20231088/vllm

[Doc] Add more tips to avoid OOM (#16765 )

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

2025-04-17 09:54:34 +00:00

21 KiB

Raw Permalink Blame History

(openai-compatible-server)=

OpenAI-Compatible Server

vLLM provides an HTTP server that implements OpenAI's Completions API, Chat API, and more! This functionality lets you serve models and interact with them using an HTTP client.

In your terminal, you can install vLLM, then start the server with the vllm serve command. (You can also use our Docker image.)

vllm serve NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123

To call the server, in your preferred text editor, create a script that uses an HTTP client. Include any messages that you want to send to the model. Then run that script. Below is an example script using the official OpenAI Python client.

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)

:::{tip} vLLM supports some parameters that are not supported by OpenAI, top_k for example. You can pass these parameters to vLLM using the OpenAI client in the extra_body parameter of your requests, i.e. extra_body={"top_k": 50} for top_k. :::

:::{important} By default, the server applies generation_config.json from the Hugging Face model repository if it exists. This means the default values of certain sampling parameters can be overridden by those recommended by the model creator.

To disable this behavior, please pass --generation-config vllm when launching the server. :::

Supported APIs

We currently support the following OpenAI APIs:

Completions API (/v1/completions)
- Only applicable to text generation models (--task generate).
- Note: suffix parameter is not supported.
Chat Completions API (/v1/chat/completions)
- Only applicable to text generation models (--task generate) with a chat template.
- Note: parallel_tool_calls and user parameters are ignored.
Embeddings API (/v1/embeddings)
- Only applicable to embedding models (--task embed).
Transcriptions API (/v1/audio/transcriptions)
- Only applicable to Automatic Speech Recognition (ASR) models (OpenAI Whisper) (--task generate).

In addition, we have the following custom APIs:

Tokenizer API (/tokenize, /detokenize)
- Applicable to any model with a tokenizer.
Pooling API (/pooling)
- Applicable to all pooling models.
Score API (/score)
- Applicable to embedding models and cross-encoder models (--task score).
Re-rank API (/rerank, /v1/rerank, /v2/rerank)
- Implements Jina AI's v1 re-rank API
- Also compatible with Cohere's v1 & v2 re-rank APIs
- Jina and Cohere's APIs are very similar; Jina's includes extra information in the rerank endpoint's response.
- Only applicable to cross-encoder models (--task score).

(chat-template)=

Chat Template

In order for the language model to support chat protocol, vLLM requires the model to include a chat template in its tokenizer configuration. The chat template is a Jinja2 template that specifies how are roles, messages, and other chat-specific tokens are encoded in the input.

An example chat template for NousResearch/Meta-Llama-3-8B-Instruct can be found here

Some models do not provide a chat template even though they are instruction/chat fine-tuned. For those model, you can manually specify their chat template in the --chat-template parameter with the file path to the chat template, or the template in string form. Without a chat template, the server will not be able to process chat and all chat requests will error.

vllm serve <model> --chat-template ./path-to-chat-template.jinja

vLLM community provides a set of chat templates for popular models. You can find them under the gh-dir:examples directory.

With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies both a type and a text field. An example is provided below:

completion = client.chat.completions.create(
  model="NousResearch/Meta-Llama-3-8B-Instruct",
  messages=[
    {"role": "user", "content": [{"type": "text", "text": "Classify this sentiment: vLLM is wonderful!"}]}
  ]
)

Most chat templates for LLMs expect the content field to be a string, but there are some newer models like meta-llama/Llama-Guard-3-1B that expect the content to be formatted according to the OpenAI schema in the request. vLLM provides best-effort support to detect this automatically, which is logged as a string like "Detected the chat template content format to be...", and internally converts incoming requests to match the detected format, which can be one of:

"string": A string.
- Example: "Hello world"
"openai": A list of dictionaries, similar to OpenAI schema.
- Example: [{"type": "text", "text": "Hello world!"}]

If the result is not what you expect, you can set the --chat-template-content-format CLI argument to override which format to use.

Extra Parameters

vLLM supports a set of parameters that are not part of the OpenAI API. In order to use them, you can pass them as extra parameters in the OpenAI client. Or directly merge them into the JSON payload if you are using HTTP call directly.

completion = client.chat.completions.create(
  model="NousResearch/Meta-Llama-3-8B-Instruct",
  messages=[
    {"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"}
  ],
  extra_body={
    "guided_choice": ["positive", "negative"]
  }
)

Extra HTTP Headers

Only X-Request-Id HTTP request header is supported for now. It can be enabled with --enable-request-id-headers.

Note that enablement of the headers can impact performance significantly at high QPS rates. We recommend implementing HTTP headers at the router level (e.g. via Istio), rather than within the vLLM layer for this reason. See this PR for more details.

completion = client.chat.completions.create(
  model="NousResearch/Meta-Llama-3-8B-Instruct",
  messages=[
    {"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"}
  ],
  extra_headers={
    "x-request-id": "sentiment-classification-00001",
  }
)
print(completion._request_id)

completion = client.completions.create(
  model="NousResearch/Meta-Llama-3-8B-Instruct",
  prompt="A robot may not injure a human being",
  extra_headers={
    "x-request-id": "completion-test",
  }
)
print(completion._request_id)

CLI Reference

(vllm-serve)=

`vllm serve`

The vllm serve command is used to launch the OpenAI-compatible server.

:::{tip} The vast majority of command-line arguments are based on those for offline inference.

See here for some common options. :::

:::{argparse} :module: vllm.entrypoints.openai.cli_args :func: create_parser_for_docs :prog: vllm serve :::

Configuration file

You can load CLI arguments via a YAML config file. The argument names must be the long form of those outlined above.

For example:

# config.yaml

model: meta-llama/Llama-3.1-8B-Instruct
host: "127.0.0.1"
port: 6379
uvicorn-log-level: "info"

To use the above config file:

vllm serve --config config.yaml

:::{note} In case an argument is supplied simultaneously using command line and the config file, the value from the command line will take precedence. The order of priorities is command line > config file values > defaults. e.g. vllm serve SOME_MODEL --config config.yaml, SOME_MODEL takes precedence over model in config file. :::

API Reference

(completions-api)=

Completions API

Our Completions API is compatible with OpenAI's Completions API; you can use the official OpenAI Python client to interact with it.

Code example: gh-file:examples/online_serving/openai_completion_client.py

Extra parameters

The following sampling parameters are supported.

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-completion-sampling-params :end-before: end-completion-sampling-params :::

The following extra parameters are supported:

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-completion-extra-params :end-before: end-completion-extra-params :::

(chat-api)=

Chat API

Our Chat API is compatible with OpenAI's Chat Completions API; you can use the official OpenAI Python client to interact with it.

We support both Vision- and Audio-related parameters; see our Multimodal Inputs guide for more information.

Note: image_url.detail parameter is not supported.

Code example: gh-file:examples/online_serving/openai_chat_completion_client.py

Extra parameters

The following sampling parameters are supported.

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-chat-completion-sampling-params :end-before: end-chat-completion-sampling-params :::

The following extra parameters are supported:

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-chat-completion-extra-params :end-before: end-chat-completion-extra-params :::

(embeddings-api)=

Embeddings API

Our Embeddings API is compatible with OpenAI's Embeddings API; you can use the official OpenAI Python client to interact with it.

If the model has a chat template, you can replace inputs with a list of messages (same schema as Chat API) which will be treated as a single prompt to the model.

Code example: gh-file:examples/online_serving/openai_embedding_client.py

You can pass multi-modal inputs to embedding models by defining a custom chat template for the server and passing a list of messages in the request. Refer to the examples below for illustration.

:::::{tab-set} ::::{tab-item} VLM2Vec

To serve the model:

vllm serve TIGER-Lab/VLM2Vec-Full --task embed \
  --trust-remote-code --max-model-len 4096 --chat-template examples/template_vlm2vec.jinja

:::{important} Since VLM2Vec has the same model architecture as Phi-3.5-Vision, we have to explicitly pass --task embed to run this model in embedding mode instead of text generation mode.

The custom chat template is completely different from the original one for this model, and can be found here: gh-file:examples/template_vlm2vec.jinja :::

Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level requests library:

import requests

image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"

response = requests.post(
    "http://localhost:8000/v1/embeddings",
    json={
        "model": "TIGER-Lab/VLM2Vec-Full",
        "messages": [{
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": image_url}},
                {"type": "text", "text": "Represent the given image."},
            ],
        }],
        "encoding_format": "float",
    },
)
response.raise_for_status()
response_json = response.json()
print("Embedding output:", response_json["data"][0]["embedding"])

::::

::::{tab-item} DSE-Qwen2-MRL

To serve the model:

vllm serve MrLight/dse-qwen2-2b-mrl-v1 --task embed \
  --trust-remote-code --max-model-len 8192 --chat-template examples/template_dse_qwen2_vl.jinja

:::{important} Like with VLM2Vec, we have to explicitly pass --task embed.

Additionally, MrLight/dse-qwen2-2b-mrl-v1 requires an EOS token for embeddings, which is handled by a custom chat template: gh-file:examples/template_dse_qwen2_vl.jinja :::

:::{important} MrLight/dse-qwen2-2b-mrl-v1 requires a placeholder image of the minimum image size for text query embeddings. See the full code example below for details. :::

::::

:::::

Full example: gh-file:examples/online_serving/openai_chat_embedding_client_for_multimodal.py

Extra parameters

The following pooling parameters are supported.

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-embedding-pooling-params :end-before: end-embedding-pooling-params :::

The following extra parameters are supported by default:

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-embedding-extra-params :end-before: end-embedding-extra-params :::

For chat-like input (i.e. if messages is passed), these extra parameters are supported instead:

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-chat-embedding-extra-params :end-before: end-chat-embedding-extra-params :::

(transcriptions-api)=

Transcriptions API

Our Transcriptions API is compatible with OpenAI's Transcriptions API; you can use the official OpenAI Python client to interact with it.

:::{note} To use the Transcriptions API, please install with extra audio dependencies using pip install vllm[audio]. :::

Code example: gh-file:examples/online_serving/openai_transcription_client.py

(tokenizer-api)=

Tokenizer API

Our Tokenizer API is a simple wrapper over HuggingFace-style tokenizers. It consists of two endpoints:

/tokenize corresponds to calling tokenizer.encode().
/detokenize corresponds to calling tokenizer.decode().

(pooling-api)=

Pooling API

Our Pooling API encodes input prompts using a pooling model and returns the corresponding hidden states.

The input format is the same as Embeddings API, but the output data can contain an arbitrary nested list, not just a 1-D list of floats.

Code example: gh-file:examples/online_serving/openai_pooling_client.py

(score-api)=

Score API

Our Score API can apply a cross-encoder model or an embedding model to predict scores for sentence pairs. When using an embedding model the score corresponds to the cosine similarity between each embedding pair. Usually, the score for a sentence pair refers to the similarity between two sentences, on a scale of 0 to 1.

You can find the documentation for cross encoder models at sbert.net.

Code example: gh-file:examples/online_serving/openai_cross_encoder_score.py

Single inference

You can pass a string to both text_1 and text_2, forming a single sentence pair.

Request:

curl -X 'POST' \
  'http://127.0.0.1:8000/score' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "BAAI/bge-reranker-v2-m3",
  "encoding_format": "float",
  "text_1": "What is the capital of France?",
  "text_2": "The capital of France is Paris."
}'

Response:

{
  "id": "score-request-id",
  "object": "list",
  "created": 693447,
  "model": "BAAI/bge-reranker-v2-m3",
  "data": [
    {
      "index": 0,
      "object": "score",
      "score": 1
    }
  ],
  "usage": {}
}

Batch inference

You can pass a string to text_1 and a list to text_2, forming multiple sentence pairs where each pair is built from text_1 and a string in text_2. The total number of pairs is len(text_2).

Request:

curl -X 'POST' \
  'http://127.0.0.1:8000/score' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "BAAI/bge-reranker-v2-m3",
  "text_1": "What is the capital of France?",
  "text_2": [
    "The capital of Brazil is Brasilia.",
    "The capital of France is Paris."
  ]
}'

Response:

{
  "id": "score-request-id",
  "object": "list",
  "created": 693570,
  "model": "BAAI/bge-reranker-v2-m3",
  "data": [
    {
      "index": 0,
      "object": "score",
      "score": 0.001094818115234375
    },
    {
      "index": 1,
      "object": "score",
      "score": 1
    }
  ],
  "usage": {}
}

You can pass a list to both text_1 and text_2, forming multiple sentence pairs where each pair is built from a string in text_1 and the corresponding string in text_2 (similar to zip()). The total number of pairs is len(text_2).

Request:

curl -X 'POST' \
  'http://127.0.0.1:8000/score' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "BAAI/bge-reranker-v2-m3",
  "encoding_format": "float",
  "text_1": [
    "What is the capital of Brazil?",
    "What is the capital of France?"
  ],
  "text_2": [
    "The capital of Brazil is Brasilia.",
    "The capital of France is Paris."
  ]
}'

Response:

{
  "id": "score-request-id",
  "object": "list",
  "created": 693447,
  "model": "BAAI/bge-reranker-v2-m3",
  "data": [
    {
      "index": 0,
      "object": "score",
      "score": 1
    },
    {
      "index": 1,
      "object": "score",
      "score": 1
    }
  ],
  "usage": {}
}

Extra parameters

The following pooling parameters are supported.

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-score-pooling-params :end-before: end-score-pooling-params :::

The following extra parameters are supported:

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-score-extra-params :end-before: end-score-extra-params :::

(rerank-api)=

Re-rank API

Our Re-rank API can apply an embedding model or a cross-encoder model to predict relevant scores between a single query, and each of a list of documents. Usually, the score for a sentence pair refers to the similarity between two sentences, on a scale of 0 to 1.

You can find the documentation for cross encoder models at sbert.net.

The rerank endpoints support popular re-rank models such as BAAI/bge-reranker-base and other models supporting the score task. Additionally, /rerank, /v1/rerank, and /v2/rerank endpoints are compatible with both Jina AI's re-rank API interface and Cohere's re-rank API interface to ensure compatibility with popular open-source tools.

Code example: gh-file:examples/online_serving/jinaai_rerank_client.py

Example Request

Note that the top_n request parameter is optional and will default to the length of the documents field. Result documents will be sorted by relevance, and the index property can be used to determine original order.

Request:

curl -X 'POST' \
  'http://127.0.0.1:8000/v1/rerank' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "BAAI/bge-reranker-base",
  "query": "What is the capital of France?",
  "documents": [
    "The capital of Brazil is Brasilia.",
    "The capital of France is Paris.",
    "Horses and cows are both animals"
  ]
}'

Response:

{
  "id": "rerank-fae51b2b664d4ed38f5969b612edff77",
  "model": "BAAI/bge-reranker-base",
  "usage": {
    "total_tokens": 56
  },
  "results": [
    {
      "index": 1,
      "document": {
        "text": "The capital of France is Paris."
      },
      "relevance_score": 0.99853515625
    },
    {
      "index": 0,
      "document": {
        "text": "The capital of Brazil is Brasilia."
      },
      "relevance_score": 0.0005860328674316406
    }
  ]
}

Extra parameters

The following pooling parameters are supported.

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-rerank-pooling-params :end-before: end-rerank-pooling-params :::

The following extra parameters are supported:

:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py :language: python :start-after: begin-rerank-extra-params :end-before: end-rerank-extra-params :::

21 KiB Raw Permalink Blame History

OpenAI-Compatible Server

Supported APIs

Chat Template

Extra Parameters

Extra HTTP Headers

CLI Reference

vllm serve

Configuration file

API Reference

Completions API

Extra parameters

Chat API

Extra parameters

Embeddings API

Multi-modal inputs

Extra parameters

Transcriptions API

Tokenizer API

Pooling API

Score API

Single inference

Batch inference

Extra parameters

Re-rank API

Example Request

Extra parameters

21 KiB

Raw Permalink Blame History

`vllm serve`