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 *modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright notice,
 *this list of conditions and the following disclaimer.
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 *notice, this list of conditions and the following disclaimer in the
 *documentation and/or other materials provided with the distribution.
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 *contributors may be used to endorse or promote products derived from this
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 *AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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 *INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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/*! \file
    \brief Unit tests for thread-level GEMM
*/

#include <fstream>

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

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

#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/epilogue/thread/linear_combination_clamp.h"
#include "cutlass/gemm/warp/default_mma_tensor_op.h"
#include "cutlass/epilogue/threadblock/default_epilogue_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"

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

TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x64_64x64x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x64_32x32x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x128_64x64x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 32>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x64_64x32x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 64, 32>;
    using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x128_32x64x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 128, 32>;
    using WarpShape = cutlass::gemm::GemmShape<32, 64, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_32x128_32x64x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<32, 128, 32>;
    using WarpShape = cutlass::gemm::GemmShape<32, 64, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x32_64x32x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 32, 32>;
    using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_256x128_64x64x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<256, 128, 32>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x256_64x64x32) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::int4b_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            32 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 256, 32>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x64_64x64x16) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    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<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x64_32x3216) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            64 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x128_64x64x16) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x128_64x64x16) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x64_64x32x16) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            64 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 64, 16>;
    using WarpShape = cutlass::gemm::GemmShape<64, 32, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x128_32x64x16) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    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<32, 64, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_32x128_32x64x16) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<32, 128, 16>;
    using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x32_64x32x16) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = int8_t;
    using ElementAccumulator = int;
    using ElementCompute = float;
    int const kElementsPerAccess =
            64 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 32, 16>;
    using WarpShape = cutlass::gemm::GemmShape<64, 32, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
    using Element = ElementOutput;
    using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
            cutlass::sizeof_bits<Element>::value, 64>;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB,
            ElementAccumulator, cutlass::layout::RowMajor,
            cutlass::arch::OpMultiplyAddSaturate>::Type;

    //
    // Output operator
    //

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

    //
    // Define the epilogue
    //

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

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_64x64_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_128x128_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_128x256_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_256x128_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_32x32_32x32x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_64x64_32x32x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_64x128_32x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_128x64_64x32x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

/////////////////////////////////////////////////////////////////////////////////////////////////
//
// Mixed precision tests
//
/////////////////////////////////////////////////////////////////////////////////////////////////

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_64x64_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_128x128_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_128x256_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_256x128_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_32x32_32x32x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_64x64_32x32x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_64x128_32x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     mixed_f16_f32_tensor_op_128x64_64x32x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess =
            128 / cutlass::sizeof_bits<ElementOutput>::value;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

/////////////////////////////////////////////////////////////////////////////////////////////////
//
// F16 acumulation
//
/////////////////////////////////////////////////////////////////////////////////////////////////

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_64x64_64x64x8) {
    //
    // 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, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_128x128_64x64x8) {
    //
    // 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<128, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_128x256_64x64x8) {
    //
    // 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<128, 256, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_256x128_64x64x8) {
    //
    // 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<256, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_32x32_32x32x8) {
    //
    // 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<32, 32, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_64x64_32x32x8) {
    //
    // 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, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_64x128_32x64x8) {
    //
    // 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, 8>;
    using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_128x64_64x32x8) {
    //
    // 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<128, 64, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_64x64_32x32x4) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = double;
    using ElementAccumulator = double;
    using ElementCompute = double;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
    using Element = double;
    using ElementC = ElementAccumulator;
    using LayoutA =
            cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_128x64_64x32x4) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = double;
    using ElementAccumulator = double;
    using ElementCompute = double;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
    using Element = double;
    using ElementC = ElementAccumulator;
    using LayoutA =
            cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_64x128_32x64x4) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = double;
    using ElementAccumulator = double;
    using ElementCompute = double;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
    using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
    using Element = double;
    using ElementC = ElementAccumulator;
    using LayoutA =
            cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_128x128_32x64x4) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = double;
    using ElementAccumulator = double;
    using ElementCompute = double;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
    using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
    using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
    using Element = double;
    using ElementC = ElementAccumulator;
    using LayoutA =
            cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     vec1_mixed_f16_f32_tensor_op_128x128_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue,
     vec1_mixed_f16_f32_tensor_op_128x256_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = cutlass::half_t;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

TEST(SM75_Epilogue_threadblock_epilogue, vec1_tensor_op_128x128_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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

TEST(SM75_Epilogue_threadblock_epilogue, vec1_tensor_op_128x256_64x64x8) {
    //
    // Define the warp-level matrix multiply
    //

    using ElementOutput = float;
    using ElementAccumulator = float;
    using ElementCompute = float;
    int const kElementsPerAccess = 1;
    int const kPartitionsK = 1;

    using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
    using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
    using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
    using Element = cutlass::half_t;
    using ElementC = ElementAccumulator;
    using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
            cutlass::sizeof_bits<Element>::value, 64>;
    using LayoutC = cutlass::layout::RowMajor;

    using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
            WarpShape, InstructionShape, Element, LayoutA, Element, 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::DefaultEpilogueTensorOp<
                    Shape, WarpMmaTensorOp, kPartitionsK, OutputOp,
                    kElementsPerAccess>::Epilogue;

    //
    // Instantiate epilogue
    //

    EpilogueTestbed<Epilogue> testbed;

    bool passed = testbed.run_all();

    EXPECT_TRUE(passed);
}

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