worker-vllm/README.md

vLLM Endpoint | Serverless Worker

 

🚀 | This serverless worker utilizes vLLM behind the scenes and is integrated into RunPod's serverless environment. It supports dynamic auto-scaling using the built-in RunPod autoscaling feature.

Setting up the Serverless Worker

Option 1: Use Pre-Built Image

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: runpod/worker-vllm

Environment Variables

Required:

• MODEL_NAME: the Hugging Face model to use.

Optional:

• MODEL_BASE_PATH: directory to store the model in
• HF_TOKEN: your Hugging Face token to access private or gated models, such as Llama, Falcon, etc.
• NUM_GPU_SHARD: Number of GPUs to split the model across.
• QUANTIZATION: awq to use AWQ Quantization (Base model must be in AWQ format). squeezellm for SqueezeLLM quantization - preliminary
• VLLM_N_CPUS: due to Serverless Endpoints having CPU-burst enabled, multi-gpu might not work correctly unless the number of CPUs is limited. It is set to 10 by default.
• CONCURRENCY_MODIFIER: limit of concurrent requests per worker.
• DEFAULT_BATCH_SIZE: default batch size for token streaming to reduce the number of http calls and speed up streaming. Defaults to 10.
• DISABLE_LOG_STATS: set to True or False to enable/disable vLLM stats logging.

Option 2: Build Image with Model Inside

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

Required:

• MODEL_NAME
• MODEL_BASE_PATH

Optional:

• QUANTIZATION
• HF_TOKEN
• CUDA_VERSION: 11.8.0 or 12.1.0. Defaults to 11.8.0

Compatible Models

• LLaMA & LLaMA-2
• Mistral
• Mixtral (Mistral MoE)
• Yi
• ChatGLM
• Phi
• MPT
• OPT
• Qwen
• Aquila & Aquila2
• Baichuan
• BLOOM
• Falcon
• GPT-2
• GPT BigCode
• GPT-J
• GPT-NeoX
• InternLM

And any other models supported by vLLM 0.2.4.

Ensure that you have Docker installed and properly set up before running the docker build commands. Once built, you can deploy this serverless worker in your desired environment with confidence that it will automatically scale based on demand. For further inquiries or assistance, feel free to contact our support team.

Model Inputs

Argument	Type	Default	Description
prompt	str		Prompt string to generate text based on.
sampling_params	dict	{}	Sampling parameters to control the generation, like temperature, top_p, etc.
streaming	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 responses to generate in one batch. Only applicable

Sampling Parameters

Argument	Type	Default	Description
n	int	1	Number of output sequences to return for the given prompt.
best_of	Optional[int]	None	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.
logprobs	Optional[int]	None	Number of log probabilities to return per output token.
prompt_logprobs	Optional[int]	None	Number of log probabilities to return per prompt token.
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.

Test Inputs

The following inputs can be used for testing the model:

{
    "input": {
       "prompt": "Why is RunPod the best platform?",
       "sampling_params": {
           "max_tokens": 100
       }
    }
}