cutlass/test/unit/epilogue/threadblock/epilogue_volta_tensor_op.cu

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/*! \file
\brief Unit tests for thread-level GEMM
*/
#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/mma_tensor_op_sm70.h"
#include "cutlass/epilogue/warp/fragment_iterator_volta_tensor_op.h"
#include "cutlass/epilogue/threadblock/default_thread_map_volta_tensor_op.h"
#include "cutlass/epilogue/threadblock/default_epilogue_volta_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(SM70_Epilogue_threadblock_epilogue, f16_volta_tensor_op_64x64_32x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_128x64_64x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_64x128_32x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_64x64_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_64x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_128x64_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_128x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_128x256_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 256, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_volta_tensor_op_256x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<256, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = cutlass::half_t;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = cutlass::half_t;
using ElementCompute = cutlass::half_t;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementC>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////
//
// Mixed: F32 accumulation
//
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(SM70_Epilogue_threadblock_epilogue, f16_f32_volta_tensor_op_64x64_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_f32_volta_tensor_op_128x256_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 256, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_f32_volta_tensor_op_256x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<256, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_f32_volta_tensor_op_128x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_f32_volta_tensor_op_64x64_32x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_f32_volta_tensor_op_64x128_32x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
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_f32_volta_tensor_op_128x64_64x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////
//
// F32 accumulation, F32 output
//
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_64x64_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_64x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_128x64_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_128x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_128x256_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 256, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_256x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<256, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_64x64_32x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_128x64_64x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, f32_volta_tensor_op_64x128_32x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////
// This works
TEST(SM70_Epilogue_threadblock_epilogue, vec8_f16_f32_volta_tensor_op_64x64_32x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 8;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
// This works
TEST(SM70_Epilogue_threadblock_epilogue, vec2_f16_f32_volta_tensor_op_64x64_32x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 2;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////
// This fails
TEST(SM70_Epilogue_threadblock_epilogue, vec1_f16_f32_volta_tensor_op_64x64_32x32x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<64, 64, 4>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 1;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(SM70_Epilogue_threadblock_epilogue, vec1_f32_volta_tensor_op_128x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = float;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 1;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, vec1_f16_f32_volta_tensor_op_128x128_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 128, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 1;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM70_Epilogue_threadblock_epilogue, vec1_f16_f32_volta_tensor_op_128x256_64x64x4) {
//
// Define the warp-level matrix multiply
//
using Shape = cutlass::gemm::GemmShape<128, 256, 4>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 4>;
using ElementA = cutlass::half_t;
using ElementB = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<ElementA>::value>;
using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<ElementB>::value>;
using ElementOutput = cutlass::half_t;
using ElementAccumulator = ElementC;
using ElementCompute = ElementC;
using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
cutlass::arch::Mma<
cutlass::gemm::GemmShape<16, 16, 4>,
32,
ElementA,
cutlass::layout::ColumnMajor,
ElementB,
cutlass::layout::RowMajor,
ElementC,
cutlass::layout::RowMajor,
cutlass::arch::OpMultiplyAdd
>,
cutlass::MatrixShape<1, 1>
>;
using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
WarpShape,
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
cutlass::layout::RowMajor,
Policy
>;
int const kPartitionsK = 1;
int const kElementsPerAccess = 1;
using ThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp<
Shape,
WarpShape,
kPartitionsK,
ElementC,
kElementsPerAccess,
ElementAccumulator>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////