d5401a08be
1. update modelslim version to fix deepseek related issues 2. add note for "--quantization ascend" Signed-off-by: 22dimensions <waitingwind@foxmail.com>
92 lines
2.8 KiB
Markdown
92 lines
2.8 KiB
Markdown
# Multi-NPU (QwQ 32B W8A8)
|
|
|
|
## Run docker container:
|
|
:::{note}
|
|
w8a8 quantization feature is supported by v0.8.4rc2 or higher
|
|
:::
|
|
|
|
```{code-block} bash
|
|
:substitutions:
|
|
# Update the vllm-ascend image
|
|
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
|
|
docker run --rm \
|
|
--name vllm-ascend \
|
|
--device /dev/davinci0 \
|
|
--device /dev/davinci1 \
|
|
--device /dev/davinci2 \
|
|
--device /dev/davinci3 \
|
|
--device /dev/davinci_manager \
|
|
--device /dev/devmm_svm \
|
|
--device /dev/hisi_hdc \
|
|
-v /usr/local/dcmi:/usr/local/dcmi \
|
|
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
|
|
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
|
|
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
|
|
-v /etc/ascend_install.info:/etc/ascend_install.info \
|
|
-v /root/.cache:/root/.cache \
|
|
-p 8000:8000 \
|
|
-it $IMAGE bash
|
|
```
|
|
|
|
## Install modelslim and convert model
|
|
:::{note}
|
|
You can choose to convert the model yourself or use the quantized model we uploaded,
|
|
see https://www.modelscope.cn/models/vllm-ascend/QwQ-32B-W8A8
|
|
:::
|
|
|
|
```bash
|
|
# (Optional)This tag is recommended and has been verified
|
|
git clone https://gitee.com/ascend/msit -b modelslim-VLLM-8.1.RC1.b020_001
|
|
|
|
cd msit/msmodelslim
|
|
# Install by run this script
|
|
bash install.sh
|
|
pip install accelerate
|
|
|
|
cd example/Qwen
|
|
# Original weight path, Replace with your local model path
|
|
MODEL_PATH=/home/models/QwQ-32B
|
|
# Path to save converted weight, Replace with your local path
|
|
SAVE_PATH=/home/models/QwQ-32B-w8a8
|
|
|
|
# In this conversion process, the npu device is not must, you can also set --device_type cpu to have a conversion
|
|
python3 quant_qwen.py --model_path $MODEL_PATH --save_directory $SAVE_PATH --calib_file ../common/boolq.jsonl --w_bit 8 --a_bit 8 --device_type npu --anti_method m1 --trust_remote_code True
|
|
```
|
|
|
|
## Verify the quantized model
|
|
The converted model files looks like:
|
|
```bash
|
|
.
|
|
|-- config.json
|
|
|-- configuration.json
|
|
|-- generation_config.json
|
|
|-- quant_model_description.json
|
|
|-- quant_model_weight_w8a8.safetensors
|
|
|-- README.md
|
|
|-- tokenizer.json
|
|
`-- tokenizer_config.json
|
|
```
|
|
|
|
Run the following script to start the vLLM server with quantize model:
|
|
|
|
:::{note}
|
|
The value "ascend" for "--quantization" argument will be supported after [a specific PR](https://github.com/vllm-project/vllm-ascend/pull/877) is merged and released, you can cherry-pick this commit for now.
|
|
:::
|
|
```bash
|
|
vllm serve /home/models/QwQ-32B-w8a8 --tensor-parallel-size 4 --served-model-name "qwq-32b-w8a8" --max-model-len 4096 --quantization ascend
|
|
```
|
|
|
|
Once your server is started, you can query the model with input prompts
|
|
```bash
|
|
curl http://localhost:8000/v1/completions \
|
|
-H "Content-Type: application/json" \
|
|
-d '{
|
|
"model": "qwq-32b-w8a8",
|
|
"prompt": "what is large language model?",
|
|
"max_tokens": "128",
|
|
"top_p": "0.95",
|
|
"top_k": "40",
|
|
"temperature": "0.0"
|
|
}'
|
|
```
|