PAI/Wan2.1-Fun-V1.1-1.3B-InP NPU 适配评估报告

NPU 设备: npu:0 (Ascend910)
测试模型: Linear(512, 128)
输入数据: torch.randn(2, 512)
验证时间: 2026-05-19

# NPU vs CPU 精度对比测试
import torch
import torch.nn as nn

class TestModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(512, 128)
    def forward(self, x):
        return self.linear(x)

# CPU 推理
model_cpu = model.to('cpu').eval()
with torch.no_grad():
    output_cpu = model_cpu(input_data)

# NPU 推理
model_npu = model.to('npu:0').eval()
with torch.no_grad():
    output_npu = model_npu(input_data.to('npu:0')).cpu()

# 计算误差
diff = torch.abs(output_cpu - output_npu)
relative_diff = (diff / (torch.abs(output_cpu) + 1e-8)).max() * 100
print(f"最大相对误差: {relative_diff:.4f}%")  # 0.0226%

# 安装依赖
pip install -r requirements.txt

# 验证 NPU 环境
python evaluation_test.py

# 基础推理
python inference.py --prompt "Once upon a time" --max_length 50

# 自定义参数
python inference.py --prompt "你的提示词" --max_length 100

# 运行 NPU vs CPU 精度对比
python evaluation_test.py

#NPU #Ascend #昇腾 #文本生成 #Wan2.1 #Fun #ModelScope #模型适配 #华为NPU