v0.10.1rc1

2025-09-09 09:40:35 +08:00
parent d6f6ef41fe
commit 9149384e03
432 changed files with 84698 additions and 1 deletions
--- a/docs/source/tutorials/multi_npu_quantization.md
+++ b/docs/source/tutorials/multi_npu_quantization.md
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+# 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 quantized 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"
+    }'
+```
+
+Run the following script to execute offline inference on multi-NPU with quantized model:
+
+:::{note}
+To enable quantization for ascend, quantization method must be "ascend"
+:::
+
+```python
+import gc
+
+import torch
+
+from vllm import LLM, SamplingParams
+from vllm.distributed.parallel_state import (destroy_distributed_environment,
+                                             destroy_model_parallel)
+
+def clean_up():
+    destroy_model_parallel()
+    destroy_distributed_environment()
+    gc.collect()
+    torch.npu.empty_cache()
+
+prompts = [
+    "Hello, my name is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
+
+llm = LLM(model="/home/models/QwQ-32B-w8a8",
+          tensor_parallel_size=4,
+          distributed_executor_backend="mp",
+          max_model_len=4096,
+          quantization="ascend")
+
+outputs = llm.generate(prompts, sampling_params)
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+del llm
+clean_up()
+```