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init v0.23.0

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Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>
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Sun Ruoxi
2026-08-27 15:11:51 +08:00
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@@ -4,25 +4,50 @@ This document describes how to install vllm-ascend manually.
## Requirements
:::::{tab-set}
::::{tab-item} Atlas A2/A3/950DT inference products
- OS: Linux
- Python: >= 3.9, < 3.12
- A hardware with Ascend NPU. It's usually the Atlas 800 A2 series.
- Python: >= 3.10, < 3.13
- Hardware with Ascend NPUs. It's usually the Atlas 800 A2 series.
- Atlas 300I DUO.
- Software:
| Software | Supported version | Note |
|---------------|----------------------------------|-------------------------------------------|
| Ascend HDK | Refer to [here](https://www.hiascend.com/document/detail/zh/canncommercial/82RC1/releasenote/releasenote_0000.html) | Required for CANN |
| CANN | >= 8.2.RC1 | Required for vllm-ascend and torch-npu |
| torch-npu | >= 2.7.1.dev20250724 | Required for vllm-ascend, No need to install manually, it will be auto installed in below steps |
| torch | >= 2.7.1 | Required for torch-npu and vllm |
| Ascend HDK | Refer to the [CANN 9.1.0 Release Notes](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/910/softwareinst/releasenote/9.1.0/release-notes.md) | Required for CANN |
| CANN | == 9.1.0 | Required for vllm-ascend and TorchNPU |
| TorchNPU | == 2.10.0.post4 | Required for vllm-ascend, No need to install manually, it will be auto installed in below steps |
| torch | == 2.10.0 | Required for TorchNPU and vllm, No need to install manually, it will be auto installed in below steps |
| NNAL | == 9.1.0 | Required for libatb.so, enables advanced tensor operations |
You have 2 way to install:
- **Using pip**: first prepare env manually or via CANN image, then install `vllm-ascend` using pip.
```{note}
Atlas 300I DUO uses its platform-specific CANN 9.1.0 package; refer to the 310P table below for its requirements.
```
::::
::::{tab-item} Atlas 300I DUO
| Software | Supported version | Note |
|---------------|----------------------------------|-------------------------------------------|
| Ascend HDK | Refer to the [CANN 9.1.0 Release Notes](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/910/softwareinst/releasenote/9.1.0/release-notes.md) | Required for CANN |
| CANN | == 9.1.0 | Required for vllm-ascend and TorchNPU |
| TorchNPU | == 2.10.0.post4 | Required for vllm-ascend, No need to install manually, it will be auto installed in below steps |
| torch | == 2.10.0 | Required for TorchNPU and vllm, No need to install manually, it will be auto installed in below steps |
| NNAL | == 9.1.0 | Required for libatb.so, enables advanced tensor operations |
| triton / triton-ascend | Not supported | Uninstalled in `Dockerfile.310p` |
::::
:::::
There are two installation methods:
- **Using pip**: first prepare the environment manually or via a CANN image, then install `vllm-ascend` using pip.
- **Using docker**: use the `vllm-ascend` pre-built docker image directly.
## Configure a new environment
## Configure Ascend CANN 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.
Before installation, you need to make sure firmware/driver, and CANN are installed correctly, refer to [CANN Installation](https://www.hiascend.com/cann/download?versionId=735&ids=d806%2Ch0501%2Ch0601%2Ch0702) for more details.
### Configure hardware environment
@@ -32,7 +57,7 @@ To verify that the Ascend NPU firmware and driver were correctly installed, run:
npu-smi info
```
Refer to [Ascend Environment Setup Guide](https://ascend.github.io/docs/sources/ascend/quick_install.html) for more details.
Refer to [CANN Installation](https://www.hiascend.com/cann/download?versionId=735&ids=d806%2Ch0501%2Ch0601%2Ch0702) for more details.
### Configure software environment
@@ -45,6 +70,10 @@ Refer to [Ascend Environment Setup Guide](https://ascend.github.io/docs/sources/
The easiest way to prepare your software environment is using CANN image directly:
```{note}
The CANN prebuilt image includes NNAL (Ascend Neural Network Acceleration Library), which provides libatb.so for advanced tensor operations. No additional installation is required when using the prebuilt image.
```
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
@@ -53,6 +82,7 @@ export DEVICE=/dev/davinci7
export IMAGE=quay.io/ascend/cann:|cann_image_tag|
docker run --rm \
--name vllm-ascend-env \
--shm-size=1g \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
@@ -70,30 +100,31 @@ docker run --rm \
:animate: fade-in-slide-down
You can also install CANN manually:
```{warning}
If you encounter "libatb.so not found" errors during runtime, please ensure NNAL is properly installed as shown in the manual installation steps below.
```
```bash
# Create a virtual environment
# Create a virtual environment.
python -m venv vllm-ascend-env
source vllm-ascend-env/bin/activate
# Install required python packages.
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple attrs 'numpy<2.0.0' decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions
# Install required Python packages.
python -m pip install --upgrade pip
pip3 install attrs numpy decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions
# Download and install the CANN package.
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.2.RC1/Ascend-cann-toolkit_8.2.RC1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-toolkit_8.2.RC1_linux-"$(uname -i)".run
./Ascend-cann-toolkit_8.2.RC1_linux-"$(uname -i)".run --full
# https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C22B800TP052/Ascend-cann-kernels-910b_8.2.rc1_linux-aarch64.run
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%209.1.0/Ascend-cann-toolkit_9.1.0_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-toolkit_9.1.0_linux-"$(uname -i)".run
./Ascend-cann-toolkit_9.1.0_linux-"$(uname -i)".run --full
source /usr/local/Ascend/ascend-toolkit/set_env.sh
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.2.RC1/Ascend-cann-kernels-910b_8.2.RC1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-kernels-910b_8.2.RC1_linux-"$(uname -i)".run
./Ascend-cann-kernels-910b_8.2.RC1_linux-"$(uname -i)".run --install
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%209.1.0/Ascend-cann-910b-ops_9.1.0_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-910b-ops_9.1.0_linux-"$(uname -i)".run
./Ascend-cann-910b-ops_9.1.0_linux-"$(uname -i)".run --install
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.2.RC1/Ascend-cann-nnal_8.2.RC1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-nnal_8.2.RC1_linux-"$(uname -i)".run
./Ascend-cann-nnal_8.2.RC1_linux-"$(uname -i)".run --install
source /usr/local/Ascend/nnal/atb/set_env.sh
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%209.1.0/Ascend-cann-nnal_9.1.0_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-nnal_9.1.0_linux-"$(uname -i)".run
./Ascend-cann-nnal_9.1.0_linux-"$(uname -i)".run --install
```
:::
@@ -102,22 +133,15 @@ 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.
No extra steps are needed if you are using the `vllm-ascend` prebuilt Docker image.
::::
:::::
Once it's done, you can start to set up `vllm` and `vllm-ascend`.
Once this is done, you can start to set up `vllm` and `vllm-ascend`.
## Setup vllm and vllm-ascend
## Set up using Python
:::::{tab-set}
:sync-group: install
::::{tab-item} Using pip
:selected:
:sync: pip
First install system dependencies and config pip mirror:
First, install system dependencies and configure the pip mirror:
```bash
# Using apt-get with mirror
@@ -125,63 +149,150 @@ sed -i 's|ports.ubuntu.com|mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list
apt-get update -y && apt-get install -y gcc g++ cmake libnuma-dev wget git curl jq
# Or using yum
# yum update -y && yum install -y gcc g++ cmake numactl-devel wget git curl jq
# Config pip mirror
# Config pip mirror,only versions 0.11.0 and earlier are supported, if using a version later than 0.11.0, do not execute this command
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
```
**[Optional]** Then config the extra-index of `pip` if you are working on a x86 machine or using torch-npu dev version:
**[Optional]** Then configure the extra-index of `pip` if you are working on an x86 machine or using TorchNPU dev version:
```bash
# For torch-npu dev version or x86 machine
pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/ https://mirrors.huaweicloud.com/ascend/repos/pypi"
# For TorchNPU dev version or x86 machine
pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"
```
Then you can install `vllm` and `vllm-ascend` from **pre-built wheel**:
Then you can install `vllm` and `vllm-ascend` from a **pre-built wheel** using one of the following methods:
:::::{tab-set}
:sync-group: install-method
::::{tab-item} Original installation
:sync: original
```{code-block} bash
:substitutions:
# Install vllm-project/vllm from pypi
# Install vllm-project/vllm. The newest supported version is |vllm_version|.
pip install vllm==|pip_vllm_version|
# Install vllm-project/vllm-ascend from pypi.
pip install vllm-ascend==|pip_vllm_ascend_version|
```
# Install vllm-project/vllm-ascend.
pip install \
--extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi/variant \
--extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi \
vllm-ascend==|pip_vllm_ascend_version|
:::{dropdown} Click here to see "Build from source code"
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 -v -e .
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 -v -e .
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.
:::
```{note}
If you are building from v0.7.3-dev and intend to use sleep mode feature, you should set `COMPILE_CUSTOM_KERNELS=1` manually.
To build custom ops, gcc/g++ higher than 8 and c++ 17 or higher is required. If you're using `pip install -e .` and encounter a torch-npu version conflict, please install with `pip install --no-build-isolation -e .` to build on system env.
If you encounter other problems during compiling, it is probably because unexpected compiler is being used, you may export `CXX_COMPILER` and `C_COMPILER` in env to specify your g++ and gcc locations before compiling.
```
::::
::::{tab-item} Using docker
:sync: docker
::::{tab-item} uv-wheelnext installation
:sync: uv-wheelnext
You can just pull the **prebuilt image** and run it with bash.
The `uv-wheelnext` installation downloads only the delta on top of vllm, resulting in a smaller download size. First install `uv-wheelnext` to support incremental wheels:
```bash
# install uv-wheelnext
curl -LsSf https://astral.sh/uv/install.sh | sed 's/verify_checksum "$_file"/true/' | INSTALLER_DOWNLOAD_URL=https://wheelnext.astral.sh sh
source $HOME/.local/bin/env
```
```{code-block} bash
:substitutions:
# Install vllm-project/vllm. The newest supported version is |vllm_version|.
pip install vllm==|pip_vllm_version|
# Install vllm-project/vllm-ascend from wheelnext index.
uv pip install --system \
--extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi/variant \
--extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi \
--index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple \
vllm-ascend==|pip_vllm_ascend_version|
```
```{note}
If you encounter errors during `uv pip install` (e.g., corrupted cache or stale package data), try clearing the uv cache first and then re-run the install command:
uv cache clean
```
::::
:::::
:::{dropdown} Click here to see "Build from source code"
or build from **source code**:
```{note}
To install `triton-ascend`, run:
pip install triton-ascend==3.2.2 --extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi
If you are installing via `uv`, make sure to install `triton-ascend` **last**, after all other packages have been installed, to avoid dependency resolution conflicts.
```
```{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 -e .
cd ..
# Install vLLM Ascend.
git clone --depth 1 --branch |vllm_ascend_version| https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
git submodule update --init --recursive
pip install -e .
cd ..
```
If you are building custom operators for Atlas A3, you should run `git submodule update --init --recursive` manually, or ensure your environment has internet access.
:::
:::{note}
Atlas 300I DUO does not support `triton` or `triton-ascend`. Source installations can pull these packages as dependencies; remove them before running on Atlas 300I DUO:
```bash
pip uninstall -y triton-ascend triton
```
:::
```{note}
To build custom operators, gcc/g++ higher than 8 and C++17 or higher are required. If you are using `pip install -e .` and encounter a TorchNPU version conflict, please install with `pip install --no-build-isolation -e .` to build on system env.
If you encounter other problems during compiling, it is probably because an unexpected compiler is being used, you may export `CXX_COMPILER` and `C_COMPILER` in the environment to specify your g++ and gcc locations before compiling.
If you are building in a CPU-only environment where `npu-smi` is unavailable, you need to set `SOC_VERSION` before `pip install -e .` so the build can target the correct chip. You can refer to `Dockerfile*` defaults, for example:
- Atlas A2: `export SOC_VERSION=ascend910b1`
- Atlas A3: `export SOC_VERSION=ascend910_9391`
- Atlas 300I DUO: `export SOC_VERSION=ascend310p1`
- Atlas 950DT: `export SOC_VERSION=ascend950dt_9582`
```
```{note}
To enable the batch invariance feature, set `VLLM_BATCH_INVARIANT=1` before building vllm-ascend to install the batch invariance custom operator library during the installation process.
For usage guidance on the batch invariance feature, see <https://github.com/vllm-project/vllm-ascend/blob/main/docs/source/user_guide/feature_guide/batch_invariance.md>
```
## Set up using Docker
`vllm-ascend` offers Docker images for deployment. You can just pull the **prebuilt image** from the image repository [ascend/vllm-ascend](https://quay.io/repository/ascend/vllm-ascend?tab=tags) and run it with bash.
Supported images as following.
| image name | Hardware | OS |
| - | - | - |
| vllm-ascend:{{ vllm_ascend_version }} | Atlas A2 | Ubuntu |
| vllm-ascend:{{ vllm_ascend_version }}-openeuler | Atlas A2 | openEuler |
| vllm-ascend:{{ vllm_ascend_version }}-a3 | Atlas A3 | Ubuntu |
| vllm-ascend:{{ vllm_ascend_version }}-a3-openeuler | Atlas A3 | openEuler |
| vllm-ascend:{{ vllm_ascend_version }}-310p | Atlas 300I DUO | Ubuntu |
| vllm-ascend:{{ vllm_ascend_version }}-310p-openeuler | Atlas 300I DUO | openEuler |
| vllm-ascend:{{ vllm_ascend_version }}-a5 | Atlas 950DT | Ubuntu |
| vllm-ascend:{{ vllm_ascend_version }}-a5-openeuler | Atlas 950DT | openEuler |
:::{dropdown} Click here to see "Build from Dockerfile"
or build IMAGE from **source code**:
@@ -194,15 +305,56 @@ docker build -t vllm-ascend-dev-image:latest -f ./Dockerfile .
:::
:::::{tab-set}
::::{tab-item} A2/A3
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15] Atlas 950DT: /dev/davinci[0-7]).
# Update the vllm-ascend image according to your environment.
# Note you should download the weight to /root/.cache in advance.
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend-env \
--shm-size=1g \
--net=host \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-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
```
::::
::::{tab-item} Atlas 300I DUO
Adjust `/dev/davinci0` to the NPU you want to use.
```{code-block} bash
:substitutions:
export DEVICE=/dev/davinci0
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-310p
docker run --rm \
--name vllm-ascend \
--shm-size=1g \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
@@ -213,14 +365,58 @@ docker run --rm \
-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
```
The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed in `/vllm-workspace` and installed in [development mode](https://setuptools.pypa.io/en/latest/userguide/development_mode.html)(`pip install -e`) to help developer immediately take place changes without requiring a new installation.
::::
::::{tab-item} Atlas 200I Pro
Atlas 200I Pro requires additional device nodes, driver libraries, and configuration files so that `npu-smi` and other driver commands work inside the container. Adjust `/dev/davinci0` to the NPU you want to use.
```{code-block} bash
:substitutions:
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-310p
docker run --rm \
--privileged \
--name vllm-ascend \
--shm-size=10g \
--device=/dev/davinci0:/dev/davinci0 \
--device=/dev/davinci_manager \
--device=/dev/ascend_manager \
--device=/dev/user_config \
-v /etc/sys_version.conf:/etc/sys_version.conf \
-v /etc/ld.so.conf.d/mind_so.conf:/etc/ld.so.conf.d/mind_so.conf \
-v /etc/hdcBasic.cfg:/etc/hdcBasic.cfg \
-v /var/dmp_daemon:/var/dmp_daemon \
-v /usr/lib64/libmmpa.so:/usr/lib64/libmmpa.so \
-v /usr/lib64/libcrypto.so.1.1:/usr/lib64/libcrypto.so.1.1 \
-v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \
-v /usr/lib64/libstackcore.so:/usr/lib64/libstackcore.so \
-v /usr/lib/aarch64-linux-gnu/libyaml-0.so.2:/usr/lib64/libyaml-0.so.2 \
-v /etc/slog.conf:/etc/slog.conf \
-v /var/slogd:/var/slogd \
-v /usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64 \
-v /usr/lib64/libtensorflow.so:/usr/lib64/libtensorflow.so \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
For openEuler, keep the same command structure and make the following substitutions:
- Set `IMAGE` to `quay.io/ascend/vllm-ascend:|vllm_ascend_version|-310p-openeuler`.
- Add `-v /usr/lib64/libsemanage.so.2:/usr/lib64/libsemanage.so.2`.
- Replace the `libyaml` mount with `-v /usr/lib64/libyaml-0.so.2.0.9:/usr/lib64/libyaml-0.so.2`.
::::
:::::
The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed in `/vllm-workspace` and installed in [development mode](https://setuptools.pypa.io/en/latest/userguide/development_mode.html) (`pip install -e`) to help developers immediately make changes without requiring a new installation.
## Extra information
### Verify installation
@@ -240,7 +436,7 @@ prompts = [
# 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")
llm = LLM(model="Qwen/Qwen3-0.6B")
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
@@ -253,31 +449,317 @@ for output in outputs:
Then run:
```bash
# Try `export VLLM_USE_MODELSCOPE=true` and `pip install modelscope`
# to speed up download if huggingface is not reachable.
python example.py
```
The output will be like:
If you encounter a connection error with Hugging Face (e.g., `We couldn't connect to 'https://huggingface.co' to load the files, and couldn't find them in the cached files.`), run the following commands to use ModelScope as an alternative:
```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<?, ?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'
export VLLM_USE_MODELSCOPE=True
pip install modelscope
python example.py
```
```{note}
If you encounter custom-op security verification errors while running inference on Atlas 950DT, refer to [Pooling enables UB and UBoE for 950DT and 950PR](https://gitcode.com/Ascend/memcache/wiki/%E6%B1%A0%E5%8C%96%E4%BD%BF%E8%83%BD950DT%E5%92%8C950PR%E7%9A%84UB%E5%92%8CUBoE.md) and run the following commands:
> Each NPU will prompt for confirmation when running the first command. You must manually enter `Y` for all of them.
```bash
for i in {0..7}; do npu-smi set -t custom-op-secverify-enable -i $i -d 1; done;
for i in {0..7}; do npu-smi set -t custom-op-secverify-mode -i $i -d 0; done;
```
This section shows ascend platform is successfully detected in vllm:
```bash
INFO 05-27 11:40:38 [__init__.py:44] Available plugins for group vllm.platform_plugins:
INFO 05-27 11:40:38 [__init__.py:46] - ascend -> vllm_ascend:register
INFO 05-27 11:40:38 [__init__.py:49] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 05-27 11:40:38 [__init__.py:238] Platform plugin ascend is activated
```
This section shows the final output:
```bash
Prompt: 'Hello, my name is', Generated text: ' Lucy and I am an 8 year old who loves to draw and write stories'
Prompt: 'The president of the United States is', Generated text: " a key leader in the federal government, and the president's role in the executive"
Prompt: 'The capital of France is', Generated text: ' a city. What is the capital of France? The capital of France is Paris'
Prompt: 'The future of AI is', Generated text: ' a topic that is being discussed in various contexts. In the business world, AI'
```
This section shows process exits after offline inference, and does not affect actual inference:
```bash
(EngineCore pid=970) INFO 05-12 11:36:00 [core.py:1201] Shutdown initiated (timeout=0)
(EngineCore pid=970) INFO 05-12 11:36:00 [core.py:1224] Shutdown complete
ERROR 05-12 11:36:01 [core_client.py:704] Engine core proc EngineCore died unexpectedly, shutting down client.
sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute
```
## Multi-node Deployment
### Verify Multi-Node Communication
First, check physical layer connectivity, then verify each node, and finally verify the inter-node connectivity.
#### Physical Layer Requirements
- The physical machines must be located on the same LAN, with network connectivity.
- All NPUs are connected with optical modules, and the connection status must be normal.
#### Each Node Verification
Execute the following commands on each node in sequence. The results must all be `success` and the status must be `UP`:
:::::{tab-set}
:sync-group: multi-node
::::{tab-item} A2 series
:sync: A2
```bash
# Check the remote switch ports
for i in {0..7}; do hccn_tool -i $i -lldp -g | grep Ifname; done
# Get the link status of the Ethernet ports (UP or DOWN)
for i in {0..7}; do hccn_tool -i $i -link -g ; done
# Check the network health status
for i in {0..7}; do hccn_tool -i $i -net_health -g ; done
# View the network detected IP configuration
for i in {0..7}; do hccn_tool -i $i -netdetect -g ; done
# View gateway configuration
for i in {0..7}; do hccn_tool -i $i -gateway -g ; done
# View NPU network configuration
cat /etc/hccn.conf
```
::::
::::{tab-item} A3 series
:sync: A3
```bash
# Check the remote switch ports
for i in {0..15}; do hccn_tool -i $i -lldp -g | grep Ifname; done
# Get the link status of the Ethernet ports (UP or DOWN)
for i in {0..15}; do hccn_tool -i $i -link -g ; done
# Check the network health status
for i in {0..15}; do hccn_tool -i $i -net_health -g ; done
# View the network detected IP configuration
for i in {0..15}; do hccn_tool -i $i -netdetect -g ; done
# View gateway configuration
for i in {0..15}; do hccn_tool -i $i -gateway -g ; done
# View NPU network configuration
cat /etc/hccn.conf
```
::::
::::{tab-item} 950DT series
:sync: 950DT
```bash
# Check the remote switch ports
for i in {0..7}; do hccn_tool -i $i -lldp -g | grep Ifname; done
# Get the link status of the Ethernet ports (UP or DOWN)
for i in {0..7}; do hccn_tool -i $i -link -g ; done
# Check the network health status
for i in {0..7}; do hccn_tool -i $i -net_health -g ; done
# View the network detected IP configuration
for i in {0..7}; do hccn_tool -i $i -netdetect -g ; done
# View gateway configuration
for i in {0..7}; do hccn_tool -i $i -gateway -g ; done
# View NPU network configuration
cat /etc/hccn.conf
```
::::
:::::
#### Interconnect Verification
##### 1. Get NPU IP Addresses
:::::{tab-set}
:sync-group: multi-node
::::{tab-item} A2 series
:sync: A2
```bash
for i in {0..7}; do hccn_tool -i $i -ip -g | grep ipaddr; done
```
::::
::::{tab-item} A3 series
:sync: A3
```bash
for i in {0..15}; do hccn_tool -i $i -ip -g | grep ipaddr; done
```
::::
::::{tab-item} 950DT series
:sync: 950DT
```bash
for i in {0..7}; do hccn_tool -i $i -ip -g | grep ipaddr; done
```
::::
:::::
##### 2. Cross-Node PING Test
```bash
# Execute on the target node (replace with actual IP)
hccn_tool -i 0 -ping -g address x.x.x.x
```
### Atlas 950 Series Server Pre-check
This pre-check applies only to Atlas 950 series servers. Other server series can skip it.
- **Prepare HiXLEP configuration paths**
When deploying an inference service on Atlas 950 series servers, verify on each server that `/lib/route.conf`, `/etc/hccl_rootinfo.json`, and the `/etc/hixlep` directory (which describes the UB link topology) exist and are configured correctly. If any of them are missing or incorrect, follow the [HiXLEP configuration file generation guide](https://gitcode.com/cann/hixl/wiki/A5%20LocalCommRes%E9%85%8D%E7%BD%AE%E6%8C%87%E5%8D%97.md) to generate the required content. When generating `/etc/hixlep`, use the "D2D scenario".
### Run Container In Each Node
Using vLLM-ascend official container is more efficient to run multi-node environment.
Run the following command to start the container in each node (You should download the weight to /root/.cache in advance):
:::::{tab-set}
:sync-group: multi-node
::::{tab-item} A2 series
:sync: A2
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
# openEuler:
# export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-openeuler
# Ubuntu:
# export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
# Run the container using the defined variables
# Note if you are running bridge network with docker, Please expose available ports
# for multiple nodes communication in advance
docker run --rm \
--name vllm-ascend \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-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
```
::::
::::{tab-item} A3 series
:sync: A3
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
# openEuler:
# export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3-openeuler
# Ubuntu:
# export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
# Run the container using the defined variables
# Note if you are running bridge network with docker, Please expose available ports
# for multiple nodes communication in advance
docker run --rm \
--name vllm-ascend \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-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
```
::::
::::{tab-item} 950DT series
:sync: 950DT
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
# openEuler:
# export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a5-openeuler
# Ubuntu:
# export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a5
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a5
# Run the container using the defined variables
# Note if you are running bridge network with docker, Please expose available ports
# for multiple nodes communication in advance
docker run --rm \
--name vllm-ascend \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-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
```
::::
:::::