--- title: Hugging Face Ascend-PyTorch-模型库-ModelZoo-昇腾社区 description: Hugging Face 核心套件 transformers 、 accelerate 、 peft 、 trl 已原生支持 Ascend NPU，相关功能陆续补齐中。 keywords: Hugging,Face,Ascend,PyTorch,模型库,ModelZoo,昇腾社区,简介 url: https://www.hiascend.com/software/modelzoo/models/detail/14ae18251d6a417d8d9fdf1c3754dae2 section: (其他) --- # Hugging Face Ascend-PyTorch-模型库-ModelZoo-昇腾社区 URL: https://www.hiascend.com/software/modelzoo/models/detail/14ae18251d6a417d8d9fdf1c3754dae2 描述: Hugging Face 核心套件 transformers 、 accelerate 、 peft 、 trl 已原生支持 Ascend NPU，相关功能陆续补齐中。 关键词: Hugging,Face,Ascend,PyTorch,模型库,ModelZoo,昇腾社区,简介 ModelZoo主页 主页 [了解详情](https://www.hiascend.com/software/modelzoo)大模型 [了解详情](https://www.hiascend.com/software/modelzoo/big-models)模型库 [了解详情](https://www.hiascend.com/software/modelzoo/models)工具库 [了解详情](https://www.hiascend.com/software/modelzoo/tools)权重库 [了解详情](https://www.hiascend.com/software/modelzoo/datasets)ModelShow [了解详情](https://www.hiascend.com/software/modelzoo/modelshow)DeepSeek专区HOT [了解详情](https://www.hiascend.com/developer/deepseek)文档中心论坛交流 主页 [了解详情](https://www.hiascend.com/software/modelzoo)大模型 [了解详情](https://www.hiascend.com/software/modelzoo/big-models)模型库 [了解详情](https://www.hiascend.com/software/modelzoo/models)工具库 [了解详情](https://www.hiascend.com/software/modelzoo/tools)权重库 [了解详情](https://www.hiascend.com/software/modelzoo/datasets)ModelShow [了解详情](https://www.hiascend.com/software/modelzoo/modelshow)DeepSeek专区HOT [了解详情](https://www.hiascend.com/developer/deepseek)文档中心论坛交流 模型库 [了解详情](https://www.hiascend.com/software/modelzoo/models) Hugging Face Ascend-PyTorch ## Hugging Face Ascend-PyTorch 套件PyTorch训推一体1160次浏览2026/02/02更新 获取源码 Hugging Face Ascend-PyTorch 查看模型源码 ... # Hugging Face ## 简介 :tada: Hugging Face 核心套件transformers [了解详情](https://github.com/huggingface/transformers)、accelerate [了解详情](https://github.com/huggingface/accelerate)、peft [了解详情](https://github.com/huggingface/peft)、trl [了解详情](https://github.com/huggingface/trl)已原生支持 Ascend NPU，相关功能陆续补齐中。 本仓用于发布最新进展、需求/问题跟踪、测试用例。 ## 更新日志 [23/09/13] :fire: accelerate 支持 Ascend NPU 使用 BF16 格式进行混合精度训练 [23/09/05] :fire:transformers [了解详情](https://github.com/huggingface/transformers)支持 Ascend NPU 使用 apex 进行混合精度训练 [23/08/23] :tada: 现在`transformers>=4.32.0``accelearte>=0.22.0`、`peft>=0.5.0`、`trl>=0.5.0`原生支持 Ascend NPU！通过`pip install`即可安装体验 [23/08/11] :sparkles:trl [了解详情](https://github.com/huggingface/trl)原生支持 Ascend NPU，请源码安装体验 [23/08/05] :sparkles:accelerate [了解详情](https://github.com/huggingface/accelerate)支持 Ascend NPU 使用 FSDP(pt-2.1, Experimental) [23/08/02] :sparkles:peft [了解详情](https://github.com/huggingface/peft)原生支持 Ascend NPU 的加载 adapter，请源码安装体验 [23/07/19] :sparkles:transformers [了解详情](https://github.com/huggingface/transformers)原生支持 Ascend NPU 的单卡/多卡/amp 训练，请源码安装体验 [23/07/12] :sparkles:accelerate [了解详情](https://github.com/huggingface/accelerate)原生支持 Ascend NPU 的单卡/多卡/amp 训练，请源码安装体验 ## Transformers ### 使用说明 当前transformers [了解详情](https://github.com/huggingface/transformers)训练流程已原生支持 Ascend NPU，这里以参考示例中text-classification [了解详情](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification)任务为例说明如何在 Ascend NPU 微调 bert 模型。 #### 环境准备 1. 请参考《Pytorch框架训练环境准备 [了解详情](https://gitee.com/link?target=https%3A%2F%2Fwww.hiascend.com%2Fdocument%2Fdetail%2Fzh%2FModelZoo%2Fpytorchframework%2Fptes)》准备环境，要求 python>=3.8, PyTorch >= 1.9 2. 安装 transformers ``` pip3 install -U transformers ``` #### 单卡训练 获取text-classification [了解详情](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification)训练脚本并安装相关依赖 ``` git clone https://github.com/huggingface/transformers.git cd examples/pytorch/text-classification pip install -r requirements.txt ``` 执行单卡训练 参考text-classification [了解详情](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification)README，执行： ``` export TASK_NAME=mrpc python run_glue.py \ --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ ``` #### 多卡训练 执行多卡训练 ``` export TASK_NAME=mrpc python -m torch.distributed.launch --nproc_per_node=8 run_glue.py \ --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ ``` #### 混合精度训练 如果希望使用混合精度，请传入训练参数`--fp16` ``` export TASK_NAME=mrpc python run_glue.py \ --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --fp16 \ --output_dir /tmp/$TASK_NAME/ ``` 如果希望使用apex进行混合精度训练，请先确保安装支持 Ascend NPU 的 apex 包，然后传入`--fp16`和`--half_precision_backend apex` ``` export TASK_NAME=mrpc python run_glue.py \ --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --fp16 \ --half_precision_backend apex \ --output_dir /tmp/$TASK_NAME/ ``` ### 支持的特性 - single NPU - multi-NPU on one node (machine) - FP16 mixed precision - PyTorch Fully Sharded Data Parallel (FSDP) support (Partially support,Experimental) need more test - DeepSpeed support (Experimental) - Big model inference ## Accelerate Accelerate 已经原生支持 Ascend NPU，这里给出基本的使用方法，请参考accelerate-usage-guides [了解详情](https://huggingface.co/docs/accelerate/main/en/usage_guides/explore)的解锁更多用法。 ### 使用说明 执行`accelerate env`查看当前环境，确保`PyTorch NPU available`为`True` ``` $ accelerate env ----------------------------------------------------------------------------------------------------------------------------------------------------------- Copy-and-paste the text below in your GitHub issue - `Accelerate` version: 0.23.0.dev0 - Platform: Linux-5.10.0-60-18.0.50.oe2203.aarch64-with-glibc2.26 - Python version: 3.8.17 - Numpy version: 1.24.4 - PyTorch version (GPU?): 2.1.0.dev20230817 (False) - PyTorch XPU available: False - PyTorch NPU available: True - System RAM: 2010.33 GB - `Accelerate` default config: Not found ``` 场景一：单卡训练 在您的设备上执行`accelerate config````shell ## $ accelerate config ## In which compute environment are you running? This machine Which type of machine are you using? No distributed training Do you want to run your training on CPU only (even if a GPU / Apple Silicon / Ascend NPU device is available)? [yes/NO]:NO Do you wish to optimize your script with torch dynamo?[yes/NO]:NO Do you want to use DeepSpeed? [yes/NO]:NO What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:all Do you wish to use FP16 or BF16 (mixed precision)? fp16 ``` 设置成功后将会得到如下提示信息： ```text accelerate configuration saved at /root/.cache/huggingface/accelerate/default_config.yaml ``` 这将生成一个配置文件，在执行操作时将自动使用该文件来设置训练选项 ``` accelerate launch my_script.py --args_to_my_script ``` 举个例子，以下是在NPU上运行NLP示例`examples/nlp_example.py`(在accelerate根目录下)。在执行完`accelerate config`后生成的`default_config.yaml`如下： ``` compute_environment: LOCAL_MACHINE distributed_type: 'NO' downcast_bf16: 'no' gpu_ids: all machine_rank: 0 main_training_function: main mixed_precision: 'fp16' num_machines: 1 num_processes: 1 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false ``` ``` accelerate launch examples/nlp_example.py ``` 场景二：单机多卡训练 同上，首先执行`accelerate config` ``` $ accelerate config ----------------------------------------------------------------------------------------------------------------------------------------------------------- In which compute environment are you running? This machine ----------------------------------------------------------------------------------------------------------------------------------------------------------- Which type of machine are you using? multi-NPU How many different machines will you use (use more than 1 for multi-node training)? [1]: 1 Should distributed operations be checked while running for errors? This can avoid timeout issues but will be slower. [yes/NO]: NO Do you wish to optimize your script with torch dynamo?[yes/NO]:NO Do you want use FullyShardedDataParallel? [yes/NO]:NO How many NPU(s) should be used for distributed training? [1]:4 What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:all Do you wish to use FP16 or BF16 (mixed precision)? fp16 ``` 生成的配置如下 ``` compute_environment: LOCAL_MACHINE debug: false distributed_type: MULTI_NPU downcast_bf16: 'no' gpu_ids: all machine_rank: 0 main_training_function: main mixed_precision: 'fp16' num_machines: 1 num_processes: 4 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false ``` 运行NLP示例`examples/nlp_example.py`(在accelerate根目录下)。 ``` accelerate launch examples/nlp_example.py ``` ### 支持的特性 - single NPU - multi-NPU on one node (machine) - FP16/BF16 mixed precision - PyTorch Fully Sharded Data Parallel (FSDP) support (Partially support,Experimental) need more test - DeepSpeed support (Experimental) - Big model inference - Quantization ## PEFT 施工中... ## TRL 施工中... ## FQA - 使用`transformers`、`accelerate`、`trl`等套件时仅需在您的脚本入口处添加`import torch, torch_npu`请勿使用`from torch_npu.contrib import transfer_to_npu` ``` import torch ipmort torch_npu # original code, no from torch_npu.contrib import transfer_to_npu ``` - 使用混合精度训练时建议开启非饱和模式：`export INF_NAN_MODE_ENABLE=1` | 版本 | 更新时间 | 精度 | 处理器 | CANN版本 | 操作 | | --- | --- | --- | --- | --- | ---| 暂无数据 使用模型资源和服务前，请您仔细阅读并理解透彻《昇腾深度学习模型许可协议 3.0》 [了解详情](https://www.hiascend.com/legal/modelzoo_models_license)