Q8GEMM Class — pytorch Architecture

Source Code

aten/src/ATen/native/quantized/cpu/qnnpack/bench/q8gemm.cc lines 79–217

class Q8GEMM : public benchmark::Fixture {
 public:
  inline Q8GEMM(uint32_t mr, uint32_t nr, uint32_t np, uint32_t kr)
      : mr_(mr), nr_(nr), np_(np), kr_(kr), mc_(mr), nc_(nr), kc_(kr) {}

   void SetUp(const benchmark::State&) override {
    std::random_device randomDevice;
    auto rng = std::mt19937(randomDevice());
    auto s32rng =
        std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
    auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

    a_.resize(mc() * kc());
    std::generate(a_.begin(), a_.end(), std::ref(u8rng));
    k_.resize(nc() * kc());
    std::generate(k_.begin(), k_.end(), std::ref(u8rng));
    b_.resize(nc());
    std::generate(b_.begin(), b_.end(), std::ref(s32rng));
    w_.resize(
        kcStride() * ncStride() +
        ncStride() * sizeof(int32_t) / sizeof(uint8_t));
    std::fill(w_.begin(), w_.end(), 127);
    size_t num_zero_points_kernel = (nc_ + (nr_ -1)) & -nr_;
    std::vector<uint8_t> kernel_zero_points(num_zero_points_kernel, 127);
    std::vector<float> requantization_scales(num_zero_points_kernel, 0.75f);
    pytorch_pack_q8gemm_w(
        nc(),
        kc(),
        nr(),
        np(),
        kr(),
#if !PYTORCH_QNNPACK_RUNTIME_QUANTIZATION
        127,
        127,
#endif
        k(),
        b(),
#if PYTORCH_QNNPACK_RUNTIME_QUANTIZATION
        kernel_zero_points.data(),
#endif
        w());
    c_.resize(mc() * nc());
    std::fill(c_.begin(), c_.end(), 0xA5);

    quantizationParams_ = pytorch_qnnp_compute_conv_quantization_params(
        127, kernel_zero_points.data(),
        requantization_scales.data(), 127, 1, 254);
  }

   void TearDown(benchmark::State& state) override {
    state.SetItemsProcessed(
        uint64_t(state.iterations()) * 2 * mc() * nc() * kc());
    a_.clear();
    k_.clear();
    b_.clear();
    w_.clear();
    c_.clear();
  }

  inline const uint8_t* a() const {
    return a_.data();
  }

  inline const uint8_t* k() const {
    return k_.data();
  }

  inline const int32_t* b() const {
    return b_.data();
  }

  inline uint8_t* w() {
    return w_.data();
  }

  inline const uint8_t* w() const {
    return w_.data();
  }

  inline uint8_t* c() {
    return c_.data();
  }

  inline uint32_t mr() const {
    return mr_;
  }

  inline uint32_t mc() const {
    return mc_;
  }

  inline uint32_t nr() const {
    return nr_;
  }

  inline uint32_t np() const {
    return np_;
  }

  inline uint32_t nc() const {
    return nc_;
  }

  inline uint32_t ncStride() const {
    return roundUp(nc(), nr());
  }

  inline uint32_t kr() const {
    return kr_;
  }

  inline uint32_t kc() const {
    return kc_;
  }

  inline uint32_t kcStride() const {
    return roundUp(kc(), kr());
  }

  inline const pytorch_qnnp_conv_quantization_params* quantizationParams()
      const {
    return &quantizationParams_;
  }

 protected:
  std::vector<uint8_t> a_;
  std::vector<uint8_t> k_;
  std::vector<int32_t> b_;
  std::vector<uint8_t, AlignedAllocator<uint8_t, 32>> w_;
  std::vector<uint8_t> c_;
  uint32_t mr_{0};
  uint32_t nr_{0};
  uint32_t np_{0};
  uint32_t kr_{0};
  uint32_t mc_{mr_};
  uint32_t nc_{nr_};
  uint32_t kc_{kr_};
  pytorch_qnnp_conv_quantization_params quantizationParams_;
};