94 lines
3.0 KiB
Markdown
94 lines
3.0 KiB
Markdown
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# Multi-NPU (deepseek-v2-lite-w8a8)
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## Run docker container:
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:::{note}
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w8a8 quantization feature is supported by v0.8.4rc2 or highter
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:::
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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=m.daocloud.io/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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## Install modelslim and convert model
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:::{note}
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You can choose to convert the model yourself or use the quantized model we uploaded,
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see https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V2-Lite-w8a8
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:::
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```bash
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git clone https://gitee.com/ascend/msit
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# (Optional)This commit has been verified
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git checkout a396750f930e3bd2b8aa13730401dcbb4bc684ca
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cd msit/msmodelslim
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# Install by run this script
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bash install.sh
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pip install accelerate
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cd /msit/msmodelslim/example/DeepSeek
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# Original weight path, Replace with your local model path
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MODEL_PATH=/home/weight/DeepSeek-V2-Lite
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# Path to save converted weight, Replace with your local path
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SAVE_PATH=/home/weight/DeepSeek-V2-Lite-w8a8
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mkdir -p $SAVE_PATH
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# In this conversion process, the npu device is not must, you can also set --device_type cpu to have a conversion
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python3 quant_deepseek.py --model_path $MODEL_PATH --save_directory $SAVE_PATH --device_type npu --act_method 2 --w_bit 8 --a_bit 8 --is_dynamic True
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```
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## Verify the quantized model
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The converted model files looks like:
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```bash
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.
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|-- config.json
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|-- configuration_deepseek.py
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|-- fusion_result.json
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|-- generation_config.json
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|-- quant_model_description_w8a8_dynamic.json
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|-- quant_model_weight_w8a8_dynamic-00001-of-00004.safetensors
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|-- quant_model_weight_w8a8_dynamic-00002-of-00004.safetensors
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|-- quant_model_weight_w8a8_dynamic-00003-of-00004.safetensors
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|-- quant_model_weight_w8a8_dynamic-00004-of-00004.safetensors
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|-- quant_model_weight_w8a8_dynamic.safetensors.index.json
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|-- tokenization_deepseek_fast.py
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|-- tokenizer.json
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`-- tokenizer_config.json
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```
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Run the following script to start the vLLM server with quantize model:
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```bash
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vllm serve /home/weight/DeepSeek-V2-Lite-w8a8 --tensor-parallel-size 4 --trust-remote-code --served-model-name "dpsk-w8a8" --max-model-len 4096
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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": "dpsk-w8a8",
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"prompt": "what is deepseek？",
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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": "0.0"
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}'
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```
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