support llava video (#426)

.gitignore

@@ -177,3 +177,7 @@ tmp*.txt
 # Plots
 *.png
 *.pdf
+
+# personnal
+work_dirs/
+*.csv

examples/quick_start/srt_example_llava.py

@@ -14,7 +14,7 @@ def single():
     state = image_qa.run(
         image_path="images/cat.jpeg",
         question="What is this?",
-        max_new_tokens=64)
+        max_new_tokens=128)
     print(state["answer"], "\n")
 
 
@@ -36,7 +36,7 @@ def batch():
             {"image_path": "images/cat.jpeg", "question":"What is this?"},
             {"image_path": "images/dog.jpeg", "question":"What is this?"},
         ],
-        max_new_tokens=64,
+        max_new_tokens=128,
     )
     for s in states:
         print(s["answer"], "\n")

examples/usage/llava_video/srt_example_llava_v.py

+"""
+Usage: python3 srt_example_llava.py
+"""
+
+import sglang as sgl
+import os
+import csv
+import time
+import argparse
+
+@sgl.function
+def video_qa(s, num_frames, video_path, question):
+    s += sgl.user(sgl.video(video_path,num_frames) + question)
+    s += sgl.assistant(sgl.gen("answer"))
+
+
+def single(path, num_frames=16):
+    state = video_qa.run(
+        num_frames=num_frames,
+        video_path=path,
+        question="Please provide a detailed description of the video, focusing on the main subjects, their actions, the background scenes",
+        temperature=0.0,
+        max_new_tokens=1024,
+    )
+    print(state["answer"], "\n")
+
+
+
+def split_into_chunks(lst, num_chunks):
+    """Split a list into a specified number of chunks."""
+    # Calculate the chunk size using integer division. Note that this may drop some items if not evenly divisible.
+    chunk_size = len(lst) // num_chunks
+
+    if chunk_size == 0:
+        chunk_size = len(lst)
+    # Use list comprehension to generate chunks. The last chunk will take any remainder if the list size isn't evenly divisible.
+    chunks = [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
+    # Ensure we have exactly num_chunks chunks, even if some are empty
+    chunks.extend([[] for _ in range(num_chunks - len(chunks))])
+    return chunks
+
+
+def save_batch_results(batch_video_files, states, cur_chunk, batch_idx, save_dir):
+    csv_filename = f"{save_dir}/chunk_{cur_chunk}_batch_{batch_idx}.csv"
+    with open(csv_filename, 'w', newline='') as csvfile:
+        writer = csv.writer(csvfile)
+        writer.writerow(['video_name', 'answer'])
+        for video_path, state in zip(batch_video_files, states):
+            video_name = os.path.basename(video_path)
+            writer.writerow([video_name, state["answer"]])
+
+def compile_and_cleanup_final_results(cur_chunk, num_batches, save_dir):
+    final_csv_filename = f"{save_dir}/final_results_chunk_{cur_chunk}.csv"
+    with open(final_csv_filename, 'w', newline='') as final_csvfile:
+        writer = csv.writer(final_csvfile)
+        writer.writerow(['video_name', 'answer'])
+        for batch_idx in range(num_batches):
+            batch_csv_filename = f"{save_dir}/chunk_{cur_chunk}_batch_{batch_idx}.csv"
+            with open(batch_csv_filename, 'r') as batch_csvfile:
+                reader = csv.reader(batch_csvfile)
+                next(reader)  # Skip header row
+                for row in reader:
+                    writer.writerow(row)
+            os.remove(batch_csv_filename)
+
+def find_video_files(video_dir):
+    # Check if the video_dir is actually a file
+    if os.path.isfile(video_dir):
+        # If it's a file, return it as a single-element list
+        return [video_dir]
+    
+    # Original logic to find video files in a directory
+    video_files = []
+    for root, dirs, files in os.walk(video_dir):
+        for file in files:
+            if file.endswith(('.mp4', '.avi', '.mov')):
+                video_files.append(os.path.join(root, file))
+    return video_files
+
+def batch(video_dir, save_dir, cur_chunk, num_chunks, num_frames=16, batch_size=64):
+    video_files = find_video_files(video_dir)
+    chunked_video_files = split_into_chunks(video_files, num_chunks)[cur_chunk]
+    num_batches = 0
+
+    for i in range(0, len(chunked_video_files), batch_size):
+        batch_video_files = chunked_video_files[i:i + batch_size]
+        print(f"Processing batch of {len(batch_video_files)} video(s)...")
+
+        if not batch_video_files:
+            print("No video files found in the specified directory.")
+            return
+        
+        batch_input = [
+            {   
+                "num_frames": num_frames,
+                "video_path": video_path,
+                "question": "Please provide a detailed description of the video, focusing on the main subjects, their actions, the background scenes.",
+            } for video_path in batch_video_files
+        ]
+
+        start_time = time.time()
+        states = video_qa.run_batch(batch_input, max_new_tokens=512, temperature=0.2)
+        total_time = time.time() - start_time
+        average_time = total_time / len(batch_video_files)
+        print(f"Number of videos in batch: {len(batch_video_files)}. Average processing time per video: {average_time:.2f} seconds. Total time for this batch: {total_time:.2f} seconds")
+
+        save_batch_results(batch_video_files, states, cur_chunk, num_batches, save_dir)
+        num_batches += 1
+
+    compile_and_cleanup_final_results(cur_chunk, num_batches, save_dir)
+
+
+if __name__ == "__main__":
+
+    # Create the parser
+    parser = argparse.ArgumentParser(description='Run video processing with specified port.')
+
+    # Add an argument for the port
+    parser.add_argument('--port', type=int, default=30000, help='The master port for distributed serving.')
+    parser.add_argument('--chunk-idx', type=int, default=0, help='The index of the chunk to process.')
+    parser.add_argument('--num-chunks', type=int, default=8, help='The number of chunks to process.')
+    parser.add_argument('--save-dir', type=str, default="./work_dirs/llava_video", help='The directory to save the processed video files.')
+    parser.add_argument('--video-dir', type=str, default="./videos/Q98Z4OTh8RwmDonc.mp4", help='The directory or path for the processed video files.')
+    parser.add_argument('--model-path', type=str, default="lmms-lab/LLaVA-NeXT-Video-7B", help='The model path for the video processing.')
+    parser.add_argument('--num-frames', type=int, default=16, help='The number of frames to process in each video.' )
+    parser.add_argument("--mm_spatial_pool_stride", type=int, default=2)
+
+    # Parse the arguments
+    args = parser.parse_args()
+
+    cur_port = args.port
+
+    cur_chunk = args.chunk_idx
+
+    num_chunks = args.num_chunks
+
+    num_frames = args.num_frames
+
+    if "34b" in args.model_path.lower():
+        tokenizer_path = "liuhaotian/llava-v1.6-34b-tokenizer"
+    elif "7b" in args.model_path.lower():
+        tokenizer_path = "llava-hf/llava-1.5-7b-hf"
+    else:
+        print("Invalid model path. Please specify a valid model path.")
+        exit()
+
+    model_overide_args = {}
+
+    model_overide_args["mm_spatial_pool_stride"] = args.mm_spatial_pool_stride
+    model_overide_args["architectures"] = ["LlavaVidForCausalLM"]
+    model_overide_args["num_frames"] = args.num_frames
+    model_overide_args["model_type"] = "llava"
+
+    if "34b" in args.model_path.lower():
+        model_overide_args["image_token_index"] = 64002
+
+
+    if args.num_frames == 32:
+        model_overide_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
+        model_overide_args["max_sequence_length"] = 4096 * 2
+        model_overide_args["tokenizer_model_max_length"] = 4096 * 2
+    elif args.num_frames < 32:
+        pass
+    else:
+        print("The maximum number of frames to process is 32. Please specify a valid number of frames.")
+        exit()
+
+
+    runtime = sgl.Runtime(
+        model_path=args.model_path, #"liuhaotian/llava-v1.6-vicuna-7b",
+        tokenizer_path=tokenizer_path,
+        port=cur_port,
+        additional_ports=[cur_port+1,cur_port+2,cur_port+3,cur_port+4],
+        model_overide_args=model_overide_args,
+        tp_size=1
+    )
+    sgl.set_default_backend(runtime)
+    print(f"chat template: {runtime.endpoint.chat_template.name}")
+
+
+    # Run a single request
+    # try:
+    print("\n========== single ==========\n")
+    root = args.video_dir
+    if os.path.isfile(root):
+        video_files = [root]
+    else:
+        video_files = [os.path.join(root, f) for f in os.listdir(root) if f.endswith(('.mp4', '.avi', '.mov'))]  # Add more extensions if needed
+    start_time = time.time()  # Start time for processing a single video
+    for cur_video in video_files[:1]:
+        print(cur_video)
+        single(cur_video, num_frames)
+    end_time = time.time()  # End time for processing a single video
+    total_time = end_time - start_time
+    average_time = total_time / len(video_files)  # Calculate the average processing time
+    print(f"Average processing time per video: {average_time:.2f} seconds")
+    runtime.shutdown()
+    # except Exception as e:
+    #     print(e)
+    runtime.shutdown()
+
+
+    # # # Run a batch of requests
+    # print("\n========== batch ==========\n")
+    # if not os.path.exists(args.save_dir):
+    #     os.makedirs(args.save_dir)
+    # batch(args.video_dir,args.save_dir,cur_chunk, num_chunks, num_frames, num_chunks)
+    # runtime.shutdown()
\ No newline at end of file

examples/usage/llava_video/srt_example_llava_v.sh

+#!/bin/bash
+
+##### USAGE #####
+#    - First node:
+#      ```sh
+#      bash examples/quick_start/srt_example_llava_v.sh K 0 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
+#      ```
+#    - Second node:
+#      ```sh
+#      bash examples/quick_start/srt_example_llava_v.sh K 1 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
+#      ```
+#    - The K node:
+#      ```sh
+#      bash examples/quick_start/srt_example_llava_v.sh K K-1 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
+#      ```
+
+
+# Replace `K`, `YOUR_VIDEO_PATH`, `YOUR_MODEL_PATH`, and `FRAMES_PER_VIDEO` with your specific details.
+CURRENT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+
+echo ${CURRENT_ROOT}
+
+cd ${CURRENT_ROOT}
+
+export PYTHONWARNINGS=ignore
+
+START_TIME=$(date +%s)  # Capture start time
+
+NUM_NODES=$1
+
+CUR_NODES_IDX=$2
+
+VIDEO_DIR=$3
+
+MODEL_PATH=$4   
+
+NUM_FRAMES=$5
+
+
+# FRAME_FORMAT=$6
+
+# FRAME_FORMAT=$(echo $FRAME_FORMAT | tr '[:lower:]' '[:upper:]')
+
+# # Check if FRAME_FORMAT is either JPEG or PNG
+# if [[ "$FRAME_FORMAT" != "JPEG" && "$FRAME_FORMAT" != "PNG" ]]; then
+#     echo "Error: FRAME_FORMAT must be either JPEG or PNG."
+#     exit 1
+# fi
+
+# export TARGET_FRAMES=$TARGET_FRAMES
+
+echo "Each video you will sample $NUM_FRAMES frames"
+
+# export FRAME_FORMAT=$FRAME_FORMAT
+
+# echo "The frame format is $FRAME_FORMAT"
+
+# Assuming GPULIST is a bash array containing your GPUs
+GPULIST=(0 1 2 3 4 5 6 7)
+LOCAL_CHUNKS=${#GPULIST[@]}
+
+echo "Number of GPUs in GPULIST: $LOCAL_CHUNKS"
+
+ALL_CHUNKS=$((NUM_NODES * LOCAL_CHUNKS))
+
+# Calculate GPUs per chunk
+GPUS_PER_CHUNK=8
+
+echo $GPUS_PER_CHUNK
+
+for IDX in $(seq 1 $LOCAL_CHUNKS); do
+    (
+        START=$(((IDX-1) * GPUS_PER_CHUNK))
+        LENGTH=$GPUS_PER_CHUNK # Length for slicing, not the end index
+        
+        CHUNK_GPUS=(${GPULIST[@]:$START:$LENGTH})
+        
+        # Convert the chunk GPUs array to a comma-separated string
+        CHUNK_GPUS_STR=$(IFS=,; echo "${CHUNK_GPUS[*]}")
+
+        LOCAL_IDX=$((CUR_NODES_IDX * LOCAL_CHUNKS + IDX))
+
+        echo "Chunk $(($LOCAL_IDX - 1)) will run on GPUs $CHUNK_GPUS_STR"
+        
+        # Calculate the port for this chunk. Ensure it's incremented by 5 for each chunk.
+        PORT=$((10000 + RANDOM % 55536))
+
+        MAX_RETRIES=10
+        RETRY_COUNT=0
+        COMMAND_STATUS=1  # Initialize as failed
+
+        while [ $RETRY_COUNT -lt $MAX_RETRIES ] && [ $COMMAND_STATUS -ne 0 ]; do
+            echo "Running chunk $(($LOCAL_IDX - 1)) on GPUs $CHUNK_GPUS_STR with port $PORT. Attempt $(($RETRY_COUNT + 1))"
+            
+#!/bin/bash
+            CUDA_VISIBLE_DEVICES=$CHUNK_GPUS_STR python3 examples/usage/llava_video/srt_example_llava_v.py \
+            --port $PORT \
+            --num-chunks $ALL_CHUNKS \
+            --chunk-idx $(($LOCAL_IDX - 1)) \
+            --save-dir work_dirs/llava_next_video_inference_results \
+            --video-dir $VIDEO_DIR \
+            --model-path $MODEL_PATH \
+            --num-frames $NUM_FRAMES #&
+            
+            wait $!  # Wait for the process to finish and capture its exit status
+            COMMAND_STATUS=$?
+            
+            if [ $COMMAND_STATUS -ne 0 ]; then
+                echo "Execution failed for chunk $(($LOCAL_IDX - 1)), attempt $(($RETRY_COUNT + 1)). Retrying..."
+                RETRY_COUNT=$(($RETRY_COUNT + 1))
+                sleep 180  # Wait a bit before retrying
+            else
+                echo "Execution succeeded for chunk $(($LOCAL_IDX - 1))."
+            fi
+        done
+
+        if [ $COMMAND_STATUS -ne 0 ]; then
+            echo "Execution failed for chunk $(($LOCAL_IDX - 1)) after $MAX_RETRIES attempts."
+        fi
+    ) #&
+    sleep 2  # Slight delay to stagger the start times
+done
+
+wait
+
+cat work_dirs/llava_next_video_inference_results/final_results_chunk_*.csv > work_dirs/llava_next_video_inference_results/final_results_node_${CUR_NODES_IDX}.csv   
+
+END_TIME=$(date +%s)  # Capture end time
+ELAPSED_TIME=$(($END_TIME - $START_TIME))
+echo "Total execution time: $ELAPSED_TIME seconds."
\ No newline at end of file

python/pyproject.toml

@@ -20,7 +20,7 @@ dependencies = [
 
 [project.optional-dependencies]
 srt = ["aiohttp", "fastapi", "psutil", "rpyc", "torch", "uvloop", "uvicorn",
-       "zmq", "vllm>=0.4.2", "interegular", "pydantic", "pillow", "outlines>=0.0.27", "packaging"]
+       "zmq", "vllm>=0.4.2", "interegular", "pydantic", "pillow", "packaging", "huggingface_hub", "hf_transfer", "outlines>=0.0.34"]
 openai = ["openai>=1.0", "numpy", "tiktoken"]
 anthropic = ["anthropic>=0.20.0", "numpy"]
 all = ["sglang[srt]", "sglang[openai]", "sglang[anthropic]"]

python/sglang/__init__.py

@@ -19,6 +19,7 @@ from sglang.api import (
     user,
     user_begin,
     user_end,
+    video,
 )
 
 # SGL Backends
@@ -46,6 +47,7 @@ __all__ = [
     "gen_int",
     "gen_string",
     "image",
+    "video",
     "select",
     "system",
     "user",

python/sglang/api.py

@@ -15,6 +15,7 @@ from sglang.lang.ir import (
     SglRoleBegin,
     SglRoleEnd,
     SglSelect,
+    SglVideo,
 )
 
 
@@ -151,6 +152,10 @@ def image(expr: SglExpr):
     return SglImage(expr)
 
 
+def video(path: str, num_frames: int):
+    return SglVideo(path, num_frames)
+
+
 def select(
     name: Optional[str] = None,
     choices: List[str] = None,

python/sglang/lang/chat_template.py

@@ -259,6 +259,8 @@ def match_vicuna(model_path: str):
         return get_chat_template("vicuna_v1.1")
     if "llava-v1.5" in model_path.lower():
         return get_chat_template("vicuna_v1.1")
+    if "llava-next-video-7b" in model_path.lower():
+        return get_chat_template("vicuna_v1.1")
 
 
 @register_chat_template_matching_function
@@ -283,19 +285,24 @@ def match_llama3_instruct(model_path: str):
 
 @register_chat_template_matching_function
 def match_chat_ml(model_path: str):
+    # import pdb;pdb.set_trace()
     model_path = model_path.lower()
     if "tinyllama" in model_path:
         return get_chat_template("chatml")
     if "qwen" in model_path and "chat" in model_path:
         return get_chat_template("chatml")
-    if "llava-v1.6-34b" in model_path:
+    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
+    ):
         return get_chat_template("chatml-llava")
 
 
 @register_chat_template_matching_function
 def match_chat_yi(model_path: str):
     model_path = model_path.lower()
-    if "yi" in model_path:
+    if "yi" in model_path and "llava" not in model_path:
         return get_chat_template("yi")
 
 

python/sglang/lang/interpreter.py

@@ -28,8 +28,9 @@ from sglang.lang.ir import (
     SglVariable,
     SglVarScopeBegin,
     SglVarScopeEnd,
+    SglVideo,
 )
-from sglang.utils import encode_image_base64, get_exception_traceback
+from sglang.utils import encode_image_base64, encode_video_base64, get_exception_traceback
 
 
 def run_internal(state, program, func_args, func_kwargs, sync):
@@ -361,6 +362,8 @@ class StreamExecutor:
             self._execute_role_end(other)
         elif isinstance(other, SglImage):
             self._execute_image(other)
+        elif isinstance(other, SglVideo):
+            self._execute_video(other)
         elif isinstance(other, SglVariable):
             self._execute_variable(other)
         elif isinstance(other, SglVarScopeBegin):
@@ -397,6 +400,16 @@ class StreamExecutor:
         self.cur_images.append((path, base64_data))
         self.text_ += self.chat_template.image_token
 
+    def _execute_video(self, expr: SglVideo):
+        path = expr.path
+        num_frames = expr.num_frames
+
+        base64_data = encode_video_base64(path, num_frames)
+
+        self.images_.append((path, base64_data))
+        self.cur_images.append((path, base64_data))
+        self.text_ += self.chat_template.image_token
+
         # if global_config.eager_fill_image:
         #     self.backend.fill_image(self)
 

python/sglang/lang/ir.py

@@ -330,6 +330,15 @@ class SglImage(SglExpr):
         return f"SglImage({self.path})"
 
 
+class SglVideo(SglExpr):
+    def __init__(self, path, num_frames):
+        self.path = path
+        self.num_frames = num_frames
+
+    def __repr__(self) -> str:
+        return f"SglVideo({self.path}, {self.num_frames})"
+
+
 class SglGen(SglExpr):
     def __init__(
         self,

python/sglang/lang/tracer.py

@@ -110,7 +110,7 @@ class TracerProgramState(ProgramState):
     ##################################
 
     def fork(self, size: int = 1, position_ids_offset: Optional[List[int]] = None):
-        assert (size >= 1)
+        assert size >= 1
 
         if self.only_trace_prefix:
             raise StopTracing()

python/sglang/launch_server.py

@@ -2,7 +2,6 @@ import argparse
 
 from sglang.srt.server import ServerArgs, launch_server
 
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     ServerArgs.add_cli_args(parser)

python/sglang/launch_server_llavavid.py

+import argparse
+import multiprocessing as mp
+
+from sglang.srt.server import ServerArgs, launch_server
+
+if __name__ == "__main__":
+
+    model_overide_args = {}
+
+    model_overide_args["mm_spatial_pool_stride"] = 2
+    model_overide_args["architectures"] = ["LlavaVidForCausalLM"]
+    model_overide_args["num_frames"] = 16
+    model_overide_args["model_type"] = "llavavid"
+    if model_overide_args["num_frames"] == 32:
+        model_overide_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
+        model_overide_args["max_sequence_length"] = 4096 * 2
+        model_overide_args["tokenizer_model_max_length"] = 4096 * 2
+        model_overide_args["model_max_length"] = 4096 * 2
+
+    parser = argparse.ArgumentParser()
+    ServerArgs.add_cli_args(parser)
+    args = parser.parse_args()
+
+    if "34b" in args.model_path.lower():
+        model_overide_args["image_token_index"] = 64002
+
+    server_args = ServerArgs.from_cli_args(args)
+
+    pipe_reader, pipe_writer = mp.Pipe(duplex=False)
+
+    launch_server(server_args, pipe_writer, model_overide_args)

python/sglang/srt/hf_transformers_utils.py

@@ -30,10 +30,17 @@ def get_config_json(model_path: str):
     return config
 
 
-def get_config(model: str, trust_remote_code: bool, revision: Optional[str] = None):
+def get_config(
+    model: str,
+    trust_remote_code: bool,
+    revision: Optional[str] = None,
+    model_overide_args: Optional[dict] = None,
+):
     config = AutoConfig.from_pretrained(
         model, trust_remote_code=trust_remote_code, revision=revision
     )
+    if model_overide_args:
+        config.update(model_overide_args)
     return config
 
 

python/sglang/srt/managers/router/manager.py

@@ -60,9 +60,7 @@ class RouterManager:
 
 
 def start_router_process(
-    server_args: ServerArgs,
-    port_args: PortArgs,
-    pipe_writer,
+    server_args: ServerArgs, port_args: PortArgs, pipe_writer, model_overide_args
 ):
     logging.basicConfig(
         level=getattr(logging, server_args.log_level.upper()),
@@ -70,7 +68,7 @@ def start_router_process(
     )
 
     try:
-        model_client = ModelRpcClient(server_args, port_args)
+        model_client = ModelRpcClient(server_args, port_args, model_overide_args)
         router = RouterManager(model_client, port_args)
     except Exception:
         pipe_writer.send(get_exception_traceback())

python/sglang/srt/managers/router/model_rpc.py

@@ -4,12 +4,13 @@ import multiprocessing
 import time
 import warnings
 from concurrent.futures import ThreadPoolExecutor
-from typing import List
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import rpyc
 import torch
 from rpyc.utils.classic import obtain
 from rpyc.utils.server import ThreadedServer
+
 try:
     from vllm.logger import _default_handler as vllm_default_logger
 except ImportError:
@@ -48,6 +49,7 @@ class ModelRpcServer:
         tp_rank: int,
         server_args: ServerArgs,
         port_args: PortArgs,
+        model_overide_args: Optional[dict] = None,
     ):
         server_args, port_args = [obtain(x) for x in [server_args, port_args]]
 
@@ -62,6 +64,7 @@ class ModelRpcServer:
             server_args.model_path,
             server_args.trust_remote_code,
             context_length=server_args.context_length,
+            model_overide_args=model_overide_args,
         )
 
         # For model end global settings
@@ -673,13 +676,15 @@ class ModelRpcService(rpyc.Service):
 
 
 class ModelRpcClient:
-    def __init__(self, server_args: ServerArgs, port_args: PortArgs):
+    def __init__(
+        self, server_args: ServerArgs, port_args: PortArgs, model_overide_args
+    ):
         tp_size = server_args.tp_size
 
         if tp_size == 1:
             # Init model
             self.model_server = ModelRpcService().exposed_ModelRpcServer(
-                0, server_args, port_args
+                0, server_args, port_args, model_overide_args
             )
 
             # Wrap functions
@@ -700,7 +705,7 @@ class ModelRpcClient:
                 # Init model
                 def init_model(i):
                     return self.remote_services[i].ModelRpcServer(
-                        i, server_args, port_args
+                        i, server_args, port_args, model_overide_args
                     )
 
                 self.model_servers = executor.map(init_model, range(tp_size))
@@ -723,7 +728,11 @@ def _init_service(port):
     t = ThreadedServer(
         ModelRpcService(),
         port=port,
-        protocol_config={"allow_pickle": True, "sync_request_timeout": 1800},
+        protocol_config={
+            "allow_public_attrs": True,
+            "allow_pickle": True,
+            "sync_request_timeout": 1800,
+        },
     )
     t.start()
 
@@ -739,7 +748,11 @@ def start_model_process(port):
             con = rpyc.connect(
                 "localhost",
                 port,
-                config={"allow_pickle": True, "sync_request_timeout": 1800},
+                config={
+                    "allow_public_attrs": True,
+                    "allow_pickle": True,
+                    "sync_request_timeout": 1800,
+                },
             )
             break
         except ConnectionRefusedError:

python/sglang/srt/managers/router/model_runner.py

@@ -9,11 +9,11 @@ from typing import List
 
 import numpy as np
 import torch
+from vllm.distributed import initialize_model_parallel
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.model_loader.utils import set_default_torch_dtype
-from vllm.distributed import initialize_model_parallel
 
 from sglang.srt.managers.router.infer_batch import Batch, ForwardMode
 from sglang.srt.memory_pool import ReqToTokenPool, TokenToKVPool
@@ -143,7 +143,7 @@ class InputMetadata:
                 self.kv_last_page_len,
                 self.model_runner.model_config.num_attention_heads // tp_size,
                 self.model_runner.model_config.num_key_value_heads // tp_size,
-                self.model_runner.model_config.head_dim
+                self.model_runner.model_config.head_dim,
             ]
 
             self.prefill_wrapper.begin_forward(*args)

python/sglang/srt/managers/tokenizer_manager.py

@@ -60,7 +60,15 @@ def get_pixel_values(
 ):
     try:
         processor = processor or global_processor
-        image = load_image(image_data)
+        image, image_size = load_image(image_data)
+        if image_size != None:
+            image_hash = hash(image_data)
+            pixel_values = processor.image_processor(image)["pixel_values"]
+            for _ in range(len(pixel_values)):
+                pixel_values[_] = pixel_values[_].astype(np.float16)
+            pixel_values = np.stack(pixel_values, axis=0)
+            return pixel_values, image_hash, image_size
+        else:
             image_hash = hash(image_data)
             if image_aspect_ratio == "pad":
                 image = expand2square(
@@ -84,6 +92,7 @@ class TokenizerManager:
         self,
         server_args: ServerArgs,
         port_args: PortArgs,
+        model_overide_args: dict = None,
     ):
         self.server_args = server_args
 
@@ -96,7 +105,9 @@ class TokenizerManager:
 
         self.model_path = server_args.model_path
         self.hf_config = get_config(
-            self.model_path, trust_remote_code=server_args.trust_remote_code
+            self.model_path,
+            trust_remote_code=server_args.trust_remote_code,
+            model_overide_args=model_overide_args,
         )
         self.context_len = get_context_length(self.hf_config)