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Unverified Commit 59230ef3 authored by Roger Wang's avatar Roger Wang Committed by GitHub
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[Misc] Consolidate example usage of OpenAI client for multimodal models (#9412) 


Co-authored-by: default avatarDarkLight1337 <tlleungac@connect.ust.hk>
parent cee711fd
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docs/source/models/vlm.rst
View file @ 59230ef3
...@@ -241,7 +241,7 @@ To consume the server, you can use the OpenAI client like in the example below: ...@@ -241,7 +241,7 @@ To consume the server, you can use the OpenAI client like in the example below:
print("Chat completion output:", chat_response.choices[0].message.content) print("Chat completion output:", chat_response.choices[0].message.content)
A full code example can be found in `examples/openai_vision_api_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_vision_api_client.py>`_. A full code example can be found in `examples/openai_api_client_for_multimodal.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_api_client_for_multimodal.py>`_.
.. note:: .. note::
......
examples/openai_api_client_for_multimodal.py 0 → 100644
View file @ 59230ef3
"""An example showing how to use vLLM to serve multimodal models
and run online inference with OpenAI client.
Launch the vLLM server with the following command:
(single image inference with Llava)
vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja
(multi-image inference with Phi-3.5-vision-instruct)
vllm serve microsoft/Phi-3.5-vision-instruct --max-model-len 4096 \
--trust-remote-code --limit-mm-per-prompt image=2
(audio inference with Ultravox)
vllm serve fixie-ai/ultravox-v0_3 --max-model-len 4096
"""
import base64
import requests
from openai import OpenAI
from vllm.assets.audio import AudioAsset
from vllm.utils import FlexibleArgumentParser
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def encode_base64_content_from_url(content_url: str) -> str:
"""Encode a content retrieved from a remote url to base64 format."""
with requests.get(content_url) as response:
response.raise_for_status()
result = base64.b64encode(response.content).decode('utf-8')
return result
# Text-only inference
def run_text_only() -> None:
chat_completion = client.chat.completions.create(
messages=[{
"role": "user",
"content": "What's the capital of France?"
}],
model=model,
max_tokens=64,
)
result = chat_completion.choices[0].message.content
print("Chat completion output:", result)
# Single-image input inference
def run_single_image() -> None:
## Use image url in the payload
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"
chat_completion_from_url = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this image?"
},
{
"type": "image_url",
"image_url": {
"url": image_url
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output from image url:", result)
## Use base64 encoded image in the payload
image_base64 = encode_base64_content_from_url(image_url)
chat_completion_from_base64 = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this image?"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_base64}"
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_base64.choices[0].message.content
print("Chat completion output from base64 encoded image:", result)
# Multi-image input inference
def run_multi_image() -> None:
image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"
chat_completion_from_url = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What are the animals in these images?"
},
{
"type": "image_url",
"image_url": {
"url": image_url_duck
},
},
{
"type": "image_url",
"image_url": {
"url": image_url_lion
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output:", result)
# Audio input inference
def run_audio() -> None:
# Any format supported by librosa is supported
audio_url = AudioAsset("winning_call").url
# Use audio url in the payload
chat_completion_from_url = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this audio?"
},
{
"type": "audio_url",
"audio_url": {
"url": audio_url
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output from audio url:", result)
audio_base64 = encode_base64_content_from_url(audio_url)
chat_completion_from_base64 = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this audio?"
},
{
"type": "audio_url",
"audio_url": {
# Any format supported by librosa is supported
"url": f"data:audio/ogg;base64,{audio_base64}"
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_base64.choices[0].message.content
print("Chat completion output from base64 encoded audio:", result)
example_function_map = {
"text-only": run_text_only,
"single-image": run_single_image,
"multi-image": run_multi_image,
"audio": run_audio,
}
def main(args) -> None:
chat_type = args.chat_type
example_function_map[chat_type]()
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description='Demo on using OpenAI client for online inference with '
'multimodal language models served with vLLM.')
parser.add_argument(
'--chat-type',
'-c',
type=str,
default="single-image",
choices=["text-only", "single-image", "multi-image", "audio"],
help='Conversation type with multimodal data.')
args = parser.parse_args()
main(args)
examples/openai_audio_api_client.py deleted 100644 → 0
View file @ cee711fd
"""An example showing how to use vLLM to serve VLMs.
Launch the vLLM server with the following command:
vllm serve fixie-ai/ultravox-v0_3
"""
import base64
import requests
from openai import OpenAI
from vllm.assets.audio import AudioAsset
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
# Any format supported by librosa is supported
audio_url = AudioAsset("winning_call").url
# Use audio url in the payload
chat_completion_from_url = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this audio?"
},
{
"type": "audio_url",
"audio_url": {
"url": audio_url
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_url.choices[0].message.content
print(f"Chat completion output:{result}")
# Use base64 encoded audio in the payload
def encode_audio_base64_from_url(audio_url: str) -> str:
"""Encode an audio retrieved from a remote url to base64 format."""
with requests.get(audio_url) as response:
response.raise_for_status()
result = base64.b64encode(response.content).decode('utf-8')
return result
audio_base64 = encode_audio_base64_from_url(audio_url=audio_url)
chat_completion_from_base64 = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this audio?"
},
{
"type": "audio_url",
"audio_url": {
# Any format supported by librosa is supported
"url": f"data:audio/ogg;base64,{audio_base64}"
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_base64.choices[0].message.content
print(f"Chat completion output:{result}")
examples/openai_vision_api_client.py deleted 100644 → 0
View file @ cee711fd
"""An example showing how to use vLLM to serve VLMs.
Launch the vLLM server with the following command:
(single image inference with Llava)
vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja
(multi-image inference with Phi-3.5-vision-instruct)
vllm serve microsoft/Phi-3.5-vision-instruct --max-model-len 4096 \
--trust-remote-code --limit-mm-per-prompt image=2
"""
import base64
import requests
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
# Single-image input inference
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"
## Use image url in the payload
chat_completion_from_url = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this image?"
},
{
"type": "image_url",
"image_url": {
"url": image_url
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output:", result)
## Use base64 encoded image in the payload
def encode_image_base64_from_url(image_url: str) -> str:
"""Encode an image retrieved from a remote url to base64 format."""
with requests.get(image_url) as response:
response.raise_for_status()
result = base64.b64encode(response.content).decode('utf-8')
return result
image_base64 = encode_image_base64_from_url(image_url=image_url)
chat_completion_from_base64 = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What's in this image?"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_base64}"
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_base64.choices[0].message.content
print(f"Chat completion output:{result}")
# Multi-image input inference
image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"
chat_completion_from_url = client.chat.completions.create(
messages=[{
"role":
"user",
"content": [
{
"type": "text",
"text": "What are the animals in these images?"
},
{
"type": "image_url",
"image_url": {
"url": image_url_duck
},
},
{
"type": "image_url",
"image_url": {
"url": image_url_lion
},
},
],
}],
model=model,
max_tokens=64,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output:", result)
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