text: // Copyright 2019 Google LLC
  //
  // This source code is licensed under the BSD-style license found in the
  // LICENSE file in the root directory of this source tree.
  
  #include <algorithm>
  #include <cfloat>
  #include <cmath>
  #include <functional>
  #include <random>
  #include <vector>
  
  #include <cpuinfo.h>
  
  #include <benchmark/benchmark.h>
  #include "bench/dconv.h"
  #include "bench/utils.h"
  #include <xnnpack/AlignedAllocator.h>
  #include <xnnpack/common.h>
  #include <xnnpack/conv.h>
  #include <xnnpack/pack.h>
  #include <xnnpack/params-init.h>
  #include <xnnpack/params.h>
  
  
  static void DConvHWC2CHW3X3S2P1Benchmark(benchmark::State& state,
    xnn_f32_conv_hwc2chw_ukernel_function conv,
    uint32_t output_channels_tile,
    benchmark::utils::IsaCheckFunction isa_check = nullptr)
  {
    if (isa_check && !isa_check(state)) {
      return;
    }
  
    const size_t input_height = state.range(0);
    const size_t input_width = state.range(1);
    const size_t output_channels = state.range(2);
  
    std::random_device random_device;
    auto rng = std::mt19937(random_device());
    auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng));
  
    const size_t input_channels = 3;
    const size_t kernel_size = 3;
    const size_t padding = 1;
    const size_t subsampling = 2;
  
    const size_t output_height = (input_height + 2 * padding - kernel_size) / subsampling + 1;
    const size_t output_width = (input_width + 2 * padding - kernel_size) / subsampling + 1;
  
    std::vector<float> input(input_height * input_width * input_channels + XNN_EXTRA_BYTES / sizeof(float));
    std::generate(input.begin(), input.end(), std::ref(f32rng));
    std::vector<float> kernel(output_channels * kernel_size * kernel_size * input_channels);
    std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
    std::vector<float> bias(output_channels);
    std::generate(bias.begin(), bias.end(), std::ref(f32rng));
  
    std::vector<float, AlignedAllocator<float, 64>> zero(input_channels * input_width + XNN_EXTRA_BYTES / sizeof(float));
  
    const size_t weights_elements = (kernel_size * kernel_size * input_channels + 1) *
      benchmark::utils::RoundUp<size_t>(output_channels, output_channels_tile);
    const size_t output_elements = output_height * output_width * output_channels;
    const size_t num_buffers = 1 +
      benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
        sizeof(float) * (weights_elements + output_elements));
  
    std::vector<float, AlignedAllocator<float, 64>> packed_weights(weights_elements * num_buffers);
    std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
    xnn_pack_f32_dconv_oki_w(
      output_channels, input_channels, output_channels_tile,
      kernel_size /* kernel height */, kernel_size /* kernel width */,
      kernel.data(), bias.data(), packed_weights.data(), NULL);
    for (size_t n = 1; n < num_buffers; n++) {
      std::copy(packed_weights.cbegin(),
        packed_weights.cbegin() + weights_elements,
        packed_weights.begin() + n * weights_elements);
    }
  
    std::vector<float> output(output_elements * num_buffers);
    std::fill(output.begin(), output.end(), std::nanf(""));
  
    xnn_f32_minmax_params params;
    xnn_init_f32_minmax_params(&params, -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());
  
    size_t buffer_index = 0;
    for (auto _ : state) {
      state.PauseTiming();
      benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
      buffer_index = (buffer_index + 1) % num_buffers;
      state.ResumeTiming();
  
      conv(
        input_height, input_width,
        0 /* output_y_start */, output_height /* output_y_end */,
        input.data(), zero.data(),
        packed_weights.data() + buffer_index * weights_elements,
        output.data() + buffer_index * output_elements,
        padding, output_channels,
        output_channels * output_width * sizeof(float),
        output_channels * sizeof(float),
        &params);
    }
  
    const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
    if (cpu_frequency != 0) {
      state.counters["cpufreq"] = cpu_frequency;
    }
  
    state.counters["FLOPS"] = benchmark::Counter(
      uint64_t(state.iterations()) * 2 *
        output_height * output_width *
        input_channels * output_channels *
        kernel_size * kernel_size,
      benchmark::Counter::kIsRate);
  }
  
  #if XNN_ARCH_ARM64
    static void f32_conv_hwc2chw_3x3s2p1c3x4__neonfma_2x2(benchmark::State& state, const char* net) {
      DConvHWC2CHW3X3S2P1Benchmark(state, xnn_f32_conv_hwc2chw_ukernel_3x3s2p1c3x4__neonfma_2x2, 4, benchmark::utils::CheckNEONFMA);
    }
  
    BENCHMARK_DCONV(f32_conv_hwc2chw_3x3s2p1c3x4__neonfma_2x2);
  #endif
  
  #if XNN_ARCH_X86 || XNN_ARCH_X86_64
    static void f32_conv_hwc2chw_3x3s2p1c3x4__sse_1x1(benchmark::State& state, const char* net) {
      DConvHWC2CHW3X3S2P1Benchmark(state, xnn_f32_conv_hwc2chw_ukernel_3x3s2p1c3x4__sse_1x1, 4);
    }
  
    BENCHMARK_DCONV(f32_conv_hwc2chw_3x3s2p1c3x4__sse_1x1);
  
    static void f32_conv_hwc2chw_3x3s2p1c3x4__sse_2x2(benchmark::State& state, const char* net) {
      DConvHWC2CHW3X3S2P1Benchmark(state, xnn_f32_conv_hwc2chw_ukernel_3x3s2p1c3x4__sse_2x2, 4);
    }
  
    BENCHMARK_DCONV(f32_conv_hwc2chw_3x3s2p1c3x4__sse_2x2);
  #endif
  
  #if XNN_ARCH_WASMSIMD || XNN_ARCH_WASMRELAXEDSIMD
    static void f32_conv_hwc2chw_3x3s2p1c3x4__wasmsimd_2x2(benchmark::State& state, const char* net) {
      DConvHWC2CHW3X3S2P1Benchmark(state, xnn_f32_conv_hwc2chw_ukernel_3x3s2p1c3x4__wasmsimd_2x2, 4);
    }
  
    BENCHMARK_DCONV(f32_conv_hwc2chw_3x3s2p1c3x4__wasmsimd_2x2);
  #endif  // XNN_ARCH_WASMSIMD || XNN_ARCH_WASMRELAXEDSIMD
  
  static void f32_conv_hwc2chw_3x3s2p1c3x4__scalar_1x1(benchmark::State& state, const char* net) {
    DConvHWC2CHW3X3S2P1Benchmark(state, xnn_f32_conv_hwc2chw_ukernel_3x3s2p1c3x4__scalar_1x1, 4);
  }
  
  BENCHMARK_DCONV(f32_conv_hwc2chw_3x3s2p1c3x4__scalar_1x1);
  
  #ifndef XNNPACK_BENCHMARK_NO_MAIN
  BENCHMARK_MAIN();
  #endif
  