[Feat/WIP] add llava-onevision, with support for (1) siglip encoder, (2) qwen2 decoder (3) openai api compatible server. (#1123)

Co-authored-by: Bo Li <drluodian@gmail.com>

examples/usage/llava/http_llava_onevision_test.py

@@ -0,0 +1,211 @@
+import base64
+import io
+import os
+import sys
+import time
+
+import numpy as np
+import openai
+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 video_stream_request_test(client, video_path):
+    print("------------------------Video Stream Request Test----------------------")
+    messages = prepare_video_messages(video_path)
+
+    start_time = time.time()
+    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.time()
+    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.time()
+    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.time()
+    video_request = client.chat.completions.create(
+        model="default",
+        messages=messages,
+        temperature=0,
+        max_tokens=1024,
+    )
+    end_time = time.time()
+    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 = base64.b64encode(buff.getvalue()).decode("utf-8")
+        base64_frames.append(base64_str)
+
+    messages = [{"role": "user", "content": []}]
+    frame_format = {
+        "type": "image_url",
+        "image_url": {"url": "data:image/jpeg;base64,{}"},
+    }
+
+    for base64_frame in base64_frames:
+        frame_format["image_url"]["url"] = "data:image/jpeg;base64,{}".format(
+            base64_frame
+        )
+        messages[0]["content"].append(frame_format.copy())
+
+    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)
+    video_stream_request_test(client, video_path)
+    image_speed_test(client)
+    video_speed_test(client, video_path)
+
+
+if __name__ == "__main__":
+    main()

examples/usage/llava_video/srt_example_llava_v.py

@@ -121,6 +121,20 @@ def batch(video_dir, save_dir, cur_chunk, num_chunks, num_frames=16, batch_size=
 
 if __name__ == "__main__":
 
+    url = "https://raw.githubusercontent.com/EvolvingLMMs-Lab/sglang/dev/onevision_local/assets/jobs.mp4"
+
+    cache_dir = os.path.expanduser("~/.cache")
+    file_path = os.path.join(cache_dir, "jobs.mp4")
+
+    os.makedirs(cache_dir, exist_ok=True)
+
+    response = requests.get(url)
+    response.raise_for_status()  # Raise an exception for bad responses
+
+    with open(file_path, "wb") as f:
+        f.write(response.content)
+
+    print(f"File downloaded and saved to: {file_path}")
     # Create the parser
     parser = argparse.ArgumentParser(
         description="Run video processing with specified port."
@@ -148,7 +162,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--video-dir",
         type=str,
-        default="./videos/Q98Z4OTh8RwmDonc.mp4",
+        default=os.path.expanduser("~/.cache/jobs.mp4"),
         help="The directory or path for the processed video files.",
     )
     parser.add_argument(