# Multi XPU (Qwen2.5-VL-32B) ## Run vllm-kunlun on Multi XPU Setup environment using container: ```bash # !/bin/bash # rundocker.sh XPU_NUM=8 DOCKER_DEVICE_CONFIG="" if [ $XPU_NUM -gt 0 ]; then for idx in $(seq 0 $((XPU_NUM-1))); do DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpu${idx}:/dev/xpu${idx}" done DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpuctrl:/dev/xpuctrl" fi export build_image="xxxxxxxxxxxxxxxxx" docker run -itd ${DOCKER_DEVICE_CONFIG} \ --net=host \ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ --tmpfs /dev/shm:rw,nosuid,nodev,exec,size=32g \ --cap-add=SYS_PTRACE \ -v /home/users/vllm-kunlun:/home/vllm-kunlun \ -v /usr/local/bin/xpu-smi:/usr/local/bin/xpu-smi \ --name "$1" \ -w /workspace \ "$build_image" /bin/bash ``` ### Offline Inference on Multi XPU Start the server in a container: ```bash from vllm import LLM, SamplingParams def main(): model_path = "/models/Qwen2.5-VL-32B-Instruct" llm_params = { "model": model_path, "tensor_parallel_size": 2, "trust_remote_code": True, "dtype": "float16", "enable_chunked_prefill": False, "enable_prefix_caching": False, "distributed_executor_backend": "mp", "max_model_len": 16384, "gpu_memory_utilization": 0.9, } llm = LLM(**llm_params) messages = [ { "role": "user", "content": [ { "type": "text", "text": "你好！你是谁？" } ] } ] sampling_params = SamplingParams( max_tokens=200, temperature=0.7, top_k=50, top_p=0.9 ) outputs = llm.chat(messages, sampling_params=sampling_params) response = outputs[0].outputs[0].text print("=" * 50) print("Input content:", messages) print("Model response:\n", response) print("=" * 50) if __name__ == "__main__": main() ``` ::::: If you run this script successfully, you can see the info shown below: ```bash ================================================== Input content: [{'role': 'user', 'content': [{'type': 'text', 'text': '你好！你是谁？'}]}] Model response: 你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。你可以叫我Qwen。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。有什么我可以帮助你的吗？ 😊 ================================================== ``` ### Online Serving on Multi XPU Start the vLLM server on a multi XPU: ```bash python -m vllm.entrypoints.openai.api_server \ --host 0.0.0.0 \ --port 9988 \ --model /models/Qwen2.5-VL-32B-Instruct \ --gpu-memory-utilization 0.9 \ --trust-remote-code \ --max-model-len 32768 \ --tensor-parallel-size 2 \ --dtype float16 \ --max_num_seqs 128 \ --max_num_batched_tokens 32768 \ --block-size 128 \ --no-enable-prefix-caching \ --no-enable-chunked-prefill \ --distributed-executor-backend mp \ --served-model-name Qwen2.5-VL-32B-Instruct \ --compilation-config '{"splitting_ops": ["vllm.unified_attention", "vllm.unified_attention_with_output", "vllm.unified_attention_with_output_kunlun", "vllm.mamba_mixer2", "vllm.mamba_mixer", "vllm.short_conv", "vllm.linear_attention", "vllm.plamo2_mamba_mixer", "vllm.gdn_attention", "vllm.sparse_attn_indexer"]}' #Version 0.11.0 ``` If your service start successfully, you can see the info shown below: ```bash (APIServer pid=110552) INFO: Started server process [110552] (APIServer pid=110552) INFO: Waiting for application startup. (APIServer pid=110552) INFO: Application startup complete. ``` Once your server is started, you can query the model with input prompts: ```bash curl http://localhost:9988/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen2.5-VL-32B-Instruct", "prompt": "你好！你是谁?", "max_tokens": 100, "temperature": 0.7 }' ``` If you query the server successfully, you can see the info shown below (client): ```bash {"id":"cmpl-9784668ac5bc4b4e975d0aa5ee8377c6","object":"text_completion","created":1768898088,"model":"Qwen2.5-VL-32B-Instruct","choices":[{"index":0,"text":" 你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。你可以回答问题、创作文字，如写故事、公文、邮件、剧本等，还能表达\n","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":45,"completion_tokens":40,"prompt_tokens_details":null},"kv_transfer_params":null} ``` Logs of the vllm server: ```bash (APIServer pid=110552) INFO 01-20 16:34:48 [loggers.py:127] Engine 000: Avg prompt throughput: 0.5 tokens/s, Avg generation throughput: 0.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0% (APIServer pid=110552) INFO: 127.0.0.1:17988 - "POST /v1/completions HTTP/1.1" 200 OK (APIServer pid=110552) INFO 01-20 16:34:58 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 3.4 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0% (APIServer pid=110552) INFO 01-20 16:35:08 [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% ``` Input an image for testing.Here,a python script is used: ```python import requests import base64 API_URL = "http://localhost:9988/v1/chat/completions" MODEL_NAME = "Qwen2.5-VL-32B-Instruct" IMAGE_PATH = "/images.jpeg" def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') base64_image = encode_image(IMAGE_PATH) payload = { "model": MODEL_NAME, "messages": [ { "role": "user", "content": [ { "type": "text", "text": "你好！请描述一下这张图片。" }, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{base64_image}" } } ] } ], "max_tokens": 300, "temperature": 0.1, "top_p": 0.9, "top_k": 50 } response = requests.post(API_URL, json=payload) print(response.json()) ``` If you query the server successfully, you can see the info shown below (client): ```bash {'id': 'chatcmpl-9857119aed664a3e8f078efd90defdca', 'object': 'chat.completion', 'created': 1768898198, 'model': 'Qwen2.5-VL-32B-Instruct', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '你好！这张图片展示了一个标志，内容如下：\n\n1. **左侧图标**：\n - 一个黄色的圆形笑脸表情符号。\n - 笑脸的表情非常开心，眼睛眯成弯弯的形状，嘴巴张开露出牙齿，显得非常愉快。\n - 笑脸的双手在胸前做出拥抱的动作，手掌朝外，象征着“拥抱”或“友好的姿态”。\n\n2. **右侧文字**：\n - 文字是英文单词：“Hugging Face”。\n - 字体为黑色，字体风格简洁、现代，看起来像是无衬线字体（sans-serif）。\n\n3. **整体设计**：\n - 整个标志的设计非常简洁明了，颜色对比鲜明（黄色笑脸和黑色文字），背景为纯白色，给人一种干净、友好的感觉。\n - 笑脸和文字之间的间距适中，布局平衡。\n\n这个标志可能属于某个品牌或组织，名字为“Hugging Face”，从设计来看，它传达了一种友好、开放和积极的形象。', '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': 95, 'total_tokens': 311, 'completion_tokens': 216, 'prompt_tokens_details': None}, 'prompt_logprobs': None, 'prompt_token_ids': None, 'kv_transfer_params': None} ``` Logs of the vllm server: ```bash (APIServer pid=110552) INFO: 127.0.0.1:19378 - "POST /v1/chat/completions HTTP/1.1" 200 OK (APIServer pid=110552) INFO 01-20 16:36:49 [loggers.py:127] Engine 000: Avg prompt throughput: 9.5 tokens/s, Avg generation throughput: 21.6 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0% (APIServer pid=110552) INFO 01-20 16:36:59 [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% ```