HarmonyOS 5 端侧模型推理精度测试：如何量化NPU加速后的浮点误差

以下为 ​​HarmonyOS 5端侧NPU模型推理精度测试方案​​，包含浮点误差量化、对比测试框架和误差分析的完整代码实现：

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1. 测试架构设计

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2. 核心测试模块

2.1 双路推理执行器

// dual-inference.ets
class InferenceComparator {
  static async run(model: Model, input: Tensor): Promise<CompareResult> {
    // CPU参考路径
    const cpuOutput = await NeuralEngine.runOnCPU(model, input);
    
    // NPU加速路径
    const npuOutput = await NPUAccelerator.run(model, input);
    
    return {
      cpu: cpuOutput,
      npu: npuOutput,
      diff: this.calculateDiff(cpuOutput, npuOutput)
    };
  }

  private static calculateDiff(cpu: Tensor, npu: Tensor): DiffMetrics {
    const absError = tensorAbs(sub(cpu, npu));
    const relError = tensorDiv(absError, tensorAbs(cpu));
    
    return {
      maxAbs: tensorMax(absError),
      avgAbs: tensorMean(absError),
      maxRel: tensorMax(relError),
      avgRel: tensorMean(relError)
    };
  }
}

2.2 误差统计方法

// error-metrics.ets
class ErrorAnalyzer {
  static calculateErrorDistribution(ref: Tensor, actual: Tensor): ErrorStats {
    const diff = tensorSub(ref, actual);
    const absDiff = tensorAbs(diff);
    const relDiff = tensorDiv(absDiff, tensorAdd(tensorAbs(ref), 1e-9)); // 避免除零

    return {
      maxAbsolute: tensorMax(absDiff),
      meanAbsolute: tensorMean(absDiff),
      maxRelative: tensorMax(relDiff),
      meanRelative: tensorMean(relDiff),
      errorHistogram: this.buildHistogram(absDiff)
    };
  }

  private static buildHistogram(tensor: Tensor): Histogram {
    const values = tensor.dataSync();
    return values.reduce((hist, val) => {
      const bin = Math.floor(val * 10); // 按0.1间隔分箱
      hist[bin] = (hist[bin] || 0) + 1;
      return hist;
    }, {});
  }
}

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3. 测试数据集构建

3.1 典型输入生成

// test-data.ets
class TestDataGenerator {
  static generateInputs(modelInfo: ModelSpec): Tensor[] {
    const inputs = [];
    const { inputShape } = modelInfo;
    
    // 标准测试用例
    inputs.push(tensorOnes(inputShape));  // 全1输入
    inputs.push(tensorZeros(inputShape)); // 全0输入
    
    // 随机测试用例
    for (let i = 0; i < 10; i++) {
      inputs.push(tensorRandomNormal(inputShape));
    }
    
    // 边界值用例
    inputs.push(tensorFill(inputShape, 1e-5));
    inputs.push(tensorFill(inputShape, 1e5));
    
    return inputs;
  }
}

3.2 黄金数据集加载

// golden-data.ets
class GoldenDataset {
  static async load(modelName: string): Promise<GoldenData> {
    const [inputs, outputs] = await Promise.all([
      FileSystem.readTensor(`golden/${modelName}/inputs/`),
      FileSystem.readTensor(`golden/${modelName}/outputs/`)
    ]);
    
    return { inputs, outputs };
  }
}

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4. NPU特性适配

4.1 量化误差补偿

// quant-compensator.ets
class QuantizationCompensator {
  static compensate(output: Tensor, model: Model): Tensor {
    if (model.quantization?.type === 'INT8') {
      const scale = model.quantization.scale;
      return tensorMul(output, scale);
    }
    return output;
  }
}

4.2 NPU精度模式设置

// npu-config.ets
class NPUPrecisionMode {
  static setHighPrecision() {
    NPUConfig.set({
      arithmeticPrecision: 'FP16',
      accumulationPrecision: 'FP32'
    });
  }

  static setLowPowerMode() {
    NPUConfig.set({
      arithmeticPrecision: 'INT8',
      accumulationPrecision: 'INT16'
    });
  }
}

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5. 完整测试流程

5.1 精度验证主流程

// precision-test.ets
async function runPrecisionTest(model: Model) {
  // 1. 准备测试数据
  const inputs = TestDataGenerator.generateInputs(model);
  
  // 2. 执行双路推理
  const results = await Promise.all(
    inputs.map(input => InferenceComparator.run(model, input))
  );
  
  // 3. 分析误差
  const errorReport = ErrorAnalyzer.generateReport(results);
  
  // 4. 验证精度要求
  return AccuracyValidator.check(
    errorReport, 
    model.precisionRequirements
  );
}

5.2 误差阈值检查

// accuracy-validator.ets
class AccuracyValidator {
  static check(report: ErrorReport, requirements: PrecisionReq): boolean {
    const pass = [
      report.maxAbsolute <= requirements.maxAbsoluteError,
      report.maxRelative <= requirements.maxRelativeError,
      report.errorHistogram[0] >= requirements.exactMatchRatio * report.total
    ];
    
    return pass.every(Boolean);
  }
}

---

6. 可视化分析

6.1 误差热力图

// error-heatmap.ets
@Component
struct ErrorHeatmap {
  @Prop diff: Tensor;

  build() {
    Canvas() {
      ForEach(this.dumpMatrix(), (row, i) => {
        ForEach(row, (value, j) => {
          Rect({
            width: 10,
            height: 10,
            fill: this.getColor(value)
          }).position(10 * j, 10 * i)
        })
      })
    }
  }

  private getColor(value: number): string {
    const intensity = Math.min(255, Math.floor(value * 2550));
    return `rgb(${intensity}, ${255 - intensity}, 0)`;
  }
}

6.2 统计报告生成

// report-generator.ets
function generateTextReport(report: ErrorReport): string {
  return `
  # NPU精度测试报告
  ## 基础信息
  - 测试模型: ${report.modelName}
  - NPU型号: ${report.npuType}
  - 测试时间: ${new Date().toISOString()}

  ## 误差统计
  - 最大绝对误差: ${report.maxAbsolute.toExponential(2)}
  - 平均绝对误差: ${report.avgAbsolute.toExponential(2)}
  - 最大相对误差: ${(report.maxRelative * 100).toFixed(2)}%
  - 平均相对误差: ${(report.avgRelative * 100).toFixed(2)}%

  ## 误差分布
  ${Object.entries(report.errorHistogram)
    .map(([range, count]) => `- ${range}: ${count}个`)
    .join('\n')}
  `;
}

---

7. 高级分析功能

7.1 逐层误差分析

// layer-wise.ets
class LayerwiseAnalyzer {
  static async analyze(model: Model, input: Tensor) {
    const cpuLayers = await Profiler.traceCPU(model, input);
    const npuLayers = await Profiler.traceNPU(model, input);
    
    return cpuLayers.map((cpu, i) => ({
      layerName: cpu.name,
      ...ErrorAnalyzer.calculateErrorDistribution(
        cpu.output, 
        npuLayers[i].output
      )
    }));
  }
}

7.2 数值稳定性测试

// stability-test.ets
class NumericalStability {
  static test(model: Model) {
    const smallInput = tensorFill(model.inputShape, 1e-6);
    const largeInput = tensorFill(model.inputShape, 1e6);
    
    return Promise.all([
      InferenceComparator.run(model, smallInput),
      InferenceComparator.run(model, largeInput)
    ]).then(([small, large]) => ({
      smallInputError: small.diff,
      largeInputError: large.diff
    }));
  }
}

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8. 关键指标定义

指标名称	计算公式	合格标准
最大绝对误差	max	CPU-NPU		≤1e-3
平均绝对误差	mean	CPU-NPU		≤5e-4
最大相对误差	max(	CPU-NPU	/	CPU	)	≤5%
误差分布峰度	误差直方图统计	90%误差<0.1%

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9. 完整测试示例

9.1 模型精度验证

// model-test.ets
describe('MobileNetV3 NPU精度测试', () => {
  const model = await ModelLoader.load('mobilenet_v3_small');
  
  it('应满足浮点误差要求', async () => {
    const report = await runPrecisionTest(model);
    expect(report.maxAbsolute).toBeLessThan(1e-3);
    expect(report.maxRelative).toBeLessThan(0.05);
  });

  it('小数值输入应稳定', async () => {
    const { smallInputError } = await NumericalStability.test(model);
    expect(smallInputError.maxRelative).toBeLessThan(0.1);
  });
});

9.2 CI集成配置

# .github/workflows/npu-test.yml
jobs:
  npu-precision:
    runs-on: harmonyos-npu
    steps:
      - uses: harmonyos/model-test-action@v1
        with:
          model: mobilenet_v3_small
          precision: fp16
          tolerance: 0.001
      - name: Upload Report
        uses: actions/upload-artifact@v3
        with:
          name: npu-precision-report
          path: report.json

---

10. 扩展测试场景

10.1 混合精度测试

// mixed-precision.ets
describe('混合精度模式', () => {
  const modes = ['FP16', 'FP16-INT8', 'INT8'];
  
  modes.forEach(mode => {
    it(`模式 ${mode} 应满足误差要求`, async () => {
      NPUConfig.setPrecisionMode(mode);
      const report = await runPrecisionTest(model);
      expect(report.maxRelative).toBeLessThan(mode === 'INT8' ? 0.1 : 0.05);
    });
  });
});

10.2 温度影响测试

// thermal-test.ets
class ThermalImpactTest {
  static async run(model: Model) {
    // 升温阶段
    await NPUStressTest.run(60); // 持续60秒压力测试
    const hotReport = await runPrecisionTest(model);
    
    // 冷却后
    await sleep(300000); // 等待5分钟冷却
    const coldReport = await runPrecisionTest(model);
    
    return { hotReport, coldReport };
  }
}

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通过本方案可实现：

1. ​​量化​​ NPU与CPU结果差异
2. ​​定位​​ 误差敏感网络层
3. ​​验证​​ 不同精度模式影响
4. ​​保障​​ 车载/医疗等高精度场景