support llava video

.gitignore

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

README.md

@@ -408,4 +408,4 @@ https://github.com/sgl-project/sglang/issues/157
 }
 ```
 
-We learned from the design and reused some code of the following projects: [Guidance](https://github.com/guidance-ai/guidance), [vLLM](https://github.com/vllm-project/vllm), [LightLLM](https://github.com/ModelTC/lightllm), [FlashInfer](https://github.com/flashinfer-ai/flashinfer), [Outlines](https://github.com/outlines-dev/outlines), [LMQL](https://github.com/eth-sri/lmql).
+We learned from the design and reused some code of the following projects: [Guidance](https://github.com/guidance-ai/guidance), [vLLM](https://github.com/vllm-project/vllm), [LightLLM](https://github.com/ModelTC/lightllm), [FlashInfer](https://github.com/flashinfer-ai/flashinfer), [Outlines](https://github.com/outlines-dev/outlines), [LMQL](https://github.com/eth-sri/lmql).

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

@@ -0,0 +1,208 @@
+"""
+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()

examples/usage/llava_video/srt_example_llava_v.sh

@@ -0,0 +1,130 @@
+#!/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."

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]"]
@@ -33,4 +33,4 @@ all = ["sglang[srt]", "sglang[openai]", "sglang[anthropic]"]
 exclude = ["assets*", "benchmark*", "docs*", "dist*", "playground*", "scripts*", "tests*"]
 
 [tool.wheel]
-exclude = ["assets*", "benchmark*", "docs*", "dist*", "playground*", "scripts*", "tests*"]
+exclude = ["assets*", "benchmark*", "docs*", "dist*", "playground*", "scripts*", "tests*"]