Chronos-2 昇腾 NPU 迁移适配指导书

# 拉取昇腾基础镜像（以 CANN 8.5.x 为例）
docker pull quay.io/ascend/cann:8.5.0-910b-ubuntu22.04-py3.11

# 安装 Python 依赖
pip install -r requirements.txt

torch>=2.7.0
torch-npu>=2.7.0
transformers>=4.31.0
huggingface-hub>=0.19.0
numpy>=1.24.0
pandas>=2.0.0

# 加载 CANN 环境
source /usr/local/Ascend/ascend-toolkit/set_env.sh

# 验证 CANN 版本
echo $ASCEND_AISERVER

# 配置 HuggingFace 镜像（必须！）
export HF_ENDPOINT=https://hf-mirror.com
echo "HuggingFace 镜像: $HF_ENDPOINT"

# 使用镜像源安装
pip install -r requirements.txt \
    -i https://mirrors.aliyun.com/pypi/simple/

python3 -c "
import torch
import torch.npu
print(f'torch: {torch.__version__}')
print(f'torch-npu: {torch.npu.__version__}')
print(f'NPU available: {torch.npu.is_available()}')
"

# 微调训练命令
export HF_ENDPOINT=https://hf-mirror.com
python3 ascend_finetune.py \
    --model-path "amazon/chronos-2" \
    --npu-device-id 4 \
    --n-samples 10 \
    --num-steps 100 \
    --batch-size 4 \
    --prediction-length 96 \
    --context-length 512 \
    --learning-rate 1e-4 \
    --finetune-mode full \
    --output-dir ./checkpoints

[✓] 微调完成!
[✓] 模型已保存到: checkpoints/chronos2_finetuned_20260403_xxxxxx

# 使用微调后的模型进行推理
export HF_ENDPOINT=https://hf-mirror.com
python3 ascend_infer.py \
    --model-path ./checkpoints/chronos2_finetuned_20260403_xxxxxx \
    --device-id 4 \
    --context-length 512 \
    --prediction-length 96 \
    --n-series 4

[✓] 推理完成!
[✓] NPU 推理验证通过!

# NPU vs CPU 精度评测
export HF_ENDPOINT=https://hf-mirror.com
python3 eval_accuracy.py \
    --model-path ./checkpoints/chronos2_finetuned_20260403_xxxxxx \
    --n-samples 10 \
    --npu-device-id 4 \
    --output-dir ./eval_results

python3 benchmark_performance.py \
    --model-path ./checkpoints/chronos2_finetuned_20260403_xxxxxx \
    --npu-device-id 4 \
    --n-iterations 100