VAddMicrokernelTester Class — pytorch Architecture

Source Code

aten/src/ATen/native/quantized/cpu/qnnpack/test/vadd-microkernel-tester.h lines 22–224

class VAddMicrokernelTester {
 public:
  inline VAddMicrokernelTester& n(size_t n) {
    assert(n != 0);
    this->n_ = n;
    return *this;
  }

  inline size_t n() const {
    return this->n_;
  }

  inline VAddMicrokernelTester& inplaceA(bool inplaceA) {
    this->inplaceA_ = inplaceA;
    return *this;
  }

  inline bool inplaceA() const {
    return this->inplaceA_;
  }

  inline VAddMicrokernelTester& inplaceB(bool inplaceB) {
    this->inplaceB_ = inplaceB;
    return *this;
  }

  inline bool inplaceB() const {
    return this->inplaceB_;
  }

  inline VAddMicrokernelTester& aScale(float aScale) {
    assert(aScale > 0.0f);
    assert(std::isnormal(aScale));
    this->aScale_ = aScale;
    return *this;
  }

  inline float aScale() const {
    return this->aScale_;
  }

  inline VAddMicrokernelTester& aZeroPoint(uint8_t aZeroPoint) {
    this->aZeroPoint_ = aZeroPoint;
    return *this;
  }

  inline uint8_t aZeroPoint() const {
    return this->aZeroPoint_;
  }

  inline VAddMicrokernelTester& bScale(float bScale) {
    assert(bScale > 0.0f);
    assert(std::isnormal(bScale));
    this->bScale_ = bScale;
    return *this;
  }

  inline float bScale() const {
    return this->bScale_;
  }

  inline VAddMicrokernelTester& bZeroPoint(uint8_t bZeroPoint) {
    this->bZeroPoint_ = bZeroPoint;
    return *this;
  }

  inline uint8_t bZeroPoint() const {
    return this->bZeroPoint_;
  }

  inline VAddMicrokernelTester& yScale(float yScale) {
    assert(yScale > 0.0f);
    assert(std::isnormal(yScale));
    this->yScale_ = yScale;
    return *this;
  }

  inline float yScale() const {
    return this->yScale_;
  }

  inline VAddMicrokernelTester& yZeroPoint(uint8_t yZeroPoint) {
    this->yZeroPoint_ = yZeroPoint;
    return *this;
  }

  inline uint8_t yZeroPoint() const {
    return this->yZeroPoint_;
  }

  inline VAddMicrokernelTester& qmin(uint8_t qmin) {
    this->qmin_ = qmin;
    return *this;
  }

  inline uint8_t qmin() const {
    return this->qmin_;
  }

  inline VAddMicrokernelTester& qmax(uint8_t qmax) {
    this->qmax_ = qmax;
    return *this;
  }

  inline uint8_t qmax() const {
    return this->qmax_;
  }

  inline VAddMicrokernelTester& iterations(size_t iterations) {
    this->iterations_ = iterations;
    return *this;
  }

  inline size_t iterations() const {
    return this->iterations_;
  }

  void test(pytorch_q8vadd_ukernel_function q8vadd) const {
    std::random_device randomDevice;
    auto rng = std::mt19937(randomDevice());
    auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

    std::vector<uint8_t> a(n());
    std::vector<uint8_t> b(n());
    std::vector<uint8_t> y(n());
    std::vector<float> yFP(n());
    std::vector<uint8_t> yRef(n());
    for (size_t iteration = 0; iteration < iterations(); iteration++) {
      std::generate(a.begin(), a.end(), std::ref(u8rng));
      std::generate(b.begin(), b.end(), std::ref(u8rng));
      if (inplaceA() || inplaceB()) {
        std::generate(y.begin(), y.end(), std::ref(u8rng));
      } else {
        std::fill(y.begin(), y.end(), 0xA5);
      }
      const uint8_t* aData = inplaceA() ? y.data() : a.data();
      const uint8_t* bData = inplaceB() ? y.data() : b.data();

      /* Prepare quantization parameters */
      const union pytorch_qnnp_add_quantization_params quantizationParams =
          pytorch_qnnp_compute_add_quantization_params(
              aZeroPoint(),
              bZeroPoint(),
              yZeroPoint(),
              aScale() / yScale(),
              bScale() / yScale(),
              qmin(),
              qmax());
      const union pytorch_qnnp_add_quantization_params
          scalarQuantizationParams =
              pytorch_qnnp_compute_scalar_add_quantization_params(
                  aZeroPoint(),
                  bZeroPoint(),
                  yZeroPoint(),
                  aScale() / yScale(),
                  bScale() / yScale(),
                  qmin(),
                  qmax());

      /* Compute reference results */
      for (size_t i = 0; i < n(); i++) {
        yFP[i] = float(yZeroPoint()) +
            float(int32_t(aData[i]) - int32_t(aZeroPoint())) *
                (aScale() / yScale()) +
            float(int32_t(bData[i]) - int32_t(bZeroPoint())) *
                (bScale() / yScale());
        yFP[i] = std::min<float>(yFP[i], float(qmax()));
        yFP[i] = std::max<float>(yFP[i], float(qmin()));
        yRef[i] = pytorch_qnnp_add_quantize(
            aData[i], bData[i], scalarQuantizationParams);
      }

      /* Call optimized micro-kernel */
      q8vadd(n(), aData, bData, y.data(), &quantizationParams);

      /* Verify results */
      for (size_t i = 0; i < n(); i++) {
        ASSERT_LE(uint32_t(y[i]), uint32_t(qmax()))
            << "at " << i << ", n = " << n();
        ASSERT_GE(uint32_t(y[i]), uint32_t(qmin()))
            << "at " << i << ", n = " << n();
        ASSERT_NEAR(float(int32_t(y[i])), yFP[i], 0.6f)
            << "at " << i << ", n = " << n();
        ASSERT_EQ(uint32_t(yRef[i]), uint32_t(y[i]))
            << "at " << i << ", n = " << n();
      }
    }
  }

 private:
  size_t n_{1};
  bool inplaceA_{false};
  bool inplaceB_{false};
  float aScale_{0.75f};
  float bScale_{1.25f};
  float yScale_{0.96875f};
  uint8_t aZeroPoint_{121};
  uint8_t bZeroPoint_{127};
  uint8_t yZeroPoint_{133};
  uint8_t qmin_{0};
  uint8_t qmax_{255};
  size_t iterations_{15};
};