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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_fprop.h"
#include "cutlass/conv/device/implicit_gemm_convolution.h"

#include "conv2d_testbed.h"

#if defined(CUTLASS_ARCH_MMA_SM80_SUPPORTED)

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

std::vector<cutlass::conv::Conv2dProblemSize> Conv2dFewChannelProblemSizes(int channels) {

  std::vector<cutlass::conv::Conv2dProblemSize> problems;

  problems.push_back(cutlass::conv::Conv2dProblemSize(
    {1, 8, 8, channels},   // input size  (NHWC)
    {16, 3, 3, channels},   // filter size (KRSC)
    {1, 1, 1, 1},                      // padding (pad_h, _, pad_w, _)
    {2, 2},                            // stride (stride_h, stride_w)
    {1, 1}                             // dilation (dilation_h, dilation_w)
  ));

  problems.push_back(cutlass::conv::Conv2dProblemSize(
    {1, 16, 16, channels},   // input size  (NHWC)
    {16, 3, 3, channels},   // filter size (KRSC)
    {1, 1, 1, 1},                      // padding (pad_h, _, pad_w, _)
    {2, 2},                            // stride (stride_h, stride_w)
    {1, 1}                             // dilation (dilation_h, dilation_w)
  ));

  problems.push_back(cutlass::conv::Conv2dProblemSize(
    {1, 16, 16, channels},   // input size  (NHWC)
    {16, 7, 7, channels},   // filter size (KRSC)
    {1, 1, 1, 1},                      // padding (pad_h, _, pad_w, _)
    {1, 1},                            // stride (stride_h, stride_w)
    {1, 1}                             // dilation (dilation_h, dilation_w)
  ));

  problems.push_back(cutlass::conv::Conv2dProblemSize(
    {1, 224, 224, channels},   // input size  (NHWC)
    {32, 7, 7, channels},   // filter size (KRSC)
    {1, 1, 1, 1},                      // padding (pad_h, _, pad_w, _)
    {1, 1},                            // stride (stride_h, stride_w)
    {1, 1}                             // dilation (dilation_h, dilation_w)
  ));

  problems.push_back(cutlass::conv::Conv2dProblemSize(
    {1, 224, 224, channels},   // input size  (NHWC)
    {64, 7, 7, channels},   // filter size (KRSC)
    {1, 1, 1, 1},                      // padding (pad_h, _, pad_w, _)
    {2, 2},                            // stride (stride_h, stride_w)
    {1, 1}                             // dilation (dilation_h, dilation_w)
  ));

  problems.push_back(cutlass::conv::Conv2dProblemSize(
    {1, 224, 224, channels},   // input size  (NHWC)
    {64, 5, 5, channels},   // filter size (KRSC)
    {1, 1, 1, 1},                      // padding (pad_h, _, pad_w, _)
    {1, 1},                            // stride (stride_h, stride_w)
    {1, 1}                             // dilation (dilation_h, dilation_w)
  ));

  problems.push_back(cutlass::conv::Conv2dProblemSize(
    {1, 224, 224, channels},   // input size  (NHWC)
    {64, 5, 5, channels},   // filter size (KRSC)
    {1, 1, 1, 1},                      // padding (pad_h, _, pad_w, _)
    {2, 2},                            // stride (stride_h, stride_w)
    {1, 1}                             // dilation (dilation_h, dilation_w)
  ));

  return problems;
}

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

TEST(SM80_Device_Conv2d_Fprop_Few_Channels_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_channels_8,
  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;

  int const kChannelCount = 8;

  /// Device-level Conv2d instance
  using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    cutlass::gemm::GemmShape<128, 128, 64>,
    cutlass::gemm::GemmShape<64, 64, 64>,
    cutlass::gemm::GemmShape<16, 8, 16>,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kFewChannels,
    cutlass::conv::StrideSupport::kStrided,
    kChannelCount,
    kChannelCount
  >::Kernel;

  using Conv2dFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel>;

  /// Run all unit test sizes with device-level Conv2d instance
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dFprop>(
    Conv2dFewChannelProblemSizes(kChannelCount)));
}

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

TEST(SM80_Device_Conv2d_Fprop_Few_Channels_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_channels_4,
  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;

  int const kChannelCount = 4;

  /// Device-level Conv2d instance
  using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    cutlass::gemm::GemmShape<128, 128, 64>,
    cutlass::gemm::GemmShape<64, 64, 64>,
    cutlass::gemm::GemmShape<16, 8, 16>,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kFewChannels,
    cutlass::conv::StrideSupport::kStrided,
    kChannelCount,
    kChannelCount
  >::Kernel;

  using Conv2dFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel>;

  /// Run all unit test sizes with device-level Conv2d instance
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dFprop>(
    Conv2dFewChannelProblemSizes(kChannelCount)));
}

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

TEST(SM80_Device_Conv2d_Fprop_Few_Channels_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_channels_2,
  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;

  int const kChannelCount = 2;

  /// Device-level Conv2d instance
  using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    cutlass::gemm::GemmShape<128, 128, 64>,
    cutlass::gemm::GemmShape<64, 64, 64>,
    cutlass::gemm::GemmShape<16, 8, 16>,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    3,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kFewChannels,
    cutlass::conv::StrideSupport::kStrided,
    kChannelCount,
    kChannelCount
  >::Kernel;

  using Conv2dFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel>;

  /// Run all unit test sizes with device-level Conv2d instance
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dFprop>(
    Conv2dFewChannelProblemSizes(2 * kChannelCount)));
}

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

TEST(SM80_Device_Conv2d_Fprop_Few_Channels_ImplicitGemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_channels_1,
  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;

  int const kChannelCount = 1;

  /// Device-level Conv2d instance
  using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFprop<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm80,
    cutlass::gemm::GemmShape<128, 128, 32>,
    cutlass::gemm::GemmShape<64, 64, 32>,
    cutlass::gemm::GemmShape<16, 8, 8>,
    cutlass::epilogue::thread::LinearCombination<
      ElementC,
      128 / cutlass::sizeof_bits<ElementC>::value,
      ElementAccumulator,
      ElementCompute
    >,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    2,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kFewChannels,
    cutlass::conv::StrideSupport::kStrided,
    kChannelCount,
    kChannelCount
  >::Kernel;

  using Conv2dFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel>;

  /// Run all unit test sizes with device-level Conv2d instance
  EXPECT_TRUE(test::conv::device::TestSpecificConv2d<Conv2dFprop>(
    Conv2dFewChannelProblemSizes(3 * kChannelCount)));
}

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

#endif  // CUTLASS_ARCH_MMA_SM80_SUPPORTED

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