// Benchmarks for Eigen Tensor shuffling (transpose / permutation).
// SPDX-FileCopyrightText: The Eigen Authors
// SPDX-License-Identifier: MPL-2.0

#define EIGEN_USE_THREADS

#include <benchmark/benchmark.h>
#include <unsupported/Eigen/Tensor>
#include <unsupported/Eigen/ThreadPool>

using namespace Eigen;

typedef float Scalar;

// --- Rank-2 transpose ---
static void BM_Shuffle2D(benchmark::State& state) {
  const int M = state.range(0);
  const int N = state.range(1);

  Tensor<Scalar, 2> A(M, N);
  Tensor<Scalar, 2> B(N, M);
  A.setRandom();

  Eigen::array<int, 2> perm = {1, 0};

  for (auto _ : state) {
    B = A.shuffle(perm);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 2);
}

// --- Identity shuffle (no permutation, measures overhead) ---
static void BM_ShuffleIdentity(benchmark::State& state) {
  const int M = state.range(0);
  const int N = state.range(1);

  Tensor<Scalar, 2> A(M, N);
  Tensor<Scalar, 2> B(M, N);
  A.setRandom();

  Eigen::array<int, 2> perm = {0, 1};

  for (auto _ : state) {
    B = A.shuffle(perm);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 2);
}

// --- Rank-3 permutation ---
static void BM_Shuffle3D(benchmark::State& state) {
  const int D0 = state.range(0);
  const int D1 = state.range(1);
  const int D2 = state.range(2);

  Tensor<Scalar, 3> A(D0, D1, D2);
  A.setRandom();

  // Permutation (2, 0, 1)
  Eigen::array<int, 3> perm = {2, 0, 1};

  for (auto _ : state) {
    Tensor<Scalar, 3> B = A.shuffle(perm);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * D0 * D1 * D2 * sizeof(Scalar) * 2);
}

// --- Rank-4 permutation (NCHW -> NHWC layout conversion) ---
static void BM_Shuffle4D_NCHW_to_NHWC(benchmark::State& state) {
  const int N = state.range(0);
  const int C = state.range(1);
  const int H = state.range(2);

  Tensor<Scalar, 4> A(N, C, H, H);
  A.setRandom();

  // NCHW -> NHWC: permute (0, 2, 3, 1)
  Eigen::array<int, 4> perm = {0, 2, 3, 1};

  for (auto _ : state) {
    Tensor<Scalar, 4> B = A.shuffle(perm);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * N * C * H * H * sizeof(Scalar) * 2);
}

// --- ThreadPool variants ---

static void BM_Shuffle2D_ThreadPool(benchmark::State& state) {
  const int M = state.range(0);
  const int N = state.range(1);
  const int threads = state.range(2);

  Tensor<Scalar, 2> A(M, N);
  Tensor<Scalar, 2> B(N, M);
  A.setRandom();

  ThreadPool tp(threads);
  ThreadPoolDevice dev(&tp, threads);

  Eigen::array<int, 2> perm = {1, 0};

  for (auto _ : state) {
    B.device(dev) = A.shuffle(perm);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 2);
  state.counters["threads"] = threads;
}

static void BM_Shuffle4D_NCHW_to_NHWC_ThreadPool(benchmark::State& state) {
  const int N = state.range(0);
  const int C = state.range(1);
  const int H = state.range(2);
  const int threads = state.range(3);

  Tensor<Scalar, 4> A(N, C, H, H);
  Tensor<Scalar, 4> B(N, H, H, C);
  A.setRandom();

  ThreadPool tp(threads);
  ThreadPoolDevice dev(&tp, threads);

  // NCHW -> NHWC: permute (0, 2, 3, 1)
  Eigen::array<int, 4> perm = {0, 2, 3, 1};

  for (auto _ : state) {
    B.device(dev) = A.shuffle(perm);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * N * C * H * H * sizeof(Scalar) * 2);
  state.counters["threads"] = threads;
}

// clang-format off
#define SHUFFLE_2D_SIZES \
  ->Args({256, 256})->Args({1024, 1024}) \
  ->Args({64, 4096})->Args({4096, 64})

#define SHUFFLE_3D_SIZES \
  ->Args({64, 64, 64})->Args({128, 128, 64})->Args({32, 256, 256})

// {batch, channels, h}: pure Cartesian product.
#define SHUFFLE_4D_SIZES ->ArgsProduct({{1, 8}, {3, 64}, {32, 64}})

#define SHUFFLE_2D_THREADPOOL_SIZES \
  ->Args({256, 256, 1})->Args({256, 256, 2})->Args({256, 256, 4}) \
  ->Args({256, 256, 8})->Args({256, 256, 12})->Args({256, 256, 16}) \
  ->Args({1024, 1024, 1})->Args({1024, 1024, 2})->Args({1024, 1024, 4}) \
  ->Args({1024, 1024, 8})->Args({1024, 1024, 12})->Args({1024, 1024, 16})

// {batch, channels, h, threads}: pure Cartesian product.
#define SHUFFLE_4D_THREADPOOL_SIZES ->ArgsProduct({{1, 8}, {64}, {32, 64}, {1, 2, 4, 8, 12, 16}})
// clang-format on

BENCHMARK(BM_Shuffle2D) SHUFFLE_2D_SIZES;
BENCHMARK(BM_ShuffleIdentity) SHUFFLE_2D_SIZES;
BENCHMARK(BM_Shuffle3D) SHUFFLE_3D_SIZES;
BENCHMARK(BM_Shuffle4D_NCHW_to_NHWC) SHUFFLE_4D_SIZES;
BENCHMARK(BM_Shuffle2D_ThreadPool) SHUFFLE_2D_THREADPOOL_SIZES->UseRealTime();
BENCHMARK(BM_Shuffle4D_NCHW_to_NHWC_ThreadPool) SHUFFLE_4D_THREADPOOL_SIZES->UseRealTime();