text: // Copyright 2020 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 <array>
  #include <cmath>
  #include <functional>
  #include <limits>
  #include <random>
  #include <vector>
  
  #include <xnnpack.h>
  
  #include <benchmark/benchmark.h>
  #include "bench/utils.h"
  #ifdef BENCHMARK_TENSORFLOW_LITE
  #include "flatbuffers/include/flatbuffers/flatbuffers.h"
  #include "tensorflow/lite/interpreter.h"
  #include "tensorflow/lite/kernels/register.h"
  #include "tensorflow/lite/model.h"
  #include "tensorflow/lite/schema/schema_generated.h"
  #include "tensorflow/lite/version.h"
  #endif  // BENCHMARK_TENSORFLOW_LITE
  
  
  static void xnnpack_square_root_f32(benchmark::State& state) {
    const size_t batch_size = state.range(0);
    const size_t channels = state.range(1);
  
    std::random_device random_device;
    auto rng = std::mt19937(random_device());
    auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 5.0f), std::ref(rng));
  
    std::vector<float> input(batch_size * channels);
    std::vector<float> output(batch_size * channels);
    std::generate(input.begin(), input.end(), std::ref(f32rng));
    std::fill(output.begin(), output.end(), std::nanf(""));
  
    xnn_status status = xnn_initialize(nullptr /* allocator */);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to initialize XNNPACK");
      return;
    }
  
    xnn_operator_t sqrt_op = nullptr;
    status = xnn_create_square_root_nc_f32(
      channels, channels /* input stride */, channels /* output stride */,
      0 /* flags */, &sqrt_op);
    if (status != xnn_status_success || sqrt_op == nullptr) {
      state.SkipWithError("failed to create Square Root operator");
      return;
    }
  
    status = xnn_setup_square_root_nc_f32(
      sqrt_op,
      batch_size,
      input.data(), output.data(),
      nullptr /* thread pool */);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to setup Square Root operator");
      return;
    }
  
    for (auto _ : state) {
      status = xnn_run_operator(sqrt_op, nullptr /* thread pool */);
      if (status != xnn_status_success) {
        state.SkipWithError("failed to run Square Root operator");
        return;
      }
    }
  
    status = xnn_delete_operator(sqrt_op);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to delete Square Root operator");
      return;
    }
  
    const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
    if (cpu_frequency != 0) {
      state.counters["cpufreq"] = cpu_frequency;
    }
  
    const size_t elements_per_iteration = batch_size * channels;
    state.counters["elements"] =
      benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
  
    const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
    state.counters["bytes"] =
      benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
  }
  
  #ifdef BENCHMARK_TENSORFLOW_LITE
  static void tflite_square_root_f32(benchmark::State& state) {
    const size_t batch_size = state.range(0);
    const size_t channels = state.range(1);
  
    std::random_device random_device;
    auto rng = std::mt19937(random_device());
    auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 5.0f), std::ref(rng));
  
    flatbuffers::FlatBufferBuilder builder;
    const flatbuffers::Offset<tflite::OperatorCode> operator_code =
        CreateOperatorCode(builder, tflite::BuiltinOperator_SQRT);
  
    const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{
      tflite::CreateBuffer(builder, builder.CreateVector({})),
    }};
  
    const std::array<int32_t, 4> input_shape{{
      static_cast<int32_t>(batch_size),
      static_cast<int32_t>(1 /* height */),
      static_cast<int32_t>(1 /* width */),
      static_cast<int32_t>(channels)
    }};
    const std::array<int32_t, 4> output_shape{{
      static_cast<int32_t>(batch_size),
      static_cast<int32_t>(1 /* height */),
      static_cast<int32_t>(1 /* width */),
      static_cast<int32_t>(channels)
    }};
  
    const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{
      tflite::CreateTensor(builder,
                           builder.CreateVector<int32_t>(input_shape.data(), input_shape.size()),
                           tflite::TensorType_FLOAT32),
      tflite::CreateTensor(builder,
                           builder.CreateVector<int32_t>(output_shape.data(), output_shape.size()),
                           tflite::TensorType_FLOAT32),
    }};
  
    const std::array<int32_t, 1> op_inputs{{ 0 }};
    const std::array<int32_t, 1> op_outputs{{ 1 }};
    flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(
        builder,
        0 /* opcode_index */,
        builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
        builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()));
  
    const std::array<int32_t, 1> graph_inputs{{ 0 }};
    const std::array<int32_t, 1> graph_outputs{{ 1 }};
    const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph(
        builder,
        builder.CreateVector(tensors.data(), tensors.size()),
        builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()),
        builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()),
        builder.CreateVector(&op, 1));
  
    const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
        TFLITE_SCHEMA_VERSION,
        builder.CreateVector(&operator_code, 1),
        builder.CreateVector(&subgraph, 1),
        builder.CreateString("Square Root model"),
        builder.CreateVector(buffers.data(), buffers.size()));
  
    builder.Finish(model_buffer);
  
    const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
    tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver;
    tflite::InterpreterBuilder interpreterBuilder(model, resolver);
    std::unique_ptr<tflite::Interpreter> interpreter;
    if (interpreterBuilder(&interpreter) != kTfLiteOk) {
      state.SkipWithError("failed to create TFLite interpreter");
      return;
    }
    if (interpreter == nullptr) {
      state.SkipWithError("TFLite interpreter is null");
      return;
    }
    interpreter->SetNumThreads(1);
  
    if (interpreter->AllocateTensors() != kTfLiteOk) {
      state.SkipWithError("failed to allocate tensors");
      return;
    }
  
    std::generate(
      interpreter->typed_tensor<float>(0),
      interpreter->typed_tensor<float>(0) + batch_size * channels,
      std::ref(f32rng));
  
    for (auto _ : state) {
      if (interpreter->Invoke() != kTfLiteOk) {
        state.SkipWithError("failed to invoke TFLite interpreter");
        return;
      }
    }
  
    const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
    if (cpu_frequency != 0) {
      state.counters["cpufreq"] = cpu_frequency;
    }
  
    const size_t elements_per_iteration = batch_size * channels;
    state.counters["elements"] =
      benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
  
    const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
    state.counters["bytes"] =
      benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
  
    interpreter.reset();
  }
  #endif  // BENCHMARK_TENSORFLOW_LITE
  
  static void CharacteristicArguments(benchmark::internal::Benchmark* b)
  {
    b->ArgNames({"N", "C"});
  
    int32_t c = 16;
    for (int32_t n = 224; n >= 7; n /= 2) {
      b->Args({n * n, c});
      c *= 2;
    }
  }
  
  BENCHMARK(xnnpack_square_root_f32)->Apply(CharacteristicArguments)->UseRealTime();
  
  #ifdef BENCHMARK_TENSORFLOW_LITE
    BENCHMARK(tflite_square_root_f32)->Apply(CharacteristicArguments)->UseRealTime();
  #endif  // BENCHMARK_TENSORFLOW_LITE
  
  #ifndef XNNPACK_BENCHMARK_NO_MAIN
  BENCHMARK_MAIN();
  #endif
  