// Benchmarks for Eigen TensorRoll.
// SPDX-FileCopyrightText: The Eigen Authors
// SPDX-License-Identifier: MPL-2.0

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

using namespace Eigen;

typedef float Scalar;

// --- Roll only the inner-most (contiguous) dimension. ---
static void BM_Roll_Inner(benchmark::State& state) {
  const int M = state.range(0);
  const int N = state.range(1);
  const int shift = state.range(2);

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

  array<Index, 2> rolls = {shift, 0};

  for (auto _ : state) {
    Tensor<Scalar, 2> B = A.roll(rolls);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * static_cast<int64_t>(M) * N * sizeof(Scalar));
}

// --- Roll only an outer dimension. Inner dim stays contiguous. ---
static void BM_Roll_Outer(benchmark::State& state) {
  const int M = state.range(0);
  const int N = state.range(1);
  const int shift = state.range(2);

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

  array<Index, 2> rolls = {0, shift};

  for (auto _ : state) {
    Tensor<Scalar, 2> B = A.roll(rolls);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * static_cast<int64_t>(M) * N * sizeof(Scalar));
}

// --- Roll every dimension. ---
static void BM_Roll_All(benchmark::State& state) {
  const int M = state.range(0);
  const int N = state.range(1);
  const int shift = state.range(2);

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

  array<Index, 2> rolls = {shift, shift};

  for (auto _ : state) {
    Tensor<Scalar, 2> B = A.roll(rolls);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * static_cast<int64_t>(M) * N * sizeof(Scalar));
}

// --- 3D roll with the inner dim shifted. ---
static void BM_Roll_3D_Inner(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();

  array<Index, 3> rolls = {D0 / 4, 0, 0};

  for (auto _ : state) {
    Tensor<Scalar, 3> B = A.roll(rolls);
    benchmark::DoNotOptimize(B.data());
    benchmark::ClobberMemory();
  }
  state.SetBytesProcessed(state.iterations() * static_cast<int64_t>(D0) * D1 * D2 * sizeof(Scalar));
}

// clang-format off
#define ROLL_SIZES \
  ->Args({64, 64, 1})->Args({64, 64, 13}) \
  ->Args({256, 256, 1})->Args({256, 256, 13}) \
  ->Args({1024, 1024, 1})->Args({1024, 1024, 13})

#define ROLL_3D_SIZES \
  ->Args({32, 32, 32})->Args({64, 64, 64})->Args({128, 128, 128})
// clang-format on

BENCHMARK(BM_Roll_Inner) ROLL_SIZES;
BENCHMARK(BM_Roll_Outer) ROLL_SIZES;
BENCHMARK(BM_Roll_All) ROLL_SIZES;
BENCHMARK(BM_Roll_3D_Inner) ROLL_3D_SIZES;