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| Original file line number | Diff line number | Diff line change |
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| # NPU训练最佳实践 | ||
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| ## 目录 | ||
| - [环境准备](#环境准备) | ||
| - [微调](#微调) | ||
| - [推理](#推理) | ||
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| ## 环境准备 | ||
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| 实验环境：8 * 昇腾910B3 | ||
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| ```shell | ||
| pip install ms-swift -U | ||
| pip install torch-npu | ||
| ``` | ||
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| 测试环境是否安装正确： | ||
| ```python | ||
| from transformers.utils import is_torch_npu_available | ||
| import torch | ||
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| print(is_torch_npu_available()) # True | ||
| print(torch.npu.device_count()) # 8 | ||
| ``` | ||
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| ## 微调 | ||
| 以下介绍LoRA的微调, 全参数微调设置参数`--sft_type full`即可. | ||
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| ### 单卡训练 | ||
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| 通过如下命令启动单卡微调： | ||
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| ```shell | ||
| # 实验环境: 昇腾910B3 | ||
| # 显存需求: 25GB | ||
| # 运行时长: 8小时 | ||
| ASCEND_RT_VISIBLE_DEVICES=0 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| ``` | ||
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| ### 数据并行训练 | ||
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| ```shell | ||
| # 实验环境: 4 * 昇腾910B3 | ||
| # 显存需求: 4 * 30GB | ||
| # 运行时长: 2小时 | ||
| NPROC_PER_NODE=4 \ | ||
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| ``` | ||
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| ### Deepspeed训练 | ||
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| ZeRO2: | ||
| ```shell | ||
| # 实验环境: 4 * 昇腾910B3 | ||
| # 显存需求: 4 * 28GB | ||
| # 运行时长: 3小时 | ||
| NPROC_PER_NODE=4 \ | ||
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| --deepspeed default-zero2 \ | ||
| ``` | ||
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| ZeRO3: | ||
| ```shell | ||
| # 实验环境: 4 * 昇腾910B3 | ||
| # 显存需求: 4 * 25GB | ||
| # 运行时长: 8小时 | ||
| NPROC_PER_NODE=4 \ | ||
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| --deepspeed default-zero3 \ | ||
| ``` | ||
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| ## 推理 | ||
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| 原始模型: | ||
| ```shell | ||
| ASCEND_RT_VISIBLE_DEVICES=0 swift infer --model_type qwen1half-7b-chat | ||
| ``` | ||
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| LoRA微调后: | ||
| ```shell | ||
| ASCEND_RT_VISIBLE_DEVICES=0 swift infer --ckpt_dir xxx/checkpoint-xxx --load_dataset_config true | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,110 @@ | ||
| # NPU Best Practice | ||
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| ## Table of Contents | ||
| - [Environment Preparation](#Environment-Preparation) | ||
| - [Fine-tuning](#Fine-tuning) | ||
| - [Inference](#Inference) | ||
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| ## Environment Preparation | ||
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| Experimental environment: 8 * Ascend 910B3 | ||
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| ```shell | ||
| pip install ms-swift -U | ||
| pip install torch-npu | ||
| ``` | ||
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| Verify the installation of the testing environment: | ||
| ```python | ||
| from transformers.utils import is_torch_npu_available | ||
| import torch | ||
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| print(is_torch_npu_available()) # True | ||
| print(torch.npu.device_count()) # 8 | ||
| ``` | ||
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| ## Fine-tuning | ||
| The following introduces the fine-tuning of LoRA. Set the parameter `--sft_type full` for full parameter fine-tuning. | ||
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| ### Single Card Training | ||
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| Start single card fine-tuning with the following command: | ||
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| ```shell | ||
| # Experimental Environment: Ascend 910B3 | ||
| # GPU Memory Requirement: 25GB | ||
| # Runtime: 8 hours | ||
| ASCEND_RT_VISIBLE_DEVICES=0 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| ``` | ||
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| ### Training with DDP | ||
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| ```shell | ||
| # Experimental Environment: 4 * Ascend 910B3 | ||
| # GPU Memory Requirement: 4 * 30GB | ||
| # Runtime: 2 hours | ||
| NPROC_PER_NODE=4 \ | ||
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| ``` | ||
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| ### Training with DeepSpeed | ||
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| ZeRO2: | ||
| ```shell | ||
| # Experimental Environment: 4 * Ascend 910B3 | ||
| # GPU Memory Requirement: 4 * 28GB | ||
| # Runtime: 3 hours | ||
| NPROC_PER_NODE=4 \ | ||
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| --deepspeed default-zero2 \ | ||
| ``` | ||
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| ZeRO3: | ||
| ```shell | ||
| # Experimental Environment: 4 * Ascend 910B3 | ||
| # GPU Memory Requirement: 4 * 25GB | ||
| # Runtime: 8 hours | ||
| NPROC_PER_NODE=4 \ | ||
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ | ||
| swift sft \ | ||
| --model_type qwen1half-7b-chat \ | ||
| --dataset blossom-math-zh \ | ||
| --num_train_epochs 5 \ | ||
| --sft_type lora \ | ||
| --output_dir output \ | ||
| --deepspeed default-zero3 \ | ||
| ``` | ||
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| ## Inference | ||
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| Original Model: | ||
| ```shell | ||
| ASCEND_RT_VISIBLE_DEVICES=0 swift infer --model_type qwen1half-7b-chat | ||
| ``` | ||
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| After LoRA Fine-tuning: | ||
| ```shell | ||
| ASCEND_RT_VISIBLE_DEVICES=0 swift infer --ckpt_dir xxx/checkpoint-xxx --load_dataset_config true | ||
| ``` |
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