# Installation This document describes how to install vllm-ascend manually. ## Requirements - OS: Linux - Python: 3.9 or higher - A hardware with Ascend NPU. It's usually the Atlas 800 A2 series. - Software: | Software | Supported version | Note | | ------------ | ----------------- | ---- | | CANN | >= 8.0.0 | Required for vllm-ascend and torch-npu | | torch-npu | >= 2.5.1.dev20250226 | Required for vllm-ascend | | torch | >= 2.5.1 | Required for torch-npu and vllm | You have 2 way to install: - **Using pip**: first prepare env manually or via CANN image, then install `vllm-ascend` using pip. - **Using docker**: use the `vllm-ascend` pre-built docker image directly. ## Configure a new environment Before installing, you need to make sure firmware/driver and CANN are installed correctly, refer to [link](https://ascend.github.io/docs/sources/ascend/quick_install.html) for more details. ### Configure hardware environment To verify that the Ascend NPU firmware and driver were correctly installed, run: ```bash npu-smi info ``` Refer to [Ascend Environment Setup Guide](https://ascend.github.io/docs/sources/ascend/quick_install.html) for more details. ### Configure software environment :::::{tab-set} :sync-group: install ::::{tab-item} Before using pip :selected: :sync: pip The easiest way to prepare your software environment is using CANN image directly: ```bash # Update DEVICE according to your device (/dev/davinci[0-7]) export DEVICE=/dev/davinci7 docker run --rm \ --name vllm-ascend-env \ --device $DEVICE \ --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 \ -it quay.io/ascend/cann:8.0.0-910b-ubuntu22.04-py3.10 bash ``` You can also install CANN manually: :::{note} This guide takes aarch64 as an example. If you run on x86, you need to replace `aarch64` with `x86_64` for the package name shown below. ::: ```bash # Create a virtual environment python -m venv vllm-ascend-env source vllm-ascend-env/bin/activate # Install required python packages. pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple attrs numpy<2.0.0 decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions # Download and install the CANN package. wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.0.0/Ascend-cann-toolkit_8.0.0_linux-aarch64.run chmod +x ./Ascend-cann-toolkit_8.0.0_linux-aarch64.run ./Ascend-cann-toolkit_8.0.0_linux-aarch64.run --full source /usr/local/Ascend/ascend-toolkit/set_env.sh wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.0.0/Ascend-cann-kernels-910b_8.0.0_linux-aarch64.run chmod +x ./Ascend-cann-kernels-910b_8.0.0_linux-aarch64.run ./Ascend-cann-kernels-910b_8.0.0_linux-aarch64.run --install wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.0.0/Ascend-cann-nnal_8.0.0_linux-aarch64.run chmod +x. /Ascend-cann-nnal_8.0.0_linux-aarch64.run ./Ascend-cann-nnal_8.0.0_linux-aarch64.run --install source /usr/local/Ascend/nnal/atb/set_env.sh ``` :::: ::::{tab-item} Before using docker :sync: docker No more extra step if you are using `vllm-ascend` prebuilt docker image. :::: ::::: Once it's done, you can start to set up `vllm` and `vllm-ascend`. ## Setup vllm and vllm-ascend :::::{tab-set} :sync-group: install ::::{tab-item} Using pip :selected: :sync: pip You can install `vllm` and `vllm-ascend` from **pre-built wheel** (**Unreleased yet**, please build from source code): ```{code-block} bash :substitutions: # Install vllm-project/vllm from pypi pip install vllm==|pip_vllm_version| # Install vllm-project/vllm-ascend from pypi. pip install vllm-ascend==|pip_vllm_ascend_version| --extra-index https://download.pytorch.org/whl/cpu/ ``` or build from **source code**: ```{code-block} bash :substitutions: # Install vLLM git clone --depth 1 --branch |vllm_version| https://github.com/vllm-project/vllm cd vllm VLLM_TARGET_DEVICE=empty pip install . --extra-index https://download.pytorch.org/whl/cpu/ # Install vLLM Ascend git clone --depth 1 --branch |vllm_ascend_version| https://github.com/vllm-project/vllm-ascend.git cd vllm-ascend pip install -e . --extra-index https://download.pytorch.org/whl/cpu/ ``` Current version depends on a unreleased `torch-npu`, you need to install manually: ``` # Once the packages are installed, you need to install `torch-npu` manually, # because that vllm-ascend relies on an unreleased version of torch-npu. # This step will be removed in the next vllm-ascend release. # # Here we take python 3.10 on aarch64 as an example. Feel free to install the correct version for your environment. See: # # https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/pta/Daily/v2.5.1/20250226.4/pytorch_v2.5.1_py39.tar.gz # https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/pta/Daily/v2.5.1/20250226.4/pytorch_v2.5.1_py310.tar.gz # https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/pta/Daily/v2.5.1/20250226.4/pytorch_v2.5.1_py311.tar.gz # mkdir pta cd pta wget https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/pta/Daily/v2.5.1/20250226.4/pytorch_v2.5.1_py310.tar.gz tar -xvf pytorch_v2.5.1_py310.tar.gz pip install ./torch_npu-2.5.1.dev20250226-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl ``` :::: ::::{tab-item} Using docker :sync: docker You can just pull the **prebuilt image** and run it with bash. ```{code-block} bash :substitutions: # Update DEVICE according to your device (/dev/davinci[0-7]) DEVICE=/dev/davinci7 # Update the vllm-ascend image IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version| docker pull $IMAGE docker run --rm \ --name vllm-ascend-env \ --device $DEVICE \ --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 \ -it $IMAGE bash ``` or build IMAGE from **source code**: ```bash git clone https://github.com/vllm-project/vllm-ascend.git cd vllm-ascend docker build -t vllm-ascend-dev-image:latest -f ./Dockerfile . ``` :::: ::::: ## Extra information ### Verify installation Create and run a simple inference test. The `example.py` can be like: ```python from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] # Create a sampling params object. sampling_params = SamplingParams(temperature=0.8, top_p=0.95) # Create an LLM. llm = LLM(model="Qwen/Qwen2.5-0.5B-Instruct") # Generate texts from the prompts. 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}") ``` Then run: ```bash # export VLLM_USE_MODELSCOPE=true to speed up download if huggingface is not reachable. python example.py ``` The output will be like: ```bash INFO 02-18 08:49:58 __init__.py:28] Available plugins for group vllm.platform_plugins: INFO 02-18 08:49:58 __init__.py:30] name=ascend, value=vllm_ascend:register INFO 02-18 08:49:58 __init__.py:32] all available plugins for group vllm.platform_plugins will be loaded. INFO 02-18 08:49:58 __init__.py:34] set environment variable VLLM_PLUGINS to control which plugins to load. INFO 02-18 08:49:58 __init__.py:42] plugin ascend loaded. INFO 02-18 08:49:58 __init__.py:174] Platform plugin ascend is activated INFO 02-18 08:50:12 config.py:526] This model supports multiple tasks: {'embed', 'classify', 'generate', 'score', 'reward'}. Defaulting to 'generate'. INFO 02-18 08:50:12 llm_engine.py:232] Initializing a V0 LLM engine (v0.7.1) with config: model='./Qwen2.5-0.5B-Instruct', speculative_config=None, tokenizer='./Qwen2.5-0.5B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=npu, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=./Qwen2.5-0.5B-Instruct, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=False, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=False, Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00