllm

The RunPod worker template for serving our large language model endpoints. Powered by VLLM.

vLLM Serverless Endpoint Worker

Deploy Blazing-fast LLMs powered by vLLM on RunPod Serverless in a few clicks.

Worker vLLM 0.2.0 - What's New

• You no longer need a linux-based machine or NVIDIA GPUs to build the worker.
• Over 3x lighter Docker image size.
• OpenAI Chat Completion output format (optional to use).
• Extremely fast image build time.
• Docker Secrets-protected Hugging Face token support for building the image with a model baked in without exposing your token.
• Support for n and best_of sampling parameters, which allow you to generate multiple responses from a single prompt.
• New environment variables for various configuration.
• vLLM Version: 0.2.7

Table of Contents

• Setting up the Serverless Worker
  • Option 1: Deploy Any Model Using Pre-Built Docker Image [RECOMMENDED]
    • Prerequisites
    • Environment Variables
  • Option 2: Build Docker Image with Model Inside
    • Arguments
    • Example: Building an image with OpenChat-3.5
      • (Optional) Including Huggingface Token
  • Compatible Models
• Usage
  • Endpoint Model Inputs
  • Text Input Formats
    • 1. prompt
    • 2. messages
  • Sampling Parameters

Setting up the Serverless Worker

Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]

We now offer a pre-built Docker Image for the vLLM Worker that you can configure entirely with Environment Variables when creating the RunPod Serverless Endpoint:

Stable Image: runpod/worker-vllm:0.2.2

Development Image: runpod/worker-vllm:dev

Prerequisites

• RunPod Account

Environment Variables

Required:

• MODEL_NAME: Hugging Face Model Repository (e.g., openchat/openchat-3.5-1210).

Optional:

• LLM Settings:

  • MODEL_REVISION: Model revision to load (default: None).
  • MAX_MODEL_LENGTH: Maximum number of tokens for the engine to be able to handle. (default: maximum supported by the model)
  • BASE_PATH: Storage directory where huggingface cache and model will be located. (default: /runpod-volume, which will utilize network storage if you attach it or create a local directory within the image if you don't)
  • LOAD_FORMAT: Format to load model in (default: auto).
  • HF_TOKEN: Hugging Face token for private and gated models (e.g., Llama, Falcon).
  • QUANTIZATION: AWQ (awq), SqueezeLLM (squeezellm) or GPTQ (gptq) Quantization. The specified Model Repo must be of a quantized model. (default: None)
  • TRUST_REMOTE_CODE: Trust remote code for Hugging Face (default: 0)

• Tokenizer Settings:

  • TOKENIZER_NAME: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: None, which uses the model's tokenizer)
  • TOKENIZER_REVISION: Tokenizer revision to load (default: None).
  • CUSTOM_CHAT_TEMPLATE: Custom chat jinja template, read more about Hugging Face chat templates here. (default: None)

• Tensor Parallelism: Note that the more GPUs you split a model's weights accross, the slower it will be due to inter-GPU communication overhead. If you can fit the model on a single GPU, it is recommended to do so.

  • TENSOR_PARALLEL_SIZE: Number of GPUs to shard the model across (default: 1).
  • If you are having issues loading your model with Tensor Parallelism, try decreasing VLLM_CPU_FRACTION (default: 1).

• System Settings:

  • GPU_MEMORY_UTILIZATION: GPU VRAM utilization (default: 0.98).
  • MAX_PARALLEL_LOADING_WORKERS: Maximum number of parallel workers for loading models, for non-Tensor Parallel only. (default: number of available CPU cores if TENSOR_PARALLEL_SIZE is 1, otherwise None).

• Serverless Settings:

  • MAX_CONCURRENCY: Max concurrent requests. (default: 100)
  • DEFAULT_BATCH_SIZE: Token streaming batch size (default: 30). This reduces the number of HTTP calls, increasing speed 8-10x vs non-batching, matching non-streaming performance.
  • ALLOW_OPENAI_FORMAT: Whether to allow users to specify use_openai_format to get output in OpenAI format. (default: 1)
  • DISABLE_LOG_STATS: Enable (0) or disable (1) vLLM stats logging.
  • DISABLE_LOG_REQUESTS: Enable (0) or disable (1) request logging.

Option 2: Build Docker Image with Model Inside

To build an image with the model baked in, you must specify the following docker arguments when building the image.

Prerequisites

• RunPod Account
• Docker

Arguments:

• Required
  • MODEL_NAME
• Optional
  • MODEL_REVISION: Model revision to load (default: main).
  • BASE_PATH: Storage directory where huggingface cache and model will be located. (default: /runpod-volume, which will utilize network storage if you attach it or create a local directory within the image if you don't. If your intention is to bake the model into the image, you should set this to something like /models to make sure there are no issues if you were to accidentally attach network storage.)
  • QUANTIZATION
  • WORKER_CUDA_VERSION: 11.8.0 or 12.1.0 (default: 11.8.0 due to a small amount of workers not having CUDA 12.1 support yet. 12.1.0 is recommended for optimal performance).
  • TOKENIZER_NAME: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: None, which uses the model's tokenizer)
  • TOKENIZER_REVISION: Tokenizer revision to load (default: main).

For the remaining settings, you may apply them as environment variables when running the container. Supported environment variables are listed in the Environment Variables section.

Example: Building an image with OpenChat-3.5

sudo docker build -t username/image:tag --build-arg MODEL_NAME="openchat/openchat_3.5" --build-arg BASE_PATH="/models" .

(Optional) Including Huggingface Token

If the model you would like to deploy is private or gated, you will need to include it during build time as a Docker secret, which will protect it from being exposed in the image and on DockerHub.

1. Enable Docker BuildKit (required for secrets).

export DOCKER_BUILDKIT=1

2. Export your Hugging Face token as an environment variable

export HF_TOKEN="your_token_here"

2. Add the token as a secret when building

docker build -t username/image:tag --secret id=HF_TOKEN --build-arg MODEL_NAME="openchat/openchat_3.5" .

Compatible Model Architectures

• Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
• Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
• Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
• LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
• Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
• StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
• Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
• Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
• Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
• Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
• BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
• ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
• DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, 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.)
• MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
• OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)

Usage

Endpoint Model Inputs

You may either use a prompt or a list of messages as input. If you use messages, the model's chat template will be applied to the messages automatically, so the model must have one. If you use prompt, you may optionally apply the model's chat template to the prompt by setting apply_chat_template to true.

Argument	Type	Default	Description
prompt	str		Prompt string to generate text based on.
messages	list[dict[str, str]]		List of messages, which will automatically have the model's chat template applied. Overrides prompt.
use_openai_format	bool	False	Whether to return output in OpenAI format. ALLOW_OPENAI_FORMAT environment variable must be 1, the input should preferably be a messages list, but prompt is accepted.
apply_chat_template	bool	False	Whether to apply the model's chat template to the prompt.
sampling_params	dict	{}	Sampling parameters to control the generation, like temperature, top_p, etc.
stream	bool	False	Whether to enable streaming of output. If True, responses are streamed as they are generated.
batch_size	int	DEFAULT_BATCH_SIZE	The number of tokens to stream every HTTP POST call.

Text Input Formats

You may either use a prompt or a list of messages as input.

1. prompt

The prompt string can be any string, and the model's chat template will not be applied to it unless apply_chat_template is set to true, in which case it will be treated as a user message.

Example:

"prompt": "..."

2. messages

Your list can contain any number of messages, and each message can have any role from the following list:

• user
• assistant
• system

The model's chat template will be applied to the messages automatically, so the model must have one.

Example:

"messages": [
    {
      "role": "system",
      "content": "..."
    },
    {
      "role": "user",
      "content": "..."
    },
    {
      "role": "assistant",
      "content": "..."
    }
  ]

Sampling Parameters

Argument	Type	Default	Description
n	int	1	Number of output sequences generated from the prompt. The top n sequences are returned.
best_of	Optional[int]	n	Number of output sequences generated from the prompt. The top n sequences are returned from these best_of sequences. Must be ≥ n. Treated as beam width in beam search. Default is n.
presence_penalty	float	0.0	Penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
frequency_penalty	float	0.0	Penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
repetition_penalty	float	1.0	Penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens, values < 1 encourage repetition.
temperature	float	1.0	Controls the randomness of sampling. Lower values make it more deterministic, higher values make it more random. Zero means greedy sampling.
top_p	float	1.0	Controls the cumulative probability of top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k	int	-1	Controls the number of top tokens to consider. Set to -1 to consider all tokens.
min_p	float	0.0	Represents the minimum probability for a token to be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.
use_beam_search	bool	False	Whether to use beam search instead of sampling.
length_penalty	float	1.0	Penalizes sequences based on their length. Used in beam search.
early_stopping	Union[bool, str]	False	Controls stopping condition in beam search. Can be True, False, or "never".
stop	Union[None, str, List[str]]	None	List of strings that stop generation when produced. Output will not contain these strings.
stop_token_ids	Optional[List[int]]	None	List of token IDs that stop generation when produced. Output contains these tokens unless they are special tokens.
ignore_eos	bool	False	Whether to ignore the End-Of-Sequence token and continue generating tokens after its generation.
max_tokens	int	16	Maximum number of tokens to generate per output sequence.
skip_special_tokens	bool	True	Whether to skip special tokens in the output.
spaces_between_special_tokens	bool	True	Whether to add spaces between special tokens in the output.