text: // Copyright (c) Facebook, Inc. and its affiliates.
  // All rights reserved.
  //
  // 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 <limits>
  #include <random>
  #include <vector>
  
  #include <xnnpack.h>
  
  #include <benchmark/benchmark.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
  #include "bench/utils.h"
  
  #ifndef XNN_NO_QU8_OPERATORS
  static void xnnpack_average_pooling_qu8(benchmark::State& state, const char* net) {
    const size_t batch_size = state.range(0);
    const size_t input_height = state.range(1);
    const size_t input_width = state.range(2);
    const size_t pooling_size = state.range(3);
    const size_t padding_size = state.range(4);
    const size_t stride = state.range(5);
    const size_t channels = state.range(6);
  
    std::random_device random_device;
    auto rng = std::mt19937(random_device());
    auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
  
    const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
    const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
  
    std::vector<uint8_t> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(uint8_t));
    std::generate(input.begin(), input.end(), std::ref(u8rng));
    std::vector<uint8_t> output(batch_size * output_height * output_width * channels);
    std::fill(output.begin(), output.end(), 0xA5);
  
    xnn_status status = xnn_initialize(nullptr /* allocator */);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to initialize XNNPACK");
      return;
    }
  
    xnn_operator_t pooling_op = nullptr;
    status = xnn_create_average_pooling2d_nhwc_qu8(
      padding_size, padding_size, padding_size, padding_size,
      pooling_size, pooling_size,
      stride, stride,
      channels, channels /* input pixel stride */, channels /* output pixel stride */,
      127 /* input zero point */, 0.75f /* input scale */,
      127 /* output zero point */, 1.25f /* output scale */,
      0, 255,
      0 /* flags */, &pooling_op);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to create Average Pooling operator");
      return;
    }
  
    status = xnn_setup_average_pooling2d_nhwc_qu8(
      pooling_op,
      batch_size, input_height, input_width,
      input.data(), output.data(),
      nullptr /* thread pool */);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to setup Average Pooling operator");
      return;
    }
  
    for (auto _ : state) {
      status = xnn_run_operator(pooling_op, nullptr /* thread pool */);
      if (status != xnn_status_success) {
        state.SkipWithError("failed to run Average Pooling operator");
        return;
      }
    }
  
    status = xnn_delete_operator(pooling_op);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to delete Average Pooling operator");
      return;
    }
    pooling_op = nullptr;
  
    const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
    if (cpu_frequency != 0) {
      state.counters["cpufreq"] = cpu_frequency;
    }
  
    state.counters["bytes"] = benchmark::Counter(
      uint64_t(state.iterations()) *
        batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(uint8_t),
      benchmark::Counter::kIsRate);
  }
  #endif  // XNN_NO_QU8_OPERATORS
  
  static void xnnpack_average_pooling_f32(benchmark::State& state, const char* net) {
    const size_t batch_size = state.range(0);
    const size_t input_height = state.range(1);
    const size_t input_width = state.range(2);
    const size_t pooling_size = state.range(3);
    const size_t padding_size = state.range(4);
    const size_t stride = state.range(5);
    const size_t channels = state.range(6);
  
    std::random_device random_device;
    auto rng = std::mt19937(random_device());
    auto f32rng = std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
  
    const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
    const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
  
    std::vector<float> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(float));
    std::generate(input.begin(), input.end(), std::ref(f32rng));
    std::vector<float> output(batch_size * output_height * output_width * channels);
    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 pooling_op = nullptr;
    status = xnn_create_average_pooling2d_nhwc_f32(
      padding_size, padding_size, padding_size, padding_size,
      pooling_size, pooling_size,
      stride, stride,
      channels, channels /* input pixel stride */, channels /* output pixel stride */,
      -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
      0 /* flags */, &pooling_op);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to create Average Pooling operator");
      return;
    }
  
    status = xnn_setup_average_pooling2d_nhwc_f32(
      pooling_op,
      batch_size, input_height, input_width,
      input.data(), output.data(),
      nullptr /* thread pool */);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to setup Average Pooling operator");
      return;
    }
  
    for (auto _ : state) {
      status = xnn_run_operator(pooling_op, nullptr /* thread pool */);
      if (status != xnn_status_success) {
        state.SkipWithError("failed to run Average Pooling operator");
        return;
      }
    }
  
    status = xnn_delete_operator(pooling_op);
    if (status != xnn_status_success) {
      state.SkipWithError("failed to delete Average Pooling operator");
      return;
    }
    pooling_op = nullptr;
  
    const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
    if (cpu_frequency != 0) {
      state.counters["cpufreq"] = cpu_frequency;
    }
  
    state.counters["bytes"] = benchmark::Counter(
      uint64_t(state.iterations()) *
        batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float),
      benchmark::Counter::kIsRate);
  }
  
  #ifdef BENCHMARK_TENSORFLOW_LITE
  void tflite_average_pooling_f32(benchmark::State& state, const char* net) {
    const size_t batch_size = state.range(0);
    const size_t input_height = state.range(1);
    const size_t input_width = state.range(2);
    const size_t pooling_size = state.range(3);
    const size_t padding_size = state.range(4);
    const size_t stride = state.range(5);
    const size_t channels = state.range(6);
  
    std::random_device random_device;
    auto rng = std::mt19937(random_device());
    auto f32rng = std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
  
    tflite::Padding padding = tflite::Padding_VALID;
    if (2 * padding_size == (pooling_size - 1)) {
      padding = tflite::Padding_SAME;
    } else if (padding_size == 0) {
      padding = tflite::Padding_VALID;
    } else {
      state.SkipWithError("unsupported padding");
      return;
    }
  
    const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
    const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
  
    std::vector<float> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(float));
    std::generate(input.begin(), input.end(), std::ref(f32rng));
    std::vector<float> output(batch_size * output_height * output_width * channels);
    std::fill(output.begin(), output.end(), std::nanf(""));
  
    flatbuffers::FlatBufferBuilder builder;
    flatbuffers::Offset<tflite::OperatorCode> operator_code =
        CreateOperatorCode(builder, tflite::BuiltinOperator_AVERAGE_POOL_2D);
  
    flatbuffers::Offset<tflite::Pool2DOptions> pool2d_options = CreatePool2DOptions(
        builder, padding,
        stride /* stride_w */, stride /* stride_h */,
        pooling_size /* filter_width */, pooling_size /* filter_height */,
        tflite::ActivationFunctionType_NONE);
  
    flatbuffers::Offset<tflite::Buffer> buffers[1] = {
      tflite::CreateBuffer(builder, builder.CreateVector({})),
    };
  
    const int32_t input_shape[4] = {
      static_cast<int32_t>(batch_size),
      static_cast<int32_t>(input_height),
      static_cast<int32_t>(input_width),
      static_cast<int32_t>(channels)
    };
    const int32_t output_shape[4] = {
      static_cast<int32_t>(batch_size),
      static_cast<int32_t>(output_height),
      static_cast<int32_t>(output_width),
      static_cast<int32_t>(channels)
    };
  
    flatbuffers::Offset<tflite::Tensor> tensors[2] = {
      tflite::CreateTensor(builder,
                           builder.CreateVector<int32_t>(input_shape, 4),
                           tflite::TensorType_FLOAT32),
      tflite::CreateTensor(builder,
                           builder.CreateVector<int32_t>(output_shape, 4),
                           tflite::TensorType_FLOAT32),
    };
  
    const int32_t op_inputs[1] = { 0 };
    const int32_t op_outputs[1] = { 1 };
    flatbuffers::Offset<tflite::Operator> op = CreateOperator(
        builder,
        0 /* opcode_index */,
        builder.CreateVector<int32_t>(op_inputs, 1),
        builder.CreateVector<int32_t>(op_outputs, 1),
        tflite::BuiltinOptions_Pool2DOptions,
        pool2d_options.Union());
  
    const int32_t graph_inputs[1] = { 0 };
    const int32_t graph_outputs[1] = { 1 };
    flatbuffers::Offset<tflite::SubGraph> subgraph = CreateSubGraph(
        builder,
        builder.CreateVector(tensors, 2),
        builder.CreateVector<int32_t>(graph_inputs, 1),
        builder.CreateVector<int32_t>(graph_outputs, 1),
        builder.CreateVector(&op, 1));
  
    flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
        TFLITE_SCHEMA_VERSION,
        builder.CreateVector(&operator_code, 1),
        builder.CreateVector(&subgraph, 1),
        builder.CreateString("AVERAGE_POOL_2D model"),
        builder.CreateVector(buffers, 1));
  
    builder.Finish(model_buffer);
  
    const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
    tflite::ops::builtin::BuiltinOpResolver 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 * input_height * input_width * 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;
    }
  
    state.counters["bytes"] = benchmark::Counter(
      uint64_t(state.iterations()) *
        batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float),
      benchmark::Counter::kIsRate);
  }
  #endif  // BENCHMARK_TENSORFLOW_LITE
  
  // Final global average pooling in ImageNet classification models.
  static void ImageNet(benchmark::internal::Benchmark* b) {
    b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
  
    /*       N   H   W   K  P  S   C */
    b->Args({1, 13, 13, 13, 0, 1, 1000});
    b->Args({1,  7,  7,  7, 0, 1, 1000});
  }
  
  // ShuffleNet v1 with 1 group.
  static void ShuffleNetV1G1(benchmark::internal::Benchmark* b) {
    b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
  
    /*       N   H   W  K  P  S   C */
    b->Args({1, 56, 56, 3, 1, 2,  24});
    b->Args({1, 28, 28, 3, 1, 2, 144});
    b->Args({1, 14, 14, 3, 1, 2, 288});
    b->Args({1,  7,  7, 3, 1, 2, 576});
  }
  
  // ShuffleNet v1 with 2 groups.
  static void ShuffleNetV1G2(benchmark::internal::Benchmark* b) {
    b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
  
    /*       N   H   W  K  P  S   C */
    b->Args({1, 56, 56, 3, 1, 2,  24});
    b->Args({1, 28, 28, 3, 1, 2, 200});
    b->Args({1, 14, 14, 3, 1, 2, 400});
    b->Args({1,  7,  7, 3, 1, 2, 800});
  }
  
  // ShuffleNet v1 with 3 groups.
  static void ShuffleNetV1G3(benchmark::internal::Benchmark* b) {
    b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
  
    /*       N   H   W  K  P  S   C */
    b->Args({1, 56, 56, 3, 1, 2,  24});
    b->Args({1, 28, 28, 3, 1, 2, 240});
    b->Args({1, 14, 14, 3, 1, 2, 480});
    b->Args({1,  7,  7, 3, 1, 2, 960});
  }
  
  // ShuffleNet v1 with 4 groups.
  static void ShuffleNetV1G4(benchmark::internal::Benchmark* b) {
    b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
  
    /*       N   H   W  K  P  S    C */
    b->Args({1, 56, 56, 3, 1, 2,   24});
    b->Args({1, 28, 28, 3, 1, 2,  272});
    b->Args({1, 14, 14, 3, 1, 2,  576});
    b->Args({1,  7,  7, 3, 1, 2, 1088});
  }
  
  // ShuffleNet v1 with 8 groups.
  static void ShuffleNetV1G8(benchmark::internal::Benchmark* b) {
    b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
  
    /*       N   H   W  K  P  S    C */
    b->Args({1, 56, 56, 3, 1, 2,   24});
    b->Args({1, 28, 28, 3, 1, 2,  384});
    b->Args({1, 14, 14, 3, 1, 2,  768});
    b->Args({1,  7,  7, 3, 1, 2, 1536});
  }
  
  BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
  
  #ifdef BENCHMARK_TENSORFLOW_LITE
  BENCHMARK_CAPTURE(tflite_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
  BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
  BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
  BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
  BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
  BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
  #endif  // BENCHMARK_TENSORFLOW_LITE
  
  #ifndef XNN_NO_QU8_OPERATORS
  BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
  BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
  #endif  // XNN_NO_QU8_OPERATORS
  
  #ifndef XNNPACK_BENCHMARK_NO_MAIN
  BENCHMARK_MAIN();
  #endif
  