204 lines
9.3 KiB
Markdown
204 lines
9.3 KiB
Markdown
# Single XPU (Qwen3-VL-32B)
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## Run vllm-kunlun on Single XPU
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Setup environment using container:
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```bash
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# !/bin/bash
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# rundocker.sh
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XPU_NUM=8
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DOCKER_DEVICE_CONFIG=""
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if [ $XPU_NUM -gt 0 ]; then
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for idx in $(seq 0 $((XPU_NUM-1))); do
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DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpu${idx}:/dev/xpu${idx}"
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done
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DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpuctrl:/dev/xpuctrl"
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fi
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export build_image="xxxxxxxxxxxxxxxxx"
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docker run -itd ${DOCKER_DEVICE_CONFIG} \
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--net=host \
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--cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
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--tmpfs /dev/shm:rw,nosuid,nodev,exec,size=32g \
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--cap-add=SYS_PTRACE \
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-v /home/users/vllm-kunlun:/home/vllm-kunlun \
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-v /usr/local/bin/xpu-smi:/usr/local/bin/xpu-smi \
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--name "$1" \
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-w /workspace \
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"$build_image" /bin/bash
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```
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### Offline Inference on Single XPU
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Start the server in a container:
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```bash
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from vllm import LLM, SamplingParams
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def main():
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model_path = "/models/Qwen3-VL-32B"
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llm_params = {
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"model": model_path,
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"tensor_parallel_size": 1,
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"trust_remote_code": True,
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"dtype": "float16",
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"enable_chunked_prefill": False,
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"distributed_executor_backend": "mp",
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"max_model_len": 16384,
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"gpu_memory_utilization": 0.9,
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}
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llm = LLM(**llm_params)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "tell a joke"
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}
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]
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}
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]
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sampling_params = SamplingParams(
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max_tokens=200,
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temperature=1.0,
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top_k=50,
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top_p=1.0
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)
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outputs = llm.chat(messages, sampling_params=sampling_params)
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response = outputs[0].outputs[0].text
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print("=" * 50)
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print("Input content:", messages)
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print("Model response:\n", response)
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print("=" * 50)
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if __name__ == "__main__":
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main()
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```
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:::::
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If you run this script successfully, you can see the info shown below:
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```bash
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==================================================
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Input content: [{'role': 'user', 'content': [{'type': 'text', 'text': 'tell a joke'}]}]
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Model response:
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Why don’t skeletons fight each other?
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Because they don’t have the guts! 🦴😄
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==================================================
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```
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### Online Serving on Single XPU
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Start the vLLM server on a single XPU:
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--host 0.0.0.0 \
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--port 9988 \
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--model /models/Qwen3-VL-32B \
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--gpu-memory-utilization 0.9 \
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--trust-remote-code \
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--max-model-len 32768 \
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--tensor-parallel-size 1 \
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--dtype float16 \
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--max_num_seqs 128 \
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--max_num_batched_tokens 32768 \
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--block-size 128 \
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--no-enable-prefix-caching \
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--no-enable-chunked-prefill \
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--distributed-executor-backend mp \
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--served-model-name Qwen3-VL-32B \
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--compilation-config '{"splitting_ops": ["vllm.unified_attention",
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"vllm.unified_attention_with_output",
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"vllm.unified_attention_with_output_kunlun",
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"vllm.mamba_mixer2",
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"vllm.mamba_mixer",
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"vllm.short_conv",
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"vllm.linear_attention",
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"vllm.plamo2_mamba_mixer",
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"vllm.gdn_attention",
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"vllm.sparse_attn_indexer"]}
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#Version 0.11.0
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```
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If your service start successfully, you can see the info shown below:
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```bash
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(APIServer pid=109442) INFO: Started server process [109442]
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(APIServer pid=109442) INFO: Waiting for application startup.
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(APIServer pid=109442) INFO: Application startup complete.
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```
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Once your server is started, you can query the model with input prompts:
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```bash
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curl http://localhost:9988/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Qwen3-VL-32B",
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"prompt": "你好！你是谁?",
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"max_tokens": 100,
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"temperature": 0
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}'
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```
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If you query the server successfully, you can see the info shown below (client):
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```bash
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{"id":"cmpl-4f61fe821ff34f23a91baade5de5103e","object":"text_completion","created":1768876583,"model":"Qwen3-VL-32B","choices":[{"index":0,"text":" 你好！我是通义千问，是阿里云研发的超大规模语言模型。我能够回答问题、创作文字、编程等，还能根据你的需求进行多轮对话。有什么我可以帮你的吗？😊\n\n（温馨提示：我是一个AI助手，虽然我尽力提供准确和有用的信息，但请记得在做重要决策时，最好结合专业意见或进一步核实信息哦！）","logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":5,"total_tokens":90,"completion_tokens":85,"prompt_tokens_details":null},"kv_transfer_params":null}
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```
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Logs of the vllm server:
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```bash
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(APIServer pid=109442) INFO: 127.0.0.1:19962 - "POST /v1/completions HTTP/1.1" 200 OK
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(APIServer pid=109442) INFO 01-20 10:36:28 [loggers.py:127] Engine 000: Avg prompt throughput: 0.5 tokens/s, Avg generation throughput: 8.5 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
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(APIServer pid=109442) INFO 01-20 10:36:38 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
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(APIServer pid=109442) INFO 01-20 10:43:23 [chat_utils.py:560] Detected the chat template content format to be 'openai'. You can set `--chat-template-content-format` to override this.
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(APIServer pid=109442) INFO 01-20 10:43:28 [loggers.py:127] Engine 000: Avg prompt throughput: 9.0 tokens/s, Avg generation throughput: 6.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.5%, Prefix cache hit rate: 0.0%
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```
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Input an image for testing.Here,a python script is used:
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```python
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import requests
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import base64
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API_URL = "http://localhost:9988/v1/chat/completions"
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MODEL_NAME = "Qwen3-VL-32B"
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IMAGE_PATH = "/images.jpeg"
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def encode_image(image_path):
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode('utf-8')
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base64_image = encode_image(IMAGE_PATH)
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payload = {
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"model": MODEL_NAME,
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "你好！请描述一下这张图片。"
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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]
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}
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],
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"max_tokens": 300,
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"temperature": 0.1
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}
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response = requests.post(API_URL, json=payload)
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print(response.json())
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```
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If you query the server successfully, you can see the info shown below (client):
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```bash
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{'id': 'chatcmpl-4b42fe46f2c84991b0af5d5e1ffad9ba', 'object': 'chat.completion', 'created': 1768877003, 'model': 'Qwen3-VL-32B', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '你好！这张图片展示的是“Hugging Face”的标志。\n\n图片左侧是一个黄色的圆形表情符号（emoji），它有着圆圆的眼睛、张开的嘴巴露出微笑，双手合拢在脸颊两侧，做出一个拥抱或欢迎的姿态，整体传达出友好、温暖和亲切的感觉。\n\n图片右侧是黑色的英文文字“Hugging Face”，字体简洁现代，与左侧的表情符号相呼应。\n\n整个标志设计简洁明了，背景为纯白色，突出了标志本身。这个标志属于Hugging Face公司，它是一家知名的开源人工智能公司，尤其在自然语言处理（NLP）领域以提供预训练模型（如Transformers库）和模型托管平台而闻名。\n\n整体来看，这个标志通过可爱的表情符号和直白的文字，成功传达了公司“拥抱”技术、开放共享、友好的品牌理念。', 'refusal': None, 'annotations': None, 'audio': None, 'function_call': None, 'tool_calls': [], 'reasoning_content': None}, 'logprobs': None, 'finish_reason': 'stop', 'stop_reason': None, 'token_ids': None}], 'service_tier': None, 'system_fingerprint': None, 'usage': {'prompt_tokens': 90, 'total_tokens': 266, 'completion_tokens': 176, 'prompt_tokens_details': None}, 'prompt_logprobs': None, 'prompt_token_ids': None, 'kv_transfer_params': None}
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```
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Logs of the vllm server:
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```bash
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(APIServer pid=109442) INFO: 127.0.0.1:26854 - "POST /v1/chat/completions HTTP/1.1" 200 OK
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(APIServer pid=109442) INFO 01-20 10:43:38 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 10.7 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
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(APIServer pid=109442) INFO 01-20 10:43:48 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
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```
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