Slime NPU Patch Installation Guide

This guide provides instructions for installing Slime with NPU support, including all required dependencies and patches.

Component Version Mapping

Component	Version/Commit	Source
Slime	v0.2.2	GitHub
SGLang	dce8b0606c06d3a191a24c7b8cbe8e238ab316c9	GitHub
SGL Kernel NPU	2026.02.01	GitHub
Megatron-Bridge	35b4ebfc486fb15dcc0273ceea804c3606be948a	GitHub
Megatron-LM	3714d81d418c9f1bca4594fc35f9e8289f652862	GitHub
MindSpeed	fc63de5c48426dd019c3b3f39e65f5bdf56e4086	GitCode
HDK	25.3.RC1	Ascend
CANN	8.5.0	Ascend

Preparing the Running Environment

Python Version

Only python==3.11 is supported currently.

conda create -n slime_release python=3.11
conda activate slime_release

Working Directory Setup

mkdir <WORKSPACE> && cd <WORKSPACE>

CANN Environment

Prior to start work with Slime on Ascend you need to install CANN Toolkit, Kernels operator package and NNAL version 8.5.0, check the installation guide

source <CANN_PATH>/ascend-toolkit/set_env.sh
source <CANN_PATH>/nnal/atb/set_env.sh

PyTorch and PyTorch NPU

pip install torch-npu==2.8.0

Installing Dependencies

SGLang

cd <WORKSPACE>
git clone https://github.com/sgl-project/sglang.git && cd sglang
git checkout dce8b0606c06d3a191a24c7b8cbe8e238ab316c9
mv python/pyproject.toml python/pyproject.toml.backup
mv python/pyproject_other.toml python/pyproject.toml
pip install -e "python[srt_npu]"
pip install torch-npu==2.8.0

SGL Kernel NPU and Torch Memory Saver

Download sgl-kernel-npu-2026.02.01-torch2.8.0-py311-cann8.5.0-a3-aarch64.zip from the release link, then install:

pip install sgl_kernel_npu-2026.2.1-cp311-cp311-linux_aarch64.whl
pip install torch_memory_saver-0.0.8-cp311-cp311-linux_aarch64.whl

Megatron-Bridge

pip install git+https://github.com/ISEEKYAN/mbridge.git@89eb10887887bc74853f89a4de258c0702932a1c --no-deps

cd <WORKSPACE>
git clone https://github.com/fzyzcjy/Megatron-Bridge.git -b dev_rl
pip install nvidia-modelopt[torch]>=0.37.0 --no-build-isolation

Megatron-LM

cd <WORKSPACE>
git clone https://github.com/NVIDIA/Megatron-LM.git --recursive && \
  cd Megatron-LM/ && git checkout 3714d81d418c9f1bca4594fc35f9e8289f652862 && \
  pip install -e .

MindSpeed

cd <WORKSPACE>
git clone https://gitcode.com/Ascend/MindSpeed.git && \
  cd MindSpeed/ && git checkout fc63de5c48426dd019c3b3f39e65f5bdf56e4086 && \
  pip install -e .

Slime

cd <WORKSPACE>
git clone https://github.com/ascend-slime/slime.git && cd slime
cp -r docker/npu_patch ../npu_patch
git checkout v0.2.2
pip install -e .

Applying Patches

cd <WORKSPACE>/slime
git apply ../npu_patch/slime.patch

cd <WORKSPACE>/sglang
git apply ../slime/docker/patch/v0.5.7/sglang.patch
git apply ../npu_patch/sglang.patch

cd <WORKSPACE>/Megatron-LM
git apply ../slime/docker/patch/v0.5.7/megatron.patch
git apply ../npu_patch/megatron.patch

cd <WORKSPACE>/Megatron-Bridge
git apply ../npu_patch/megatron-bridge.patch

cd <WORKSPACE>/MindSpeed
git apply ../npu_patch/mindspeed.patch

Additional Dependencies

cd <WORKSPACE>/slime
pip install triton-ascend
pip install torch-npu==2.8.0
pip install torchvision==0.23.0
pip install numpy==1.26.0

Running the Training

Configuration

Modify the paths in the following files according to your environment (note to use your CANN version):

Common (both GRPO and PPO):

slime/utils/external_utils/command_utils.py

GRPO:

examples/geo3k_vlm_multi_turn/run_grpo_npu.sh
examples/geo3k_vlm_multi_turn/run_geo3k_vlm_multi_turn_grpo_npu.py

PPO:

examples/geo3k_vlm_multi_turn/run_ppo_npu.sh
examples/geo3k_vlm_multi_turn/run_geo3k_vlm_multi_turn_ppo_npu.py

Dataset

Download the dataset from HuggingFace following the instructions in the script directory.

Execute Training

cd <WORKSPACE>/slime
# GRPO
bash examples/geo3k_vlm_multi_turn/run_grpo_npu.sh
# PPO
bash examples/geo3k_vlm_multi_turn/run_ppo_npu.sh

To save logs and display them simultaneously:

# GRPO
bash examples/geo3k_vlm_multi_turn/run_grpo_npu.sh 2>&1 | tee -a <LOG_FILE>
# PPO
bash examples/geo3k_vlm_multi_turn/run_ppo_npu.sh 2>&1 | tee -a <LOG_FILE>

Placeholders Reference

Placeholder	Description	Example
`<WORKSPACE>`	Root directory for all installations	`/root/slime-release`
`<CANN_PATH>`	Path to CANN installation directory	`/usr/local/ascend`
`<LOG_FILE>`	Path to log file for training output	`training.log`