xc-llm-ascend/docs/source/installation.md

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.dev20250320	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 for more details.

Configure hardware environment

To verify that the Ascend NPU firmware and driver were correctly installed, run:

npu-smi info

Refer to Ascend Environment Setup Guide 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:

   :substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/cann:|cann_image_tag|
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 \
    -v /root/.cache:/root/.cache \
    -it $IMAGE bash

:::{dropdown} Click here to see "Install CANN manually" :animate: fade-in-slide-down You can also install CANN manually:

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.

# 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

First install system dependencies:

apt update  -y
apt install -y gcc g++ cmake libnuma-dev wget

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/v2.5.1/20250320.3/pytorch_v2.5.1_py39.tar.gz
# https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/pta/Daily/v2.5.1/20250320.3/pytorch_v2.5.1_py310.tar.gz
# https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/pta/Daily/v2.5.1/20250320.3/pytorch_v2.5.1_py311.tar.gz
#
mkdir pta
cd pta
wget https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/pta/Daily/v2.5.1/20250320.3/pytorch_v2.5.1_py310.tar.gz
tar -xvf pytorch_v2.5.1_py310.tar.gz
pip install ./torch_npu-2.5.1.dev20250320-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
cd ..

Then you can install vllm and vllm-ascend from pre-built wheel:

   :substitutions:

# Install vllm-project/vllm from pypi
# There was a vLLM v0.8.4 installation bug, please use "Build from source code"
# https://github.com/vllm-project/vllm-ascend/issues/581
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/

:::{dropdown} Click here to see "Build from source code" or build from source code:

   :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/
cd ..

# 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/
cd ..

:::

vllm-ascend will build custom ops by default. If you don't want to build it, set `COMPILE_CUSTOM_KERNELS=0` environment to disable it.
To build custom ops, gcc/g++ higher than 8 and c++ 17 or higher is required. If you encourage a torch-npu version conflict, please install with `pip install --no-build-isolation -e .` to build on system env.

::::

::::{tab-item} Using docker :sync: docker

You can just pull the prebuilt image and run it with bash.

:::{dropdown} Click here to see "Build from Dockerfile" or build IMAGE from source code:

git clone https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
docker build -t vllm-ascend-dev-image:latest -f ./Dockerfile .

:::

   :substitutions:

# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
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 \
    -v /root/.cache:/root/.cache \
    -it $IMAGE bash

::::

:::::

Extra information

Verify installation

Create and run a simple inference test. The example.py can be like:

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:

# export VLLM_USE_MODELSCOPE=true to speed up download if huggingface is not reachable.
python example.py

The output will be like:

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<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  5.86it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  5.85it/s]
INFO 02-18 08:50:24 executor_base.py:108] # CPU blocks: 35064, # CPU blocks: 2730
INFO 02-18 08:50:24 executor_base.py:113] Maximum concurrency for 32768 tokens per request: 136.97x
INFO 02-18 08:50:25 llm_engine.py:429] init engine (profile, create kv cache, warmup model) took 3.87 seconds
Processed prompts: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00,  8.46it/s, est. speed input: 46.55 toks/s, output: 135.41 toks/s]
Prompt: 'Hello, my name is', Generated text: " Shinji, a teenage boy from New York City. I'm a computer science"
Prompt: 'The president of the United States is', Generated text: ' a very important person. When he or she is elected, many people think that'
Prompt: 'The capital of France is', Generated text: ' Paris. The oldest part of the city is Saint-Germain-des-Pr'
Prompt: 'The future of AI is', Generated text: ' not bright\n\nThere is no doubt that the evolution of AI will have a huge'