[Feat/WIP] add llava-onevision, with support for (1) siglip encoder, (2) qwen2...

[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>

README.md

@@ -231,8 +231,13 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
   - `python -m sglang.launch_server --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --chat-template vicuna_v1.1 --port 30000`
   - `python -m sglang.launch_server --model-path liuhaotian/llava-v1.6-vicuna-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --chat-template vicuna_v1.1 --port 30000`
   - `python -m sglang.launch_server --model-path liuhaotian/llava-v1.6-34b --tokenizer-path liuhaotian/llava-v1.6-34b-tokenizer --port 30000`
+  - `python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000 --host=127.0.0.1 --tp-size=1 --chat-template=llava_llama_3`
+  - `python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --host="127.0.0.1" --tp-size=8 --chat-template=chatml-llava`
 - LLaVA-NeXT-Video
   - see [examples/usage/llava_video](examples/usage/llava_video)
+- [LLaVA-OneVision](https://arxiv.org/abs/2408.03326)
+  - `python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --host=127.0.0.1 --tp-size=8 --chat-template=chatml-llava --chunked-prefill-size=16384`
+  - see [test/srt/test_llava_onevision_openai_server.py](test/srt/test_llava_onevision_openai_server.py)
 - Yi-VL
   - see [srt_example_yi_vl.py](examples/quick_start/srt_example_yi_vl.py).
 - StableLM

examples/usage/llava/http_llava_onevision_test.py

+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(

python/pyproject.toml

@@ -20,7 +20,7 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-srt = ["aiohttp", "fastapi", "hf_transfer", "huggingface_hub", "interegular",
+srt = ["aiohttp", "decord", "fastapi", "hf_transfer", "huggingface_hub", "interegular",
        "packaging", "pillow", "psutil", "pydantic", "python-multipart",
        "torch", "uvicorn", "uvloop", "zmq",
        "vllm==0.5.4", "outlines>=0.0.44"]

python/sglang/lang/chat_template.py

@@ -137,7 +137,7 @@ register_chat_template(
 register_chat_template(
     ChatTemplate(
         name="chatml-llava",
-        default_system_prompt="Answer the questions.",
+        default_system_prompt="You are a helpful assistant.",
         role_prefix_and_suffix={
             "system": ("<|im_start|>system\n", "<|im_end|>\n"),
             "user": ("<|im_start|>user\n", "<|im_end|>\n"),
@@ -145,7 +145,7 @@ register_chat_template(
         },
         style=ChatTemplateStyle.PLAIN,
         stop_str=("<|im_end|>",),
-        image_token=" <image>\n",
+        image_token="<image>\n",
     )
 )
 
@@ -322,12 +322,17 @@ def match_chat_ml(model_path: str):
     if "tinyllama" in model_path:
         return get_chat_template("chatml")
     # Now the suffix for qwen2 chat model is "instruct"
-    if "qwen" in model_path and ("chat" in model_path or "instruct" in model_path):
+    if (
+        "qwen" in model_path
+        and ("chat" in model_path or "instruct" in model_path)
+        and ("llava" not in model_path)
+    ):
         return get_chat_template("qwen")
     if (
         "llava-v1.6-34b" in model_path
         or "llava-v1.6-yi-34b" in model_path
         or "llava-next-video-34b" in model_path
+        or "llava-onevision-qwen2" in model_path
     ):
         return get_chat_template("chatml-llava")
 

python/sglang/srt/conversation.py

@@ -34,6 +34,7 @@ class SeparatorStyle(IntEnum):
     NO_COLON_TWO = auto()
     ADD_NEW_LINE_SINGLE = auto()
     LLAMA2 = auto()
+    LLAMA3 = auto()
     CHATGLM = auto()
     CHATML = auto()
     CHATINTERN = auto()
@@ -137,6 +138,20 @@ class Conversation:
                 else:
                     ret += role + ":"
             return ret
+        elif self.sep_style == SeparatorStyle.LLAMA3:
+            ret = "<|begin_of_text|>"
+            if self.system_message:
+                ret += system_prompt
+            else:
+                ret += ""
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += f"<|start_header_id|>{role}<|end_header_id|>\n\n"
+                    ret += f"{message.strip()}<|eot_id|>"
+                else:
+                    ret += f"<|start_header_id|>{role}<|end_header_id|>\n\n"
+            # print(ret)
+            return ret
         elif self.sep_style == SeparatorStyle.LLAMA2:
             seps = [self.sep, self.sep2]
             if self.system_message:
@@ -379,12 +394,23 @@ def generate_chat_conv(
                 conv.append_message(conv.roles[0], message.content)
             else:
                 real_content = ""
+                # calculate number of image_url
+                num_image_url = 0
+                for content in message.content:
+                    if content.type == "image_url":
+                        num_image_url += 1
+                if num_image_url > 1:
+                    image_token = "<image>"
+                else:
+                    image_token = "<image>\n"
                 for content in message.content:
                     if content.type == "text":
+                        if num_image_url > 16:
+                            real_content += "\n"  # for video
                         real_content += content.text
                     elif content.type == "image_url":
                         # NOTE: Only works for llava
-                        real_content += "<image>\n"
+                        real_content += image_token
                         conv.append_image(content.image_url.url)
                 conv.append_message(conv.roles[0], real_content)
         elif msg_role == "assistant":
@@ -425,6 +451,18 @@ register_conv_template(
     )
 )
 
+register_conv_template(
+    Conversation(
+        name="chatml-llava",
+        system_template="<|im_start|>system\n{system_message}",
+        system_message="You are a helpful assistant.",
+        roles=("<|im_start|>user", "<|im_start|>assistant"),
+        sep_style=SeparatorStyle.CHATML,
+        sep="<|im_end|>",
+        stop_str=["<|endoftext|>", "<|im_end|>"],
+    )
+)
+
 register_conv_template(
     Conversation(
         name="vicuna_v1.1",
@@ -437,6 +475,17 @@ register_conv_template(
     )
 )
 
+register_conv_template(
+    Conversation(
+        name="llava_llama_3",
+        system_message="You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.",
+        system_template="<|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>",
+        roles=("user", "assistant"),
+        sep_style=SeparatorStyle.LLAMA3,
+        sep="",
+        stop_str=["<|end_of_text|>", "<|eot_id|>"],
+    )
+)
 # Reference: https://github.com/InternLM/lmdeploy/blob/387bf54b4f124e72aab30ae9755f562e435d3d01/lmdeploy/model.py#L425-L442
 register_conv_template(
     Conversation(

python/sglang/srt/managers/tokenizer_manager.py

@@ -131,11 +131,49 @@ class TokenizerManager:
         self.model_update_lock = asyncio.Lock()
         self.model_update_result = None
 
-    async def get_pixel_values(self, image_data):
-        aspect_ratio = getattr(self.hf_config, "image_aspect_ratio", None)
+    async def get_pixel_values(self, image_data, aspect_ratio=None):
+        aspect_ratio = (
+            getattr(self.hf_config, "image_aspect_ratio", None)
+            if aspect_ratio is None
+            else aspect_ratio
+        )
         grid_pinpoints = (
-            self.hf_config.image_grid_pinpoints if aspect_ratio == "anyres" else None
+            self.hf_config.image_grid_pinpoints
+            if hasattr(self.hf_config, "image_grid_pinpoints")
+            and "anyres" in aspect_ratio
+            else None
         )
+
+        if isinstance(image_data, list) and len(image_data) > 0:
+            pixel_values, image_hash, image_size = [], [], []
+            if len(image_data) > 1:
+                aspect_ratio = "pad"  # LLaVA OneVision Handling: more than one image --> interleaved image mode or video mode. We do not use anyres
+                for img_data in image_data:
+                    pixel_v, image_h, image_s = await self._process_single_image(
+                        img_data, aspect_ratio, grid_pinpoints
+                    )
+                    pixel_values.append(pixel_v)
+                    image_hash.append(image_h)
+                    image_size.append(image_s)
+                pixel_values = np.stack(pixel_values, axis=0)
+            else:
+                pixel_values, image_hash, image_size = await self._process_single_image(
+                    image_data[0], aspect_ratio, grid_pinpoints
+                )
+                image_hash = [image_hash]
+                image_size = [image_size]
+        elif isinstance(image_data, str):
+            pixel_values, image_hash, image_size = await self._process_single_image(
+                image_data, aspect_ratio, grid_pinpoints
+            )
+            image_hash = [image_hash]
+            image_size = [image_size]
+        else:
+            pixel_values, image_hash, image_size = None, None, None
+
+        return pixel_values, image_hash, image_size
+
+    async def _process_single_image(self, image_data, aspect_ratio, grid_pinpoints):
         if self.executor is not None:
             loop = asyncio.get_event_loop()
             return await loop.run_in_executor(
@@ -194,8 +232,8 @@ class TokenizerManager:
             )
 
             if self.is_generation:
-                pixel_values, image_hash, image_size = await self._get_pixel_values(
-                    obj.image_data if not_use_index else obj.image_data[index]
+                pixel_values, image_hash, image_size = await self.get_pixel_values(
+                    obj.image_data
                 )
                 return_logprob = (
                     obj.return_logprob if not_use_index else obj.return_logprob[index]
@@ -704,7 +742,7 @@ def get_pixel_values(
                     tuple(int(x * 255) for x in processor.image_processor.image_mean),
                 )
                 pixel_values = processor.image_processor(image)["pixel_values"][0]
-            elif image_aspect_ratio == "anyres":
+            elif image_aspect_ratio == "anyres" or "anyres_max" in image_aspect_ratio:
                 pixel_values = process_anyres_image(
                     image, processor.image_processor, image_grid_pinpoints
                 )

python/sglang/srt/managers/tp_worker.py

@@ -322,11 +322,16 @@ class ModelTpServer:
         if self.model_runner.is_generation:
             req.pixel_values = recv_req.pixel_values
             if req.pixel_values is not None:
+                image_hash = (
+                    hash(tuple(recv_req.image_hash))
+                    if isinstance(recv_req.image_hash, list)
+                    else recv_req.image_hash
+                )
                 req.pad_value = [
-                    (recv_req.image_hash) % self.model_config.vocab_size,
-                    (recv_req.image_hash >> 16) % self.model_config.vocab_size,
-                    (recv_req.image_hash >> 32) % self.model_config.vocab_size,
-                    (recv_req.image_hash >> 64) % self.model_config.vocab_size,
+                    (image_hash) % self.model_config.vocab_size,
+                    (image_hash >> 16) % self.model_config.vocab_size,
+                    (image_hash >> 32) % self.model_config.vocab_size,
+                    (image_hash >> 64) % self.model_config.vocab_size,
                 ]
                 req.image_size = recv_req.image_size
                 (

python/sglang/srt/mm_utils.py

@@ -13,10 +13,25 @@ See the License for the specific language governing permissions and
 limitations under the License.
 """
 
-# Source: https://github.com/haotian-liu/LLaVA/blob/main/llava/mm_utils.py
+# Source: https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/llava/mm_utils.py
+"""
+Utilities for multi-modal models.
+
+This python file mainly contains utilities that were used in the 
+image processing logic of llava-next including operations such as
+anyres and anyres_max
+
+Currently supports the anyres and anyres_max operation for CLIP and
+SigLip. For more information, you may refer to the paper or the blog
+
+LLaVA-NeXT : https://llava-vl.github.io/blog/2024-01-30-llava-next/
+LLaVA-Onevision : https://arxiv.org/pdf/2408.03326
+
+"""
 import ast
 import base64
 import math
+import re
 from io import BytesIO
 
 import numpy as np
@@ -40,10 +55,13 @@ def select_best_resolution(original_size, possible_resolutions):
     min_wasted_resolution = float("inf")
 
     for width, height in possible_resolutions:
+        # Calculate the downscaled size to keep the aspect ratio
         scale = min(width / original_width, height / original_height)
         downscaled_width, downscaled_height = int(original_width * scale), int(
             original_height * scale
         )
+
+        # Calculate effective and wasted resolutions
         effective_resolution = min(
             downscaled_width * downscaled_height, original_width * original_height
         )
@@ -129,6 +147,26 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
     Returns:
         tuple: The shape of the image patch grid in the format (width, height).
     """
+    if isinstance(grid_pinpoints, str) and "x" in grid_pinpoints:
+        assert patch_size in [
+            224,
+            336,
+            384,
+            448,
+            512,
+        ], "patch_size should be in [224, 336, 384, 448, 512]"
+        # Use regex to extract the range from the input string
+        matches = re.findall(r"\((\d+)x(\d+)\)", grid_pinpoints)
+        range_start = tuple(map(int, matches[0]))
+        range_end = tuple(map(int, matches[-1]))
+        # Generate a matrix of tuples from (range_start[0], range_start[1]) to (range_end[0], range_end[1])
+        grid_pinpoints = [
+            (i, j)
+            for i in range(range_start[0], range_end[0] + 1)
+            for j in range(range_start[1], range_end[1] + 1)
+        ]
+        # Multiply all elements by patch_size
+        grid_pinpoints = [[dim * patch_size for dim in pair] for pair in grid_pinpoints]
     if type(grid_pinpoints) is list:
         possible_resolutions = grid_pinpoints
     else:
@@ -149,6 +187,31 @@ def process_anyres_image(image, processor, grid_pinpoints):
     Returns:
         np.array: An np array containing the processed image patches.
     """
+    if isinstance(grid_pinpoints, str) and "x" in grid_pinpoints:
+        try:
+            patch_size = processor.size[0]
+        except Exception as e:
+            patch_size = processor.size["shortest_edge"]
+        assert patch_size in [
+            224,
+            336,
+            384,
+            448,
+            512,
+        ], "patch_size should be in [224, 336, 384, 448, 512]"
+        # Use regex to extract the range from the input string
+        matches = re.findall(r"\((\d+)x(\d+)\)", grid_pinpoints)
+        range_start = tuple(map(int, matches[0]))
+        range_end = tuple(map(int, matches[-1]))
+        # Generate a matrix of tuples from (range_start[0], range_start[1]) to (range_end[0], range_end[1])
+        grid_pinpoints = [
+            (i, j)
+            for i in range(range_start[0], range_end[0] + 1)
+            for j in range(range_start[1], range_end[1] + 1)
+        ]
+        # Multiply all elements by patch_size
+        grid_pinpoints = [[dim * patch_size for dim in pair] for pair in grid_pinpoints]
+
     if type(grid_pinpoints) is list:
         possible_resolutions = grid_pinpoints
     else:
@@ -156,15 +219,24 @@ def process_anyres_image(image, processor, grid_pinpoints):
     best_resolution = select_best_resolution(image.size, possible_resolutions)
     image_padded = resize_and_pad_image(image, best_resolution)
 
-    patches = divide_to_patches(image_padded, processor.crop_size["height"])
-
-    image_original_resize = image.resize(
-        (processor.size["shortest_edge"], processor.size["shortest_edge"])
+    # For Siglip processor, only have size but no crop size
+    crop_size = (
+        processor.crop_size["height"]
+        if "crop_size" in processor.__dict__
+        else processor.size["height"]
     )
+    shortest_edge = (
+        processor.size["shortest_edge"]
+        if "shortest_edge" in processor.size
+        else processor.size["height"]
+    )
+    patches = divide_to_patches(image_padded, crop_size)
+
+    image_original_resize = image.resize((shortest_edge, shortest_edge))
 
     image_patches = [image_original_resize] + patches
     image_patches = [
-        processor.preprocess(image_patch)["pixel_values"][0]
+        processor.preprocess(image_patch.convert("RGB"))["pixel_values"][0]
         for image_patch in image_patches
     ]
     return np.stack(image_patches, axis=0)
@@ -255,7 +327,7 @@ def process_images(images, image_processor, model_cfg):
             )
             image = image_processor.preprocess(image)["pixel_values"][0]
             new_images.append(image)
-    elif image_aspect_ratio == "anyres":
+    elif "anyres" in image_aspect_ratio:
         for image in images:
             image = process_anyres_image(
                 image, image_processor, model_cfg.image_grid_pinpoints

python/sglang/srt/model_executor/forward_batch_info.py

@@ -88,14 +88,19 @@ class InputMetadata:
         reqs = batch.reqs
         self.pixel_values = [r.pixel_values for r in reqs]
         self.image_sizes = [r.image_size for r in reqs]
-        self.image_offsets = [
-            (
-                (r.image_offset - batch.prefix_lens_cpu[i])
-                if r.image_offset is not None
-                else 0
-            )
-            for i, r in enumerate(reqs)
-        ]
+        self.image_offsets = []
+        for r in reqs:
+            if isinstance(r.image_offset, list):
+                self.image_offsets.append(
+                    [
+                        (image_offset - len(r.prefix_indices))
+                        for image_offset in r.image_offset
+                    ]
+                )
+            elif isinstance(r.image_offset, int):
+                self.image_offsets.append(r.image_offset - len(r.prefix_indices))
+            elif r.image_offset is None:
+                self.image_offsets.append(0)
 
     def compute_positions(self, batch: ScheduleBatch):
         position_ids_offsets = batch.position_ids_offsets

python/sglang/srt/models/llava.py

@@ -15,6 +15,8 @@ limitations under the License.
 
 """Inference-only LLaVa model compatible with HuggingFace weights."""
 
+import math
+import re
 from typing import Iterable, List, Optional, Tuple
 
 import numpy as np
@@ -26,6 +28,8 @@ from transformers import (
     LlavaConfig,
     MistralConfig,
     Qwen2Config,
+    SiglipVisionConfig,
+    SiglipVisionModel,
 )
 from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
 from vllm.config import CacheConfig
@@ -63,34 +67,61 @@ class LlavaLlamaForCausalLM(nn.Module):
             )
 
     def pad_input_ids(self, input_ids, pad_value, pt_shape=None, image_size=None):
-        new_image_feature_len = self.image_feature_len
-        # now only support spatial_unpad + anyres
-        if self.mm_patch_merge_type.startswith("spatial"):
+
+        # hardcode for spatial_unpad + anyres
+        image_aspect_ratio = "anyres" if len(image_size) == 1 else "pad"
+        offset_list = []
+        for image_s in image_size:
+            if len(image_size) > 16:
+                # 2x2 pooling with stride 2
+                new_image_feature_len = (
+                    math.ceil(self.image_size / self.patch_size / 2) ** 2
+                )
+            else:
+                new_image_feature_len = self.image_feature_len  # multiimage
+
             height = width = self.num_patches_per_side
-            if pt_shape[0] > 1:
-                if self.image_aspect_ratio == "anyres":
-                    num_patch_width, num_patch_height = get_anyres_image_grid_shape(
-                        image_size,
-                        self.image_grid_pinpoints,
-                        self.vision_tower.config.image_size,
+            if "anyres" in image_aspect_ratio:
+                num_patch_width, num_patch_height = get_anyres_image_grid_shape(
+                    image_s,
+                    self.image_grid_pinpoints,
+                    self.vision_tower.config.image_size,
+                )
+                h = num_patch_height * height
+                w = num_patch_width * width
+                new_h, new_w = unpad_image_shape(h, w, image_s)
+
+                if "anyres_max" in self.config.image_aspect_ratio:
+                    matched_anyres_max_num_patches = re.match(
+                        r"anyres_max_(\d+)", self.config.image_aspect_ratio
+                    )
+                    if matched_anyres_max_num_patches:
+                        max_num_patches = int(matched_anyres_max_num_patches.group(1))
+                    # times = math.sqrt(h * w / (max_num_patches * unit**2))
+                    times = math.sqrt(
+                        new_h * new_w / (max_num_patches * self.image_feature_len)
                     )
-                if "unpad" in self.mm_patch_merge_type:
-                    h = num_patch_height * height
-                    w = num_patch_width * width
-                    new_h, new_w = unpad_image_shape(h, w, image_size)
-                    new_image_feature_len += new_h * (new_w + 1)
-
-        pad_ids = pad_value * (
-            (new_image_feature_len + len(pad_value)) // len(pad_value)
-        )
-        offset = input_ids.index(self.config.image_token_index)
-        # old_len + pad_len - 1, because we need to remove image_token_id
-        new_input_ids = (
-            input_ids[:offset]
-            + pad_ids[:new_image_feature_len]
-            + input_ids[offset + 1 :]
-        )
-        return new_input_ids, offset
+                    if times > 1.1:
+                        new_h = int(new_h // times)
+                        new_w = int(new_w // times)
+                new_image_feature_len += new_h * (new_w + 1)
+
+            pad_ids = pad_value * (
+                (new_image_feature_len + len(pad_value)) // len(pad_value)
+            )
+            # print("calculated new_image_feature_len: ", new_image_feature_len)
+            try:
+                offset = input_ids.index(self.config.image_token_index)
+            except ValueError:
+                offset = 0
+            # old_len + pad_len - 1, because we need to remove image_token_id
+            input_ids = (
+                input_ids[:offset]
+                + pad_ids[:new_image_feature_len]
+                + input_ids[offset + 1 :]
+            )
+            offset_list.append(offset)
+        return input_ids, offset_list
 
     def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
         image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
@@ -124,7 +155,6 @@ class LlavaLlamaForCausalLM(nn.Module):
 
             # Embed text input
             input_embeds = self.language_model.model.embed_tokens(input_ids)
-
             # Embed vision input
             need_vision = (
                 (positions[input_metadata.extend_start_loc] < self.image_feature_len)
@@ -163,27 +193,73 @@ class LlavaLlamaForCausalLM(nn.Module):
 
                 if self.mm_patch_merge_type.startswith("spatial"):
                     new_image_features = []
+                    height = width = self.num_patches_per_side
                     for image_idx, image_feature in enumerate(image_features):
-                        if image_feature.shape[0] > 1:
+                        if len(image_sizes[image_idx]) == 1:
+                            image_aspect_ratio = (
+                                self.config.image_aspect_ratio
+                            )  # single image
+                        else:
+                            image_aspect_ratio = "pad"  # multi image
+                        # image_aspect_ratio = (
+                        #     "anyres" if len(image_sizes[image_idx]) == 1 else "pad"
+                        # )
+                        if (
+                            image_feature.shape[0] > 1
+                            and "anyres" in image_aspect_ratio
+                        ):
                             base_image_feature = image_feature[0]
                             image_feature = image_feature[1:]
-                            height = width = self.num_patches_per_side
                             assert height * width == base_image_feature.shape[0]
-                            if self.image_aspect_ratio == "anyres":
-                                (
-                                    num_patch_width,
-                                    num_patch_height,
-                                ) = get_anyres_image_grid_shape(
-                                    image_sizes[image_idx],
-                                    self.image_grid_pinpoints,
-                                    self.vision_tower.config.image_size,
+
+                            if "anyres_max" in image_aspect_ratio:
+                                matched_anyres_max_num_patches = re.match(
+                                    r"anyres_max_(\d+)", image_aspect_ratio
                                 )
+                                if matched_anyres_max_num_patches:
+                                    max_num_patches = int(
+                                        matched_anyres_max_num_patches.group(1)
+                                    )
+
+                            if (
+                                image_aspect_ratio == "anyres"
+                                or "anyres_max" in image_aspect_ratio
+                            ):
+                                vision_tower_image_size = self.image_size
+                                try:
+                                    num_patch_width, num_patch_height = (
+                                        get_anyres_image_grid_shape(
+                                            image_sizes[image_idx][0],
+                                            self.config.image_grid_pinpoints,
+                                            vision_tower_image_size,
+                                        )
+                                    )
+                                except Exception as e:
+                                    print(f"Error: {e}")
+                                    num_patch_width, num_patch_height = 2, 2
                                 image_feature = image_feature.view(
                                     num_patch_height, num_patch_width, height, width, -1
                                 )
                             else:
-                                raise NotImplementedError()
+                                image_feature = image_feature.view(
+                                    2, 2, height, width, -1
+                                )
+
+                            # (
+                            #     num_patch_width,
+                            #     num_patch_height,
+                            # ) = get_anyres_image_grid_shape(
+                            #     image_sizes[image_idx][0],
+                            #     self.image_grid_pinpoints,
+                            #     self.vision_tower.config.image_size,
+                            # )
+
+                            # image_feature = image_feature.view(
+                            #     num_patch_height, num_patch_width, height, width, -1
+                            # )
+
                             if "unpad" in self.mm_patch_merge_type:
+                                unit = image_feature.shape[2]
                                 image_feature = image_feature.permute(
                                     4, 0, 2, 1, 3
                                 ).contiguous()
@@ -191,8 +267,23 @@ class LlavaLlamaForCausalLM(nn.Module):
                                     2, 3
                                 )
                                 image_feature = unpad_image(
-                                    image_feature, image_sizes[image_idx]
+                                    image_feature, image_sizes[image_idx][0]
                                 )
+                                if (
+                                    "anyres_max" in image_aspect_ratio
+                                    and matched_anyres_max_num_patches
+                                ):
+                                    c, h, w = image_feature.shape
+                                    times = math.sqrt(
+                                        h * w / (max_num_patches * unit**2)
+                                    )
+                                    if times > 1.1:
+                                        image_feature = image_feature[None]
+                                        image_feature = nn.functional.interpolate(
+                                            image_feature,
+                                            [int(h // times), int(w // times)],
+                                            mode="bilinear",
+                                        )[0]
                                 image_feature = torch.cat(
                                     (
                                         image_feature,
@@ -213,16 +304,31 @@ class LlavaLlamaForCausalLM(nn.Module):
                             image_feature = torch.cat(
                                 (base_image_feature, image_feature), dim=0
                             )
+                            image_feature = image_feature.unsqueeze(0)
                         else:
-                            image_feature = image_feature[0]
-                            if "unpad" in self.mm_patch_merge_type:
-                                image_feature = torch.cat(
-                                    (
-                                        image_feature,
-                                        self.language_model.model.image_newline[None],
-                                    ),
-                                    dim=0,
+                            if image_feature.shape[0] > 16:  # video
+                                # 2x2 pooling
+                                num_of_frames = image_feature.shape[0]
+                                image_feature = image_feature.view(
+                                    num_of_frames, height, width, -1
                                 )
+                                image_feature = image_feature.permute(
+                                    0, 3, 1, 2
+                                ).contiguous()  # N, C, H, W
+                                height, weight = image_feature.shape[2:]
+                                scaled_shape = [
+                                    math.ceil(height / 2),
+                                    math.ceil(weight / 2),
+                                ]
+                                image_feature = nn.functional.interpolate(
+                                    image_feature, size=scaled_shape, mode="bilinear"
+                                )
+                                image_feature = (
+                                    image_feature.flatten(2)
+                                    .transpose(1, 2)
+                                    .contiguous()
+                                )  # N, C, H*W
+
                         new_image_features.append(image_feature)
                     image_features = new_image_features
 
@@ -233,21 +339,22 @@ class LlavaLlamaForCausalLM(nn.Module):
                         continue
 
                     start_idx = extend_start_loc_cpu[i]
-                    pad_len, pad_dim = image_features[pt].shape  # 576, 4096
+                    pad_dim = image_features[pt].shape[-1]  # 576, 4096
                     dim = input_embeds.shape[1]
                     assert (
                         pad_dim == dim
                     ), "invalid pad_dim={}, input_embed_dim={}!".format(pad_dim, dim)
                     # Fill in the placeholder for the image
                     try:
-                        input_embeds[
-                            start_idx
-                            + image_offsets[i] : start_idx
-                            + image_offsets[i]
-                            + pad_len
-                        ] = image_features[pt]
+                        for j, image_off in enumerate(image_offsets[i]):
+                            # print("actual image_features length: ", image_features[pt][j].shape[0])
+                            pad_len = image_features[pt][j].shape[0]
+                            input_embeds[
+                                start_idx + image_off : start_idx + image_off + pad_len
+                            ] = image_features[pt][j]
                     except RuntimeError as e:
                         print(f"RuntimeError in llava image encoding: {e}")
+                        print(image_features[pt].shape)
                         print(input_embeds.shape)
                         print(start_idx, image_offsets[i])
                     pt += 1
@@ -262,9 +369,16 @@ class LlavaLlamaForCausalLM(nn.Module):
         # load clip vision model by cfg['mm_vision_tower']:
         #   huggingface_name or path_of_clip_relative_to_llava_model_dir
         vision_path = self.config.mm_vision_tower
-        self.vision_tower = CLIPVisionModel.from_pretrained(
-            vision_path, torch_dtype=torch.float16
-        ).cuda()
+        if "clip" in vision_path:
+            self.vision_tower = CLIPVisionModel.from_pretrained(
+                vision_path, torch_dtype=torch.float16
+            ).cuda()
+        elif "siglip" in vision_path:
+            self.vision_tower = SiglipVisionModel.from_pretrained(
+                vision_path, torch_dtype=torch.float16
+            ).cuda()
+            # Siglip needs all feature tokens
+            self.config.mm_vision_select_feature = "full"
         self.vision_tower.eval()
 
         self.vision_feature_layer = self.config.mm_vision_select_layer
@@ -276,8 +390,11 @@ class LlavaLlamaForCausalLM(nn.Module):
         self.image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
         self.image_grid_pinpoints = getattr(self.config, "image_grid_pinpoints", None)
 
-        self.image_feature_len = int((self.image_size / self.patch_size) ** 2)
-        if self.vision_feature_select_strategy == "patch":
+        self.image_feature_len = int((self.image_size // self.patch_size) ** 2)
+        if (
+            self.vision_feature_select_strategy == "patch"
+            or self.vision_feature_select_strategy == "full"
+        ):
             pass
         elif self.vision_feature_select_strategy == "cls_patch":
             self.image_feature_len += 1

test/srt/test_vision_openai_server.py

+import base64
+import io
 import json
+import os
+import sys
+import time
 import unittest
 
+import numpy as np
 import openai
+import requests
+from decord import VideoReader, cpu
+from PIL import Image
 
 from sglang.srt.hf_transformers_utils import get_tokenizer
 from sglang.srt.utils import kill_child_process
 from sglang.test.test_utils import DEFAULT_URL_FOR_UNIT_TEST, popen_launch_server
 
 
+# python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --tokenizer-path lmms-lab/llavanext-qwen-siglip-tokenizer --port=30000 --host=127.0.0.1 --tp-size=8 --chat-template=chatml-llava --chunked-prefill-size=16384
 class TestOpenAIVisionServer(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
-        cls.model = "liuhaotian/llava-v1.6-vicuna-7b"
+        cls.model = "lmms-lab/llava-onevision-qwen2-0.5b-ov"
         cls.base_url = DEFAULT_URL_FOR_UNIT_TEST
         cls.api_key = "sk-123456"
         cls.process = popen_launch_server(
@@ -21,9 +31,11 @@ class TestOpenAIVisionServer(unittest.TestCase):
             api_key=cls.api_key,
             other_args=[
                 "--chat-template",
-                "vicuna_v1.1",
+                "chatml-llava",
                 "--tokenizer-path",
-                "llava-hf/llava-1.5-7b-hf",
+                "lmms-lab/llavanext-qwen-siglip-tokenizer",
+                "--chunked-prefill-size",
+                "16384",
                 "--log-requests",
             ],
         )
@@ -68,6 +80,81 @@ class TestOpenAIVisionServer(unittest.TestCase):
         assert response.usage.completion_tokens > 0
         assert response.usage.total_tokens > 0
 
+    def prepare_video_messages(self, 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 test_video_chat_completion(self):
+        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)
+
+        if not os.path.exists(file_path):
+            response = requests.get(url)
+            response.raise_for_status()
+
+            with open(file_path, "wb") as f:
+                f.write(response.content)
+
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        messages = self.prepare_video_messages(file_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)
+
+        # Add assertions to validate the video response
+        self.assertIsNotNone(video_response)
+        self.assertGreater(len(video_response), 0)
+
     def test_regex(self):
         client = openai.Client(api_key=self.api_key, base_url=self.base_url)