ROLL x Ascend

Last updated: 06/23/2026.

We have added support for Huawei Ascend devices in ROLL.

Hardware Compatibility and Supported Operating Systems

ROLL's Ascend support is currently validated on training-series Ascend hardware:

Product	Support status	Notes
Atlas 900 A2 PODc (Ascend 910B1) / Atlas A2 training series	√	Use docker/Dockerfile.A2 or the roll:ascend-a2 image.
Atlas 900 A3 PODc (Ascend 910_9391) / Atlas A3 training series	√	Use docker/Dockerfile.A3 or the roll:ascend-a3 image.
Ascend 950 training series	√	Use the Ascend 950 installation profile: torch 2.10, vLLM v0.20.2, vLLM-Ascend main, and COMPILE_CUSTOM_KERNELS=1 when building vLLM-Ascend.
Atlas A2/A3 inference series and Atlas 200I/500 A2 inference products	x	Current ROLL NPU images and examples target training-series devices.
Other Ascend training or inference products	Not validated	Validate the driver, firmware, CANN, torch_npu, and vLLM-Ascend versions before use.

In this table, √ means supported by the current ROLL Ascend Dockerfiles/examples or the manual Ascend 950 installation profile, and x means not supported in the current ROLL NPU setup.

Supported operating systems:

Deployment scenario	Supported OS	Notes
Physical host	Ubuntu 22.04	Recommended and validated by the current ROLL Ascend guides.
ROLL Ascend container	Ubuntu 22.04	The A2/A3 Dockerfiles are based on quay.io/ascend/cann:9.0.0-*-ubuntu22.04-py3.11.
Ascend 950 manual installation	Ubuntu 22.04	Use the Ascend 950-specific torch/vLLM stack below. Keep the driver, firmware, CANN, and torch_npu versions aligned with the target Ascend 950 environment.
VM/container deployments on other host OS versions	Follow Ascend/CANN compatibility guidance	Check the Ascend compatibility query assistant and the CANN Software Installation OS compatibility notes for the target hardware.

Installation

Basic Environment Setup

Software	Version
Python	3.11
CANN	9.0.0

For Ascend 950 , keep Python 3.11 and use the Ascend 950-specific torch/vLLM stack described in [Ascend 950 Installation Profile](#Ascend 950-installation-profile).

Create Conda Environment

Use the following commands to create a new conda environment in Miniconda:

conda create --name roll python=3.11
conda activate roll

Install torch & torch_npu

To use torch and torch_npu in ROLL, install them using the commands below:

# Use CPU-only torch when installing outside the pre-built image
pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 --index-url https://download.pytorch.org/whl/cpu

# Install the torch_npu version matching torch/CANN
pip install torch_npu==2.9.0

Install vllm & vllm-ascend

To use vllm in ROLL, compile and install vllm and vllm-ascend as follows:

# vllm
git clone -b v0.18.0 --depth 1 https://github.com/vllm-project/vllm.git
cd vllm
pip install -r requirements/build.txt

VLLM_TARGET_DEVICE=empty pip install -v -e .
cd ..

# vllm-ascend
git clone -b v0.18.0 --depth 1 https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend

pip install -e .
cd ..

Or you could install vllm and vllm-ascend from pre-built wheel:

# Install vllm-project/vllm. The newest supported version is v0.18.0.
pip install vllm==0.18.0

# Install vllm-project/vllm-ascend from pypi.
pip install vllm-ascend==0.18

Ascend 950 Installation Profile

For Ascend 950, use torch 2.10, vLLM v0.20.2, and vLLM-Ascend from the main branch. Set COMPILE_CUSTOM_KERNELS=1 before installing vLLM-Ascend so its custom kernels are built:

# Install torch 2.10
pip install torch==2.10.0 torchvision==0.25.0 torchaudio==2.10.0 --index-url https://download.pytorch.org/whl/cpu

# Install the torch_npu package matching torch 2.10 and your CANN release
pip install torch_npu==2.10.0

# vLLM v0.20.2
git clone -b v0.20.2 --depth 1 https://github.com/vllm-project/vllm.git
cd vllm
pip install -r requirements/build.txt
VLLM_TARGET_DEVICE=empty pip install -v -e .
cd ..

# vLLM-Ascend main
git clone -b main --depth 1 https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
export COMPILE_CUSTOM_KERNELS=1
pip install -v -e .
cd ..

Install ROLL

git clone https://github.com/alibaba/ROLL.git
cd ROLL
pip install -r requirements_common.txt
pip install -e .
cd ..

Additional Third-Party Libraries

Software	Description
transformers	>= v4.57.6
flash_attn	not supported
transformer-engine[pytorch]	not supported

1. transformers v4.57.6 supports enabling --flash_attention_2.
2. flash_attn acceleration is not supported currently.
3. transformer-engine[pytorch] is currently not supported.

pip install transformers==4.57.6

Quick Start: Single-Node Deployment

Before full usage, we recommend testing the single-node pipeline to verify your environment and installation. Since Megatron-LM is not supported on NPU, first change strategy_args in the relevant files to use the fsdp2 option.

1. Run the single-node pipeline via shell:

bash examples/agentic_demo/run_agentic_pipeline_frozen_lake_single_node_demo.sh  

2. Run the agentic pipeline using a config file:

# Make sure you are in the root directory of the ROLL project

python examples/start_agentic_pipeline.py \
        --config_path qwen2.5-0.5B-agentic \
        --config_name agentic_val_sokoban

• --config_path – Directory containing your YAML configuration files.
• --config_name – Filename (without the .yaml extension).

Current Support Status

Feature	Example	Training Backend	Inference Backend	Hardware
Agentic	examples/qwen2.5-0.5B-agentic/run_agentic_pipeline_sokoban.sh	FSDP2	vLLM	Atlas 900 A2/A3 PODc
Agentic-Rollout	examples/qwen2.5-0.5B-agentic/run_agentic_rollout_sokoban.sh	FSDP2	vLLM	Atlas 900 A2/A3 PODc
RLVR	examples/ascend_examples/run_rlvr_pipeline.sh	FSDP2	vLLM	Atlas 900 A2/A3/Ascend 950 training series

Disclaimer

The Ascend support provided in ROLL is intended as a reference example. For production use, please consult official channels.