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===================================
Last updated: 10/31/2025.
我们在 verl 上增加对华为昇腾设备的支持。
硬件支持
-----------------------------------
Atlas 200T A2 Box16
Atlas 900 A2 PODc
Atlas 800T A3
安装
-----------------------------------
基础环境准备
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-----------+-------------+
| software | version |
+-----------+-------------+
| Python | == 3.10 |
+-----------+-------------+
| CANN | == 8.2.RC1 |
+-----------+-------------+
| torch | == 2.5.1 |
+-----------+-------------+
| torch_npu | == 2.5.1 |
+-----------+-------------+
基础环境准备请参照这份 `文档 <https://gitcode.com/Ascend/pytorch>`_ 。
vllm & vllm-ascend
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
为了能够在 verl 中正常使用 vllm，需使用以下命令编译安装 vllm 和 vllm-ascend。请注意根据机器类型区分安装方式。
.. code-block:: bash
# vllm
git clone -b v0.9.1 --depth 1 https://github.com/vllm-project/vllm.git
cd vllm
pip install -r requirements-build.txt
# for Atlas 200T A2 Box16
VLLM_TARGET_DEVICE=empty pip install -e . --extra-index https://download.pytorch.org/whl/cpu/
# for Atlas 900 A2 PODc
VLLM_TARGET_DEVICE=empty pip install -e .
.. code-block:: bash
# vllm-ascend
git clone -b v0.9.1 --depth 1 https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
export COMPILE_CUSTOM_KERNELS=1
python setup.py install
安装verl
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
git clone https://github.com/volcengine/verl.git
cd verl
pip install -r requirements-npu.txt
pip install -e .
DockerFile镜像构建
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
如需要通过DockerFile构建镜像， 请参考 `文档 <https://github.com/volcengine/verl/tree/main/docs/ascend_tutorial/dockerfile_build_guidance.rst>`_ 。
其他三方库说明
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+--------------+---------------+
| software | description |
+--------------+---------------+
| transformers | v4.52.4 |
+--------------+---------------+
| flash_attn | not supported |
+--------------+---------------+
| liger-kernel | not supported |
+--------------+---------------+
1. 支持通过 transformers 使能 --flash_attention_2， transformers 需等于 4.52.4版本。
2. 不支持通过 flash_attn 使能 flash attention 加速。
3. 不支持 liger-kernel 使能。
4. 针对 x86 服务器，需要安装 cpu 版本的 torchvision。
.. code-block:: bash
pip install torchvision==0.20.1+cpu --index-url https://download.pytorch.org/whl/cpu
快速开始
-----------------------------------
正式使用前，建议您通过对Qwen2.5-0.5B GRPO的训练尝试以检验环境准备和安装的正确性。
1.下载数据集并将数据集预处理为parquet格式，以便包含计算RL奖励所需的必要字段
.. code-block:: bash
python3 examples/data_preprocess/gsm8k.py --local_save_dir ~/data/gsm8k
2.执行训练
.. code-block:: bash
set -x
export VLLM_ATTENTION_BACKEND=XFORMERS
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=$HOME/data/gsm8k/train.parquet \
data.val_files=$HOME/data/gsm8k/test.parquet \
data.train_batch_size=128 \
data.max_prompt_length=512 \
data.max_response_length=128 \
data.filter_overlong_prompts=True \
data.truncation='error' \
actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \
actor_rollout_ref.actor.optim.lr=5e-7 \
actor_rollout_ref.model.use_remove_padding=False \
actor_rollout_ref.actor.entropy_coeff=0.001 \
actor_rollout_ref.actor.ppo_mini_batch_size=64 \
actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=20 \
actor_rollout_ref.actor.use_kl_loss=True \
actor_rollout_ref.actor.kl_loss_coef=0.001 \
actor_rollout_ref.actor.kl_loss_type=low_var_kl \
actor_rollout_ref.model.enable_gradient_checkpointing=True \
actor_rollout_ref.actor.fsdp_config.param_offload=False \
actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \
actor_rollout_ref.rollout.enable_chunked_prefill=False \
actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
actor_rollout_ref.rollout.name=vllm \
actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
actor_rollout_ref.rollout.n=5 \
actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \
actor_rollout_ref.ref.fsdp_config.param_offload=True \
algorithm.kl_ctrl.kl_coef=0.001 \
trainer.critic_warmup=0 \
trainer.logger=console \
trainer.project_name='verl_grpo_example_gsm8k' \
trainer.experiment_name='qwen2_7b_function_rm' \
trainer.n_gpus_per_node=8 \
trainer.nnodes=1 \
trainer.save_freq=-1 \
trainer.test_freq=5 \
trainer.total_epochs=1 \
trainer.device=npu $@
(可选) 设置MindSpeed训练后端指导
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
1. 参考 `MindSpeed README <https://gitcode.com/Ascend/MindSpeed>`_ 说明安装 MindSpeed 加速库。
2. 使能 verl worker 模型 ``strategy`` 配置为 ``megatron`` ，例如 ``actor_rollout_ref.actor.strategy=megatron``。
3. MindSpeed 自定义入参可通过 ``override_transformer_config`` 参数传入，例如对 actor 模型开启 FA 特性可使用 ``+actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True``。
4. 更多特性信息可参考 `MindSpeed+verl 文档 <https://gitcode.com/Ascend/MindSpeed/blob/master/docs/user-guide/verl.md>`_ 。
支持现状
-----------------------------------
**表1** RL类算法
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| algorithm | model | actor.strategy | rollout.name | hardware |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| GRPO | Qwen2.5-7B-instruct | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| GRPO | Qwen2.5-32B-instruct | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| GRPO | Qwen2.5-VL-3B-instruct | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| GRPO | Qwen2.5-VL-7B-instruct | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| GRPO | Qwen2.5-VL-32B-instruct | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| GRPO | Qwen3-8B | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| GRPO | Qwen3-32B | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| DAPO | Qwen2.5-7B-instruct | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| DAPO | Qwen2.5-32B | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| DAPO | Qwen3-8B-base | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| DAPO | Qwen3-14B-base | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| DAPO | Qwen3-30B-A3B-base | FSDP | vllm-ascend | Atlas 200T A2 Box16 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| DAPO | Qwen3-30B-A3B | megatron | vllm-ascend | Atlas 800T A3 |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
| PPO | Qwen3-8B | FSDP | vllm-ascend | Atlas 900 A2 PODc |
+-----------+-------------------------+-------------------+-------------------+--------------------------+
**表2** SFT类算法
+-----------+-------------------------+-------------------+----------------------+
| algorithm | model | actor.strategy | hardware |
+-----------+-------------------------+-------------------+----------------------+
| SFT-PEFT | Qwen3-8B | FSDP | Atlas 900 A2 PODc |
+-----------+-------------------------+-------------------+----------------------+
| ReTool-SFT| Qwen2.5-7B-instruct | FSDP | Atlas 900 A2 PODc |
+-----------+-------------------------+-------------------+----------------------+
计划
-----------------------------------
查看 `roadmap <https://github.com/volcengine/verl/discussions/2171>`_ 获取更多特性的支持进度。
声明
-----------------------------------
verl中提供的ascend支持代码、Dockerfile、镜像皆为参考样例，如在生产环境中使用请通过官方正式途径沟通，谢谢。
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