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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/array.h"
#include "cutlass/epilogue/thread/linear_combination_bias_elementwise.h"
#include "cutlass/epilogue/thread/linear_combination_residual_block.h"
#include "cutlass/epilogue/thread/activation.h"
#include "cutlass/conv/kernel/default_deconv3d_with_broadcast.h"
#include "cutlass/conv/device/implicit_gemm_convolution.h"

#include "conv3d_with_broadcast_testbed.h"

#if defined(CUTLASS_ARCH_MMA_SM80_SUPPORTED)

TEST(SM80_Device_Deconv3d_With_Broadcast_Optimized_ImplicitGemm_f32ndhwc_f32ndhwc_f32ndhwc_simt_f32,
  128x128_32x2_64x64x32) {

  /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
  using ElementA = float;
  using ElementB = float;
  using ElementC = float;
  using ElementCompute = float;
  using ElementAccumulator = float;

  using EpilogueOutputOp = cutlass::epilogue::thread::LinearCombinationBiasElementwise<
    ElementC,
    ElementAccumulator,
    ElementCompute,
    ElementC,
    ElementC,
    1,
    cutlass::epilogue::thread::ReLu<float>
  >;

  /// Device-level Conv3d instance
  using Deconv3dKernel = typename cutlass::conv::kernel::DefaultDeconv3dWithBroadcast<
    ElementA, cutlass::layout::TensorNDHWC,
    ElementB, cutlass::layout::TensorNDHWC,
    ElementC, cutlass::layout::TensorNDHWC,
    ElementAccumulator,
    cutlass::arch::OpClassSimt,
    cutlass::arch::Sm80,
    cutlass::gemm::GemmShape<128, 128, 8>,
    cutlass::gemm::GemmShape<32, 64, 8>,
    cutlass::gemm::GemmShape<1, 1, 1>,
    EpilogueOutputOp,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    4,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kOptimized,
    cutlass::conv::StrideSupport::kUnity
  >::Kernel;

  using Deconv3d = cutlass::conv::device::ImplicitGemmConvolution<Deconv3dKernel>;

  /// Run all unit test sizes with device-level Conv3d instance
  EXPECT_TRUE(test::conv::device::TestAllConv3dWithBroadcast<Deconv3d>());
}

// Test residual block fusion: UnaryOp(BinaryOp(ActivationOp(Conv3d(X) + bias), residual))
// LinearCombinationResidualBlock does not support the split-k mode unless ActivationOp is Identity.
// This is because the activation needs to be applied to the fully accumulated output of the Conv3d op,
// which only the last thread block would have an access to, before applying BinaryOp.
// The epilogue functor in the last thread block would have to be given three inputs, namely
// partial outputs, bias, and residual, but this is not supported in the current interface.
// Set TestSplitK = false to skip split-k tests with non-trivial ActivationOp.
template <
 template<typename T> class ActivationOp,
 template<typename T> class BinaryOp,
 template<typename T> class UnaryOp,
 bool TestSplitK = true
>
static void Deconv3dSM80TestResidaulBlock() {
  using ElementA = float;
  using ElementB = float;
  using ElementC = float;
  using ElementD = ElementC;
  using ElementCompute = float;
  using ElementAccumulator = float;

  using EpilogueOutputOp = cutlass::epilogue::thread::LinearCombinationResidualBlock<
    ElementD,
    ElementAccumulator,
    ElementCompute,
    ElementC,
    1,
    ActivationOp,
    BinaryOp,
    UnaryOp
  >;

  using Deconv3dKernel = typename cutlass::conv::kernel::DefaultDeconv3dWithBroadcast<
    ElementA, cutlass::layout::TensorNDHWC,
    ElementB, cutlass::layout::TensorNDHWC,
    ElementC, cutlass::layout::TensorNDHWC,
    ElementAccumulator,
    cutlass::arch::OpClassSimt,
    cutlass::arch::Sm80,
    cutlass::gemm::GemmShape<128, 128, 8>,
    cutlass::gemm::GemmShape<32, 64, 8>,
    cutlass::gemm::GemmShape<1, 1, 1>,
    EpilogueOutputOp,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    4,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kOptimized,
    cutlass::conv::StrideSupport::kUnity
  >::Kernel;

  using Deconv3d = cutlass::conv::device::ImplicitGemmConvolution<Deconv3dKernel>;

  struct ReferenceOp {
    using OutputOp = typename Deconv3d::EpilogueOutputOp;
    using ElementZ = typename OutputOp::ElementZ;

    ActivationOp<ElementCompute> activation;
    BinaryOp<ElementCompute> binary_op;
    UnaryOp<ElementCompute> unary_op;

    void operator()(ElementZ &Z, ElementZ&, ElementCompute conv3d, ElementCompute residual) {
      Z = ElementZ(unary_op(binary_op(activation(conv3d), residual)));
    }
  };

  bool passed = test::conv::device::TestAllConv3dWithBroadcast<Deconv3d, ReferenceOp, true, TestSplitK>();
  EXPECT_TRUE(passed);
}

TEST(SM80_Device_Deconv3d_With_Residual_Block_Plus_Analytic_ImplicitGemm_f32ndhwc_f32ndhwc_f32ndhwc_simt_f32,
     128x128_8x4_32x64x8) {
  // Resnet
  Deconv3dSM80TestResidaulBlock<cutlass::epilogue::thread::Identity, cutlass::plus, cutlass::epilogue::thread::ReLu>();
}

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

#endif  // CUTLASS_ARCH_MMA_SM80_SUPPORTED

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