6.2 KiB
6.2 KiB
Single XPU (Qwen3-8B)
Run vllm-kunlun on Single XPU
Setup environment using container:
# !/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 Single XPU
Start the server in a container:
from vllm import LLM, SamplingParams
def main():
model_path = "/models/Qwen3-8B"
llm_params = {
"model": model_path,
"tensor_parallel_size": 1,
"trust_remote_code": True,
"dtype": "float16",
"enable_chunked_prefill": False,
"distributed_executor_backend": "mp",
}
llm = LLM(**llm_params)
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "tell a joke"
}
]
}
]
sampling_params = SamplingParams(
max_tokens=200,
temperature=1.0,
top_k=50,
top_p=1.0,
stop_token_ids=[181896]
)
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:
==================================================
Input content: [{'role': 'user', 'content': [{'type': 'text', 'text': 'tell a joke'}]}]
Model response:
<think>
Okay, the user asked me to tell a joke. First, I need to consider the user's needs. They might just want to relax or need some entertainment. Next, I need to choose a suitable joke that is not too complicated, easy to understand, and also interesting.
The user might expect the joke to be in Chinese, so I need to ensure that the joke conforms to the language habits and cultural background of Chinese. I need to avoid sensitive topics, such as politics, religion, or anything that might cause misunderstanding. Then, I have to consider the structure of the joke, which usually involves a setup and an unexpected ending to create humor.
For example, I could tell a light-hearted story about everyday life, such as animals or common scenarios. For instance, the story of a turtle and a rabbit racing, but with a twist. However, I need to ensure that the joke is of moderate length and not too long, so the user doesn't lose interest. Additionally, I should pay attention to using colloquial language and avoid stiff or complex sentence structures.
I might also need to check if this joke is common to avoid repetition. If the user has heard something similar before, I may need to come up with a different angle.
==================================================
Online Serving on Single XPU
Start the vLLM server on a single XPU:
python -m vllm.entrypoints.openai.api_server \
--host 0.0.0.0 \
--port 9000 \
--model /models/Qwen3-8B\
--gpu-memory-utilization 0.9 \
--trust-remote-code \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype float16 \
--max_num_seqs 128 \
--max_num_batched_tokens 32768 \
--max-seq-len-to-capture 32768 \
--block-size 128 \
--no-enable-prefix-caching \
--no-enable-chunked-prefill \
--distributed-executor-backend mp \
--served-model-name Qwen3-8B \
--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"]}' \
If your service start successfully, you can see the info shown below:
(APIServer pid=118459) INFO: Started server process [118459]
(APIServer pid=118459) INFO: Waiting for application startup.
(APIServer pid=118459) INFO: Application startup complete.
Once your server is started, you can query the model with input prompts:
curl http://localhost:9000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen3-8B",
"prompt": "What is your name?",
"max_tokens": 100,
"temperature": 0
}'
If you query the server successfully, you can see the info shown below (client):
{"id":"cmpl-80ee8b893dc64053947b0bea86352faa","object":"text_completion","created":1763015742,"model":"Qwen3-8B","choices":[{"index":0,"text":" is the S, and ,","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":5,"total_tokens":12,"completion_tokens":7,"prompt_tokens_details":null},"kv_transfer_params":null}
Logs of the vllm server:
(APIServer pid=54567) INFO: 127.0.0.1:60338 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=54567) INFO 11-13 14:35:48 [loggers.py:123] Engine 000: Avg prompt throughput: 0.5 tokens/s, Avg generation throughput: 0.7 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%