cutlass/test/unit/conv/device/conv2d_fprop_with_broadcast_sm75.cu

/***************************************************************************************************
 * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification, are permitted
 * provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright notice, this list of
 *       conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright notice, this list of
 *       conditions and the following disclaimer in the documentation and/or other materials
 *       provided with the distribution.
 *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
 *       to endorse or promote products derived from this software without specific prior written
 *       permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
 * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
 * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
 * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
 * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/
/*! \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_conv2d_fprop_with_broadcast.h"
#include "cutlass/conv/device/implicit_gemm_convolution.h"

#include "conv2d_with_broadcast_testbed.h"

#if defined(CUTLASS_ARCH_MMA_SM75_SUPPORTED)

TEST(SM75_Device_Conv2d_Fprop_With_Broadcast_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32,
  128x128_32x2_64x64x32) {

  /// 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 EpilogueOutputOp = cutlass::epilogue::thread::LinearCombinationBiasElementwise<
    cutlass::half_t,
    float,
    float,
    cutlass::half_t,
    cutlass::half_t,
    8,
    cutlass::epilogue::thread::ReLu<float>
  >;

  /// Device-level Conv2d instance
  using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFpropWithBroadcast<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm75,
    cutlass::gemm::GemmShape<128, 128, 32>,
    cutlass::gemm::GemmShape<64, 64, 32>,
    cutlass::gemm::GemmShape<16, 8, 8>,
    EpilogueOutputOp,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    2,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

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

  /// Run all unit test sizes with device-level Conv2d instance
  EXPECT_TRUE(test::conv::device::TestAllConv2dWithBroadcast<Conv2dFprop>());
}

// Test residual block fusion: UnaryOp(BinaryOp(ActivationOp(Conv2d(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 Conv2d 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 <
 typename ElementAccumulator,
 template<typename T> class ActivationOp,
 template<typename T> class BinaryOp,
 template<typename T> class UnaryOp,
 bool TestSplitK = true
>
void TestResidaulBlock() {
  using ElementA = cutlass::half_t;
  using ElementB = cutlass::half_t;
  using ElementC = cutlass::half_t;
  using ElementD = ElementC;
  using ElementCompute = ElementAccumulator;

  using EpilogueOutputOp = cutlass::epilogue::thread::LinearCombinationResidualBlock<
    ElementD,
    ElementAccumulator,
    ElementCompute,
    ElementC,
    8,
    ActivationOp,
    BinaryOp,
    UnaryOp
  >;

  using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFpropWithBroadcast<
    ElementA, cutlass::layout::TensorNHWC,
    ElementB, cutlass::layout::TensorNHWC,
    ElementC, cutlass::layout::TensorNHWC,
    ElementAccumulator,
    cutlass::arch::OpClassTensorOp,
    cutlass::arch::Sm75,
    cutlass::gemm::GemmShape<128, 128, 32>,
    cutlass::gemm::GemmShape<64, 64, 32>,
    cutlass::gemm::GemmShape<16, 8, 8>,
    EpilogueOutputOp,
    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
    2,
    cutlass::arch::OpMultiplyAdd,
    cutlass::conv::IteratorAlgorithm::kAnalytic
  >::Kernel;

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

  struct ReferenceOp {
    using OutputOp = typename Conv2dFprop::EpilogueOutputOp;
    using ElementZ = typename OutputOp::ElementZ;

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

    void operator()(ElementZ &Z, ElementZ&, ElementCompute conv2d, ElementCompute residual) {
      Z = ElementZ(unary_op(binary_op(activation(conv2d), residual)));
    }
  };

  bool passed = test::conv::device::TestAllConv2dWithBroadcast<Conv2dFprop, ReferenceOp, true, TestSplitK>();
  EXPECT_TRUE(passed);
}

TEST(SM75_Device_Conv2d_Fprop_With_Residual_Block_Plus_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32,
     128x128_32x2_64x64x32) {
  // Resnet
  TestResidaulBlock<cutlass::half_t, cutlass::epilogue::thread::Identity, cutlass::plus, cutlass::epilogue::thread::ReLu>();
}

TEST(SM75_Device_Conv2d_Fprop_With_Residual_Block_Multiply_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32,
     128x128_32x2_64x64x32) {
  // EfficientNet V2
  // Do not run split-K tests since the activation op is not Identity.
  TestResidaulBlock<float, cutlass::epilogue::thread::Sigmoid, cutlass::multiplies, cutlass::epilogue::thread::Identity, false>();
}

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

#endif  // CUTLASS_ARCH_MMA_SM75_SUPPORTED

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