lmms-lab

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llavanext-qwen-tokenizer

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LMMs-Lab 235

@kcz358

This tokenizer repo is used for llava qwen on sglang. I don't think you can load it using the llava model in hf

This tokenizer repo is used for llava qwen on sglang. I don't think you can load it using the llava model in hf

@KC I'm loading it like this:

runtime = sgl.Runtime(
model_path=MODEL_PATH,
tokenizer_path=TOKENIZER_PATH,
tp_size=GPU_CONFIG.count,
log_evel="debug",
enable_flashinfer=True,
)
sgl.set_default_backend(runtime)

Can not reproduce using the srt_example_llava

"""
Usage: python3 srt_example_llava.py
"""
import sglang as sgl




@sgl
	.function
def image_qa(s, image_path, question):
    s += sgl.user(sgl.image(image_path) + question)
    s += sgl.assistant(sgl.gen("answer"))


def single():
    state = image_qa.run(
        image_path="images/cat.jpeg",
        question="What is this?",
        max_new_tokens=128)
    print(state["answer"], "\n")


def stream():
    state = image_qa.run(
        image_path="images/cat.jpeg",
        question="What is this?",
        max_new_tokens=64,
        stream=True)

    for out in state.text_iter("answer"):
        print(out, end="", flush=True)
    print()


def batch():
    states = image_qa.run_batch(
        [
            {"image_path": "images/cat.jpeg", "question":"What is this?"},
            {"image_path": "images/dog.jpeg", "question":"What is this?"},
        ],
        max_new_tokens=128,
    )
    for s in states:
        print(s["answer"], "\n")


if __name__ == "__main__":
    runtime = sgl.Runtime(model_path="lmms-lab/llava-next-72b",
                          tokenizer_path="lmms-lab/llavanext-qwen-tokenizer", tp_size=8)
    sgl.set_default_backend(runtime)
    print(f"chat template: {runtime.endpoint.chat_template.name}")

    # Or you can use API models
    # sgl.set_default_backend(sgl.OpenAI("gpt-4-vision-preview"))
    # sgl.set_default_backend(sgl.VertexAI("gemini-pro-vision"))

    # Run a single request
    print("\n========== single ==========\n")
    single()

    # Stream output
    print("\n========== stream ==========\n")
    stream()

    # Run a batch of requests
    print("\n========== batch ==========\n")
    batch()

    runtime.shutdown()

If it is internet related error you should check your internet settings

Can not reproduce using the srt_example_llava

"""
Usage: python3 srt_example_llava.py
"""
import sglang as sgl




@sgl
	.function
def image_qa(s, image_path, question):
    s += sgl.user(sgl.image(image_path) + question)
    s += sgl.assistant(sgl.gen("answer"))


def single():
    state = image_qa.run(
        image_path="images/cat.jpeg",
        question="What is this?",
        max_new_tokens=128)
    print(state["answer"], "\n")


def stream():
    state = image_qa.run(
        image_path="images/cat.jpeg",
        question="What is this?",
        max_new_tokens=64,
        stream=True)

    for out in state.text_iter("answer"):
        print(out, end="", flush=True)
    print()


def batch():
    states = image_qa.run_batch(
        [
            {"image_path": "images/cat.jpeg", "question":"What is this?"},
            {"image_path": "images/dog.jpeg", "question":"What is this?"},
        ],
        max_new_tokens=128,
    )
    for s in states:
        print(s["answer"], "\n")


if __name__ == "__main__":
    runtime = sgl.Runtime(model_path="lmms-lab/llava-next-72b",
                          tokenizer_path="lmms-lab/llavanext-qwen-tokenizer", tp_size=8)
    sgl.set_default_backend(runtime)
    print(f"chat template: {runtime.endpoint.chat_template.name}")

    # Or you can use API models
    # sgl.set_default_backend(sgl.OpenAI("gpt-4-vision-preview"))
    # sgl.set_default_backend(sgl.VertexAI("gemini-pro-vision"))

    # Run a single request
    print("\n========== single ==========\n")
    single()

    # Stream output
    print("\n========== stream ==========\n")
    stream()

    # Run a batch of requests
    print("\n========== batch ==========\n")
    batch()

    runtime.shutdown()

If it is internet related error you should check your internet settings

@kcz358 Can you please sign up for Modal and run this script? They will give you $30 free credits!

import modal

GPU_CONFIG = modal.gpu.A100(size="80GB", count=4)

MINUTES = 60

MODEL_PATH = "lmms-lab/llava-next-72b"
TOKENIZER_PATH = "lmms-lab/llavanext-qwen-tokenizer"


def download_model():
   import sglang as sgl

   sgl.Runtime(
       model_path=MODEL_PATH,
       tokenizer_path=TOKENIZER_PATH,
       tp_size=GPU_CONFIG.count,
       log_evel="debug",
   )


vllm_image = (
   modal.Image.from_registry(
       "nvidia/cuda:12.2.0-devel-ubuntu22.04", add_python="3.11")
   .apt_install("git", "wget", "cmake")
   .pip_install(
       "wheel==0.43.0",
       "torch==2.3.0",
       "torchvision==0.18.0",
       "transformers==4.40.2",
       "vllm==0.4.2",
       "timm==0.9.12",
       "Pillow==10.3.0",
       "hf-transfer==0.1.6",
       "huggingface_hub==0.22.2",
       "requests==2.31.0",
   )
   .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
   .run_commands("pip install flash-attn --no-build-isolation")
   .run_commands("pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git")
   .run_commands(
       "git clone https://github.com/sgl-project/sglang.git && cd sglang && pip install -e 'python[all]'"
   )
   # .run_function(download_model, gpu=GPU_CONFIG)
   .run_commands("pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/")

)

app = modal.App("app")




@app
	.cls(
   gpu=GPU_CONFIG,
   timeout=20 * MINUTES,
   container_idle_timeout=20 * MINUTES,
   allow_concurrent_inputs=10,
   image=vllm_image,
)
class Model:
   

@modal
	.enter()
   async def start_engine(self):
       import sglang as sgl

       import subprocess

       subprocess.run(["nvidia-smi", "-L"])
       runtime = sgl.Runtime(
           model_path=MODEL_PATH,
           tokenizer_path=TOKENIZER_PATH,
           tp_size=GPU_CONFIG.count,
           log_evel="debug",
           enable_flashinfer=True,
       )
       sgl.set_default_backend(runtime)

   

@modal
	.method()
   async def generate(self):
       print("Generating...")
       import sglang as sgl
       import requests

       response = requests.get(
           "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
       )
       with open("./nyc.png", "wb") as file:
           file.write(response.content)

       

@sgl
	.function
       def image_qa(s, image_path, question):
           s += sgl.user(sgl.image(image_path) + question)
           s += sgl.assistant(sgl.gen("answer"))

       state = image_qa.run(
           image_path="./nyc.png", question="What is this?", max_new_tokens=128
       )
       print(state["answer"], "\n")




@app
	.local_entrypoint()
def main():
   Model().generate.remote()