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/*! \file
    \brief Unit tests for conversion operators.
*/

#include "../common/cutlass_unit_test.h"

#include "cutlass/numeric_conversion.h"

#include "cutlass/layout/matrix.h"
#include "cutlass/util/host_tensor.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace test {
namespace core {
namespace kernel {

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Conversion template
template <typename Destination, typename Source, int Count>
__global__ void convert(
  cutlass::Array<Destination, Count> *destination, 
  cutlass::Array<Source, Count> const *source) {

  cutlass::NumericArrayConverter<Destination, Source, Count> convert;

  *destination = convert(*source);
}

/////////////////////////////////////////////////////////////////////////////////////////////////

} // namespace kernel
} // namespace core
} // namespace test

/////////////////////////////////////////////////////////////////////////////////////////////////

TEST(NumericConversion, f32_to_f16_rn) {

  int const kN = 1;
  using Source = float;
  using Destination = cutlass::half_t;

  dim3 grid(1, 1);
  dim3 block(1, 1);

  cutlass::HostTensor<cutlass::half_t, cutlass::layout::RowMajor> destination({1, kN});
  cutlass::HostTensor<float, cutlass::layout::RowMajor> source({1, kN});

  for (int i = 0; i < kN; ++i) {
    source.host_data()[i] = float(i);
  }

  source.sync_device();

  test::core::kernel::convert<Destination, Source, 1><<< grid, block >>>(
    reinterpret_cast<cutlass::Array<Destination, 1> *>(destination.device_data()),
    reinterpret_cast<cutlass::Array<Source, 1> const *>(source.device_data())
  );

  destination.sync_host();

  for (int i = 0; i < kN; ++i) {
    EXPECT_TRUE(float(destination.host_data()[i]) == source.host_data()[i]);
  }
}

TEST(NumericConversion, f32x8_to_f16x8_rn) {

  int const kN = 8;
  using Source = float;
  using Destination = cutlass::half_t;

  dim3 grid(1, 1);
  dim3 block(1, 1);

  cutlass::HostTensor<Destination, cutlass::layout::RowMajor> destination({1, kN});
  cutlass::HostTensor<Source, cutlass::layout::RowMajor> source({1, kN});

  for (int i = 0; i < kN; ++i) {
    source.host_data()[i] = float(i);
  }

  source.sync_device();

  test::core::kernel::convert<Destination, Source, kN><<< grid, block >>>(
    reinterpret_cast<cutlass::Array<Destination, kN> *>(destination.device_data()),
    reinterpret_cast<cutlass::Array<Source, kN> const *>(source.device_data())
  );

  destination.sync_host();

  for (int i = 0; i < kN; ++i) {
    EXPECT_TRUE(float(destination.host_data()[i]) == source.host_data()[i]);
  }
}

/////////////////////////////////////////////////////////////////////////////////////////////////

TEST(NumericConversion, f16_to_f32_rn) {
  
  int const kN = 1;
  using Source = cutlass::half_t;
  using Destination = float;

  dim3 grid(1, 1);
  dim3 block(1, 1);

  cutlass::HostTensor<float, cutlass::layout::RowMajor> destination({1, kN});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::RowMajor> source({1, kN});

  for (int i = 0; i < kN; ++i) {
    source.host_data()[i] = Source(i);
  }

  source.sync_device();

  test::core::kernel::convert<Destination, Source, kN><<< grid, block >>>(
    reinterpret_cast<cutlass::Array<Destination, kN> *>(destination.device_data()),
    reinterpret_cast<cutlass::Array<Source, kN> const *>(source.device_data())
  );

  destination.sync_host();

  for (int i = 0; i < kN; ++i) {
    EXPECT_TRUE(float(destination.host_data()[i]) == float(source.host_data()[i]));
  }
}

TEST(NumericConversion, f16x8_to_f32x8_rn) {

  int const kN = 8;
  using Source = cutlass::half_t;
  using Destination = float;

  dim3 grid(1, 1);
  dim3 block(1, 1);

  cutlass::HostTensor<float, cutlass::layout::RowMajor> destination({1, kN});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::RowMajor> source({1, kN});

  for (int i = 0; i < kN; ++i) {
    source.host_data()[i] = float(i);
  }

  source.sync_device();

  test::core::kernel::convert<Destination, Source, kN><<< grid, block >>>(
    reinterpret_cast<cutlass::Array<Destination, kN> *>(destination.device_data()),
    reinterpret_cast<cutlass::Array<Source, kN> const *>(source.device_data())
  );

  destination.sync_host();

  for (int i = 0; i < kN; ++i) {
    EXPECT_TRUE(float(destination.host_data()[i]) == float(source.host_data()[i]));
  }
}

/////////////////////////////////////////////////////////////////////////////////////////////////

TEST(NumericConversion, f32x8_to_s8x8_rn) {

  int const kN = 8;
  using Source = float;
  using Destination = int8_t;

  dim3 grid(1, 1);
  dim3 block(1, 1);

  cutlass::HostTensor<Destination, cutlass::layout::RowMajor> destination({1, kN});
  cutlass::HostTensor<Source, cutlass::layout::RowMajor> source({1, kN});

  for (int i = 0; i < kN; ++i) {
    source.host_data()[i] = float(i);
  }

  source.sync_device();

  test::core::kernel::convert<Destination, Source, kN><<< grid, block >>>(
    reinterpret_cast<cutlass::Array<Destination, kN> *>(destination.device_data()),
    reinterpret_cast<cutlass::Array<Source, kN> const *>(source.device_data())
  );

  destination.sync_host();

  for (int i = 0; i < kN; ++i) {
    EXPECT_TRUE(float(destination.host_data()[i]) == source.host_data()[i]);
  }
}

/////////////////////////////////////////////////////////////////////////////////////////////////
