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Usage:
python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --tp-size=8
python3 llava_onevision_server.py
"""
import io
import os
import sys
import time
import numpy as np
import openai
import pybase64
import requests
from decord import VideoReader, cpu
from PIL import Image
# pip install httpx==0.23.3
# pip install decord
# pip install protobuf==3.20.0
def download_video(url, cache_dir):
file_path = os.path.join(cache_dir, "jobs.mp4")
os.makedirs(cache_dir, exist_ok=True)
response = requests.get(url)
response.raise_for_status()
with open(file_path, "wb") as f:
f.write(response.content)
print(f"File downloaded and saved to: {file_path}")
return file_path
def create_openai_client(base_url):
return openai.Client(api_key="EMPTY", base_url=base_url)
def image_stream_request_test(client):
print("----------------------Image Stream Request Test----------------------")
stream_request = client.chat.completions.create(
model="default",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"
},
},
{
"type": "text",
"text": "Please describe this image. Please list the benchmarks and the models.",
},
],
},
],
temperature=0.7,
max_tokens=1024,
stream=True,
)
stream_response = ""
for chunk in stream_request:
if chunk.choices[0].delta.content is not None:
content = chunk.choices[0].delta.content
stream_response += content
sys.stdout.write(content)
sys.stdout.flush()
print("-" * 30)
def multi_image_stream_request_test(client):
print(
"----------------------Multi-Images Stream Request Test----------------------"
)
stream_request = client.chat.completions.create(
model="default",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"
},
"modalities": "multi-images",
},
{
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/sgl-project/sglang/main/examples/assets/example_image.png"
},
"modalities": "multi-images",
},
{
"type": "text",
"text": "I have shown you two images. Please describe the two images to me.",
},
],
},
],
temperature=0.7,
max_tokens=1024,
stream=True,
)
stream_response = ""
for chunk in stream_request:
if chunk.choices[0].delta.content is not None:
content = chunk.choices[0].delta.content
stream_response += content
sys.stdout.write(content)
sys.stdout.flush()
print("-" * 30)
def video_stream_request_test(client, video_path):
print("------------------------Video Stream Request Test----------------------")
messages = prepare_video_messages(video_path)
video_request = client.chat.completions.create(
model="default",
messages=messages,
temperature=0,
max_tokens=1024,
stream=True,
)
print("-" * 30)
video_response = ""
for chunk in video_request:
if chunk.choices[0].delta.content is not None:
content = chunk.choices[0].delta.content
video_response += content
sys.stdout.write(content)
sys.stdout.flush()
print("-" * 30)
def image_speed_test(client):
print("----------------------Image Speed Test----------------------")
start_time = time.perf_counter()
request = client.chat.completions.create(
model="default",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"
},
},
{
"type": "text",
"text": "Please describe this image. Please list the benchmarks and the models.",
},
],
},
],
temperature=0,
max_tokens=1024,
)
end_time = time.perf_counter()
response = request.choices[0].message.content
print(response)
print("-" * 30)
print_speed_test_results(request, start_time, end_time)
def video_speed_test(client, video_path):
print("------------------------Video Speed Test------------------------")
messages = prepare_video_messages(video_path)
start_time = time.perf_counter()
video_request = client.chat.completions.create(
model="default",
messages=messages,
temperature=0,
max_tokens=1024,
)
end_time = time.perf_counter()
video_response = video_request.choices[0].message.content
print(video_response)
print("-" * 30)
print_speed_test_results(video_request, start_time, end_time)
def prepare_video_messages(video_path):
max_frames_num = 32
vr = VideoReader(video_path, ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(
0, total_frame_num - 1, max_frames_num, dtype=int
)
frame_idx = uniform_sampled_frames.tolist()
frames = vr.get_batch(frame_idx).asnumpy()
base64_frames = []
for frame in frames:
pil_img = Image.fromarray(frame)
buff = io.BytesIO()
pil_img.save(buff, format="JPEG")
base64_str = pybase64.b64encode(buff.getvalue()).decode("utf-8")
base64_frames.append(base64_str)
messages = [{"role": "user", "content": []}]
for base64_frame in base64_frames:
frame_format = {
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{base64_frame}"},
"modalities": "video",
}
messages[0]["content"].append(frame_format)
prompt = {"type": "text", "text": "Please describe the video in detail."}
messages[0]["content"].append(prompt)
return messages
def print_speed_test_results(request, start_time, end_time):
total_tokens = request.usage.total_tokens
completion_tokens = request.usage.completion_tokens
prompt_tokens = request.usage.prompt_tokens
print(f"Total tokens: {total_tokens}")
print(f"Completion tokens: {completion_tokens}")
print(f"Prompt tokens: {prompt_tokens}")
print(f"Time taken: {end_time - start_time} seconds")
print(f"Token per second: {total_tokens / (end_time - start_time)}")
print(f"Completion token per second: {completion_tokens / (end_time - start_time)}")
print(f"Prompt token per second: {prompt_tokens / (end_time - start_time)}")
def main():
url = "https://raw.githubusercontent.com/EvolvingLMMs-Lab/sglang/dev/onevision_local/assets/jobs.mp4"
cache_dir = os.path.expanduser("~/.cache")
video_path = download_video(url, cache_dir)
client = create_openai_client("http://127.0.0.1:30000/v1")
image_stream_request_test(client)
multi_image_stream_request_test(client)
video_stream_request_test(client, video_path)
image_speed_test(client)
video_speed_test(client, video_path)
if __name__ == "__main__":
main()
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