| # NPU Support |
| Author: [chuanzhubin](https://github.com/chuanzhubin) |
|
|
| ## Environment Preparation |
|
|
| Experiment Environment: 8 * Ascend 910B3 64G (The device is provided by [@chuanzhubin](https://github.com/chuanzhubin), thanks for the support of modelscope and swift~) |
|
|
| ```shell |
| # Create a new conda virtual environment (optional) |
| conda create -n swift-npu python=3.10 -y |
| conda activate swift-npu |
| |
| # Set pip global mirror (optional, to speed up downloads) |
| pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ |
| pip install ms-swift -U |
| |
| # Install torch-npu |
| pip install torch-npu decorator |
| # If you want to use deepspeed (to control memory usage, training speed might decrease) |
| pip install deepspeed |
| ``` |
|
|
| Check if the test environment is installed correctly and whether the NPU can be loaded properly. |
| ```python |
| from transformers.utils import is_torch_npu_available |
| import torch |
| |
| print(is_torch_npu_available()) # True |
| print(torch.npu.device_count()) # 8 |
| print(torch.randn(10, device='npu:0')) |
| ``` |
|
|
| Check the P2P connections of the NPU, where we can see that each NPU is interconnected through 7 HCCS links with other NPUs. |
| ```shell |
| (valle) root@valle:~/src# npu-smi info -t topo |
| NPU0 NPU1 NPU2 NPU3 NPU4 NPU5 NPU6 NPU7 CPU Affinity |
| NPU0 X HCCS HCCS HCCS HCCS HCCS HCCS HCCS 144-167 |
| NPU1 HCCS X HCCS HCCS HCCS HCCS HCCS HCCS 144-167 |
| NPU2 HCCS HCCS X HCCS HCCS HCCS HCCS HCCS 96-119 |
| NPU3 HCCS HCCS HCCS X HCCS HCCS HCCS HCCS 96-119 |
| NPU4 HCCS HCCS HCCS HCCS X HCCS HCCS HCCS 0-23 |
| NPU5 HCCS HCCS HCCS HCCS HCCS X HCCS HCCS 0-23 |
| NPU6 HCCS HCCS HCCS HCCS HCCS HCCS X HCCS 48-71 |
| NPU7 HCCS HCCS HCCS HCCS HCCS HCCS HCCS X 48-71 |
| |
| Legend: |
| |
| X = Self |
| SYS = Path traversing PCIe and NUMA nodes. Nodes are connected through SMP, such as QPI, UPI. |
| PHB = Path traversing PCIe and the PCIe host bridge of a CPU. |
| PIX = Path traversing a single PCIe switch |
| PXB = Path traversing multiple PCIe switches |
| HCCS = Connection traversing HCCS. |
| NA = Unknown relationship. |
| ``` |
|
|
| Check the status of the NPU. Detailed information about the `npu-smi` command can be found in the [official documentation](https://support.huawei.com/enterprise/zh/doc/EDOC1100079287/10dcd668). |
| ```shell |
| (valle) root@valle:~/src# npu-smi info |
| +------------------------------------------------------------------------------------------------+ |
| | npu-smi 24.1.rc1.b030 Version: 24.1.rc1.b030 | |
| +---------------------------+---------------+----------------------------------------------------+ |
| | NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page)| |
| | Chip | Bus-Id | AICore(%) Memory-Usage(MB) HBM-Usage(MB) | |
| +===========================+===============+====================================================+ |
| | 0 910B3 | OK | 101.8 43 0 / 0 | |
| | 0 | 0000:C1:00.0 | 0 0 / 0 3318 / 65536 | |
| +===========================+===============+====================================================+ |
| | 1 910B3 | OK | 92.0 39 0 / 0 | |
| | 0 | 0000:C2:00.0 | 0 0 / 0 3314 / 65536 | |
| +===========================+===============+====================================================+ |
| | 2 910B3 | OK | 102.0 40 0 / 0 | |
| | 0 | 0000:81:00.0 | 0 0 / 0 3314 / 65536 | |
| +===========================+===============+====================================================+ |
| | 3 910B3 | OK | 99.8 40 0 / 0 | |
| | 0 | 0000:82:00.0 | 0 0 / 0 3314 / 65536 | |
| +===========================+===============+====================================================+ |
| | 4 910B3 | OK | 98.6 45 0 / 0 | |
| | 0 | 0000:01:00.0 | 0 0 / 0 3314 / 65536 | |
| +===========================+===============+====================================================+ |
| | 5 910B3 | OK | 99.7 44 0 / 0 | |
| | 0 | 0000:02:00.0 | 0 0 / 0 3314 / 65536 | |
| +===========================+===============+====================================================+ |
| | 6 910B3 | OK | 103.8 45 0 / 0 | |
| | 0 | 0000:41:00.0 | 0 0 / 0 3314 / 65536 | |
| +===========================+===============+====================================================+ |
| | 7 910B3 | OK | 98.2 44 0 / 0 | |
| | 0 | 0000:42:00.0 | 0 0 / 0 3315 / 65536 | |
| +===========================+===============+====================================================+ |
| ``` |
|
|
| ## Fine-tuning |
| The following introduces the fine-tuning of LoRA. To perform full-parameter fine-tuning, simply set the parameter `--train_type full`. |
|
|
| | Model Size | Number of NPUs | Deepspeed Type | Max Memory Usage | |
| |------|-------|-------------|-----------| |
| | 7B | 1 | None | 1 * 28 GB | |
| | 7B | 4 | None | 4 * 22 GB | |
| | 7B | 4 | zero2 | 4 * 28 GB | |
| | 7B | 4 | zero3 | 4 * 22 GB | |
| | 7B | 8 | None | 8 * 22 GB | |
| | 14B | 1 | None | 1 * 45 GB | |
| | 14B | 8 | None | 8 * 51 GB | |
| | 14B | 8 | zero2 | 8 * 49 GB | |
| | 14B | 8 | zero3 | 8 * 31 GB | |
|
|
| ### Single Card Training |
|
|
| Start single card fine-tuning with the following command: (Note: If NaN occurs during fine-tuning, please set `--torch_dtype float32`.) |
|
|
| ```shell |
| # Experiment environment: Ascend 910B3 |
| # Memory requirement: 28 GB |
| # Runtime: 8 hours |
| ASCEND_RT_VISIBLE_DEVICES=0 \ |
| swift sft \ |
| --model Qwen/Qwen2-7B-Instruct \ |
| --dataset AI-ModelScope/blossom-math-v2 \ |
| --num_train_epochs 5 \ |
| --train_type lora \ |
| --output_dir output \ |
| --learning_rate 1e-4 \ |
| --gradient_accumulation_steps 16 \ |
| --save_steps 100 \ |
| --eval_steps 100 |
| ``` |
|
|
| ### Data Parallel Training |
| We use 4 cards for DDP training. |
|
|
| ```shell |
| # Experiment environment: 4 * Ascend 910B3 |
| # Memory requirement: 4 * 22 GB |
| # Runtime: 2 hours |
| NPROC_PER_NODE=4 \ |
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ |
| swift sft \ |
| --model Qwen/Qwen2-7B-Instruct \ |
| --dataset AI-ModelScope/blossom-math-v2 \ |
| --num_train_epochs 5 \ |
| --train_type lora \ |
| --output_dir output \ |
| ... |
| ``` |
|
|
| ### Deepspeed Training |
|
|
| ZeRO2: |
| ```shell |
| # Experiment environment: 4 * Ascend 910B3 |
| # Memory requirement: 4 * 28GB |
| # Runtime: 3.5 hours |
| NPROC_PER_NODE=4 \ |
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ |
| swift sft \ |
| --model Qwen/Qwen2-7B-Instruct \ |
| --dataset AI-ModelScope/blossom-math-v2 \ |
| --num_train_epochs 5 \ |
| --train_type lora \ |
| --output_dir output \ |
| --deepspeed zero2 \ |
| ... |
| ``` |
|
|
| ZeRO3: |
| ```shell |
| # Experiment environment: 4 * Ascend 910B3 |
| # Memory requirement: 4 * 22 GB |
| # Runtime: 8.5 hours |
| NPROC_PER_NODE=4 \ |
| ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 \ |
| swift sft \ |
| --model Qwen/Qwen2-7B-Instruct \ |
| --dataset AI-ModelScope/blossom-math-v2 \ |
| --num_train_epochs 5 \ |
| --train_type lora \ |
| --output_dir output \ |
| --deepspeed zero3 \ |
| ... |
| ``` |
|
|
| ## Inference |
|
|
| Original Model: |
| ```shell |
| ASCEND_RT_VISIBLE_DEVICES=0 swift infer \ |
| --model Qwen/Qwen2-7B-Instruct \ |
| --stream true --max_new_tokens 2048 |
| ``` |
|
|
| After LoRA Fine-tuning: |
| ```shell |
| ASCEND_RT_VISIBLE_DEVICES=0 swift infer \ |
| --adapters xxx/checkpoint-xxx --load_data_args true \ |
| --stream true --max_new_tokens 2048 |
| |
| # Merge LoRA and infer |
| ASCEND_RT_VISIBLE_DEVICES=0 swift export --adapters xx/checkpoint-xxx --merge_lora true |
| |
| ASCEND_RT_VISIBLE_DEVICES=0 swift infer \ |
| --model xxx/checkpoint-xxx-merged --load_data_args true \ |
| --stream true --max_new_tokens 2048 |
| ``` |
|
|
| ## Deployment |
| NPUs do not support using vllm for inference/acceleration during deployment, but can be deployed using native PyTorch. |
|
|
| Original Model: |
| ```shell |
| ASCEND_RT_VISIBLE_DEVICES=0 swift deploy --model Qwen/Qwen2-7B-Instruct --max_new_tokens 2048 |
| ``` |
|
|
| After LoRA Fine-tuning: |
| ```shell |
| ASCEND_RT_VISIBLE_DEVICES=0 swift deploy --adapters xxx/checkpoint-xxx --max_new_tokens 2048 |
| |
| # Merge LoRA and deploy |
| ASCEND_RT_VISIBLE_DEVICES=0 swift export --adapters xx/checkpoint-xxx --merge_lora true |
| ASCEND_RT_VISIBLE_DEVICES=0 swift deploy --model xxx/checkpoint-xxx-merged --max_new_tokens 2048 |
| ``` |
|
|
