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/*! \file
    \brief Unit tests for thread-level GEMM
*/
#include "cutlass/arch/wmma.h"

#ifdef CUTLASS_ARCH_WMMA_SM70_ENABLED

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

#include "cutlass/aligned_buffer.h"
#include "cutlass/half.h"

#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/gemm/warp/default_mma_wmma_tensor_op.h"
#include "cutlass/epilogue/threadblock/default_epilogue_wmma_tensor_op.h"

#include "cutlass/util/host_tensor.h"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/reference/host/tensor_fill.h"

#include "testbed.h"


/////////////////////////////////////////////////////////////////////////////////////////////////
//
// F16 acumulation
//
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(SM70_Epilogue_threadblock_epilogue, f16_wmma_tensor_op_64x64_64x64x16) {

  //
  // Define the warp-level matrix multiply
  //
  using ElementOutput = cutlass::half_t;
  using ElementAccumulator = cutlass::half_t;
  using ElementCompute = cutlass::half_t;
  int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
  int const kPartitionsK = 1;
  
  using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
  using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
  using InstructionShape = cutlass::gemm::GemmShape<16, 16, 16>;
  using ElementA = cutlass::half_t;
  using ElementB = cutlass::half_t;
  using ElementC = ElementAccumulator;
  using LayoutA = cutlass::layout::RowMajor;
  using LayoutB = cutlass::layout::ColumnMajor;
  using LayoutC = cutlass::layout::RowMajor;

  using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
      WarpShape, 
      InstructionShape, 
      ElementA, 
      LayoutA, 
      ElementB, 
      LayoutB, 
      ElementC,
      LayoutC>::Type;

  //
  // Output operator
  //

  using OutputOp = cutlass::epilogue::thread::LinearCombination<
    ElementOutput,
    kElementsPerAccess,
    ElementAccumulator,
    ElementCompute
  >;

  //
  // Define the epilogue
  //

  using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueWmmaTensorOp<
    Shape,
    WarpMmaTensorOp,
    kPartitionsK,
    OutputOp,
    kElementsPerAccess
  >::Epilogue;

  //
  // Instantiate epilogue
  //

  EpilogueTestbed<Epilogue> testbed;

  bool passed = testbed.run_all();

  EXPECT_TRUE(passed);

}

TEST(SM70_Epilogue_threadblock_epilogue, f16_wmma_tensor_op_64x128_64x64x16) {

  //
  // Define the warp-level matrix multiply
  //
  using ElementOutput = cutlass::half_t;
  using ElementAccumulator = cutlass::half_t;
  using ElementCompute = cutlass::half_t;
  int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
  int const kPartitionsK = 1;
  
  using Shape = cutlass::gemm::GemmShape<64, 128, 16>;
  using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
  using InstructionShape = cutlass::gemm::GemmShape<16, 16, 16>;
  using ElementA = cutlass::half_t;
  using ElementB = cutlass::half_t;
  using ElementC = ElementAccumulator;
  using LayoutA = cutlass::layout::RowMajor;
  using LayoutB = cutlass::layout::ColumnMajor;
  using LayoutC = cutlass::layout::RowMajor;

  using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
      WarpShape, 
      InstructionShape, 
      ElementA, 
      LayoutA, 
      ElementB, 
      LayoutB, 
      ElementC,
      LayoutC>::Type;

  //
  // Output operator
  //

  using OutputOp = cutlass::epilogue::thread::LinearCombination<
    ElementOutput,
    kElementsPerAccess,
    ElementAccumulator,
    ElementCompute
  >;

  //
  // Define the epilogue
  //

  using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueWmmaTensorOp<
    Shape,
    WarpMmaTensorOp,
    kPartitionsK,
    OutputOp,
    kElementsPerAccess
  >::Epilogue;

  //
  // Instantiate epilogue
  //

  EpilogueTestbed<Epilogue> testbed;

  bool passed = testbed.run_all();

  EXPECT_TRUE(passed);

}

/////////////////////////////////////////////////////////////////////////////////////////////////
//
// F32 acumulation and F32 output
//
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(SM70_Epilogue_threadblock_epilogue, f32_wmma_tensor_op_64x64_64x64x16) {

  //
  // Define the warp-level matrix multiply
  //
  using ElementOutput = float;
  using ElementAccumulator = float;
  using ElementCompute = cutlass::half_t;
  int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
  int const kPartitionsK = 1;
  
  using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
  using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
  using InstructionShape = cutlass::gemm::GemmShape<16, 16, 16>;
  using ElementA = cutlass::half_t;
  using ElementB = cutlass::half_t;
  using ElementC = ElementAccumulator;
  using LayoutA = cutlass::layout::RowMajor;
  using LayoutB = cutlass::layout::ColumnMajor;
  using LayoutC = cutlass::layout::RowMajor;

  using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
      WarpShape, 
      InstructionShape, 
      ElementA, 
      LayoutA, 
      ElementB, 
      LayoutB, 
      ElementC,
      LayoutC>::Type;

  //
  // Output operator
  //

  using OutputOp = cutlass::epilogue::thread::LinearCombination<
    ElementOutput,
    kElementsPerAccess,
    ElementAccumulator,
    ElementCompute
  >;

  //
  // Define the epilogue
  //

  using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueWmmaTensorOp<
    Shape,
    WarpMmaTensorOp,
    kPartitionsK,
    OutputOp,
    kElementsPerAccess
  >::Epilogue;

  //
  // Instantiate epilogue
  //

  EpilogueTestbed<Epilogue> testbed;

  bool passed = testbed.run_all();

  EXPECT_TRUE(passed);

}
/////////////////////////////////////////////////////////////////////////////////////////////////


#endif //CUTLASS_ARCH_WMMA_ENABLED
