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/*! \file
    \brief Tests for device-wide Implicit GEMM interface
*/

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


#include "cutlass/conv/kernel/default_conv2d_group_fprop.h"
#include "cutlass/conv/device/implicit_gemm_convolution.h"

#include "conv2d_testbed.h"

#if defined(CUTLASS_ARCH_MMA_SM80_SUPPORTED)

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

TEST(SM80_Device_Conv2d_Group_Fprop_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  SingleGroupPerCTA_128x128_64x3_64x64x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<128, 128, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<64, 64, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kSingleGroup,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_single_group_sizes));
}

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

TEST(SM80_Device_Conv2d_Group_Fprop_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  SingleGroupPerCTA_64x64_64x3_32x32x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<64, 64, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<32, 32, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kSingleGroup,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_single_group_sizes));
}

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

TEST(SM80_Device_Conv2d_Group_Fprop_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  MultipleGroupPerCTA_128x128_64x3_64x64x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<128, 128, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<64, 64, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kMultipleGroup,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_multiple_group_sizes));
}

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

TEST(SM80_Device_Conv2d_Group_Fprop_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  MutipleGroupPerCTA_64x64_64x3_32x32x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<64, 64, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<32, 32, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kMultipleGroup,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_multiple_group_sizes));
}

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

// Analytic 2 stage SingleGroup kernel
TEST(SM80_Device_Conv2d_Group_Fprop_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  SingleGroupPerCTA_128x128_64x2_64x64x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<128, 128, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<64, 64, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    2,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kSingleGroup,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_single_group_sizes));
}

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

// Analytic 2 stage MutipleGroup kernel
TEST(SM80_Device_Conv2d_Group_Fprop_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  MutipleGroupPerCTA_64x64_64x2_32x32x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<64, 64, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<32, 32, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    2,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kMultipleGroup,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_multiple_group_sizes));
}

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

TEST(SM80_Device_Conv2d_Group_Fprop_Optimized_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  SingleGroupPerCTA_128x128_64x3_64x64x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<128, 128, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<64, 64, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kSingleGroup,
    cutlass::conv::IteratorAlgorithm::kOptimized
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_single_group_sizes));
}

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

// Optimized multistage singleGroup kernel
TEST(SM80_Device_Conv2d_Group_Fprop_Optimized_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  SingleGroupPerCTA_64x64_64x3_32x32x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<64, 64, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<32, 32, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kSingleGroup,
    cutlass::conv::IteratorAlgorithm::kOptimized
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_single_group_sizes));
}

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

// Optimized 2 stage SingleGroup kernel
TEST(SM80_Device_Conv2d_Group_Fprop_Optimized_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32,
  SingleGroupPerCTA_64x64_64x2_32x32x64) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA           = cutlass::half_t;
  using ElementB           = cutlass::half_t;
  using ElementC           = cutlass::half_t;
  using ElementAccumulator = float;
  using ElementCompute     = float;
  using ThreadblockShape   = cutlass::gemm::GemmShape<64, 64, 64>;
  using WarpShape          = cutlass::gemm::GemmShape<32, 32, 64>;
  using InstructionShape   = cutlass::gemm::GemmShape<16, 8, 16>;

  /// Device-level Conv2d instance
  using Conv2dGroupFpropKernel = typename cutlass::conv::kernel::DefaultConv2dGroupFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    ThreadblockShape,
    WarpShape,
    InstructionShape,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    2,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::GroupMode::kSingleGroup,
    cutlass::conv::IteratorAlgorithm::kOptimized
  >::Kernel;

  using Conv2dGroupFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dGroupFpropKernel>;

  /// Run group conv unit test sizes with device-level Conv2d instance
  test::conv::device::TestbedGroupConv2dProblemSizes problem_sizes(
    ThreadblockShape::kN, ThreadblockShape::kK,
    128/cutlass::sizeof_bits<ElementA>::value
  );
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dGroupFprop>(problem_sizes.default_single_group_sizes));
}

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

#endif  // CUTLASS_ARCH_MMA_SM80_SUPPORTED

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