108 lines
2.8 KiB
Markdown
108 lines
2.8 KiB
Markdown
# Multi-NPU (QwQ 32B)
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## Run vllm-ascend on Multi-NPU
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Run docker container:
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```{code-block} bash
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:substitutions:
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# Update the vllm-ascend image
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export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
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docker run --rm \
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--name vllm-ascend \
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--device /dev/davinci0 \
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--device /dev/davinci1 \
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--device /dev/davinci2 \
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--device /dev/davinci3 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-p 8000:8000 \
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-it $IMAGE bash
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```
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Setup environment variables:
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```bash
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# Load model from ModelScope to speed up download
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export VLLM_USE_MODELSCOPE=True
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# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
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export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
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```
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### Online Inference on Multi-NPU
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Run the following script to start the vLLM server on Multi-NPU:
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```bash
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vllm serve Qwen/QwQ-32B --max-model-len 4096 --port 8000 -tp 4
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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:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Qwen/QwQ-32B",
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"prompt": "QwQ-32B是什么？",
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"max_tokens": "128",
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"top_p": "0.95",
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"top_k": "40",
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"temperature": "0.6"
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}'
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```
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### Offline Inference on Multi-NPU
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Run the following script to execute offline inference on multi-NPU:
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```python
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import gc
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import torch
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from vllm import LLM, SamplingParams
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from vllm.distributed.parallel_state import (destroy_distributed_environment,
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destroy_model_parallel)
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def clean_up():
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destroy_model_parallel()
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destroy_distributed_environment()
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gc.collect()
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torch.npu.empty_cache()
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prompts = [
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"Hello, my name is",
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"The future of AI is",
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]
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sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
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llm = LLM(model="Qwen/QwQ-32B",
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tensor_parallel_size=4,
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distributed_executor_backend="mp",
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max_model_len=4096)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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del llm
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clean_up()
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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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Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
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Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'
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```
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