Installation

Installation#

This document describes how to install vllm-ascend manually.

Requirements#

OS: Linux
Python: >= 3.9, < 3.12
A hardware with Ascend NPU. It’s usually the Atlas 800 A2 series.

Software:

Software	Supported version	Note
CANN	>= 8.2.RC1	Required for vllm-ascend and torch-npu
torch-npu	>= 2.5.1.post1	Required for vllm-ascend, No need to install manually, it will be auto installed in below steps
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#

Before using pip

The easiest way to prepare your software environment is using CANN image directly:

# 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:8.2.rc1-910b-ubuntu22.04-py3.11
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

Before using 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#

Using pip

First install system dependencies and config pip mirror:

# Using apt-get with mirror
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
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:

# 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"

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

# Install vllm-project/vllm from pypi
pip install vllm==0.9.0

# Install vllm-project/vllm-ascend from pypi.
pip install vllm-ascend==0.9.0rc2

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 encourage 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.

Using docker

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

# 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:v0.9.0rc2
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

The default workdir is /workspace, vLLM and vLLM Ascend code are placed in /vllm-workspace and installed in development mode(pip install -e) to help developer immediately take place changes without requiring a new installation.

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:

# 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:

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'