128 lines
5.3 KiB
Plaintext
128 lines
5.3 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Tests for device-wide Implicit GEMM interface
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*/
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/cutlass.h"
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#include "cutlass/array.h"
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#include "cutlass/epilogue/thread/linear_combination_bias_elementwise.h"
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#include "cutlass/epilogue/thread/linear_combination_residual_block.h"
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#include "cutlass/epilogue/thread/activation.h"
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#include "cutlass/conv/kernel/default_conv2d_fprop_with_broadcast.h"
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#include "cutlass/conv/device/implicit_gemm_convolution.h"
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#include "conv2d_with_broadcast_testbed.h"
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#if defined(CUTLASS_ARCH_MMA_SM70_SUPPORTED)
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// Test residual block fusion: UnaryOp(BinaryOp(ActivationOp(Conv2d(X) + bias), residual))
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// LinearCombinationResidualBlock does not support the split-k mode unless ActivationOp is Identity.
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// This is because the activation needs to be applied to the fully accumulated output of the Conv2d op,
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// which only the last thread block would have an access to, before applying BinaryOp.
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// The epilogue functor in the last thread block would have to be given three inputs, namely
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// partial outputs, bias, and residual, but this is not supported in the current interface.
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// Set TestSplitK = false to skip split-k tests with non-trivial ActivationOp.
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template <
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typename ElementAccumulator,
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template<typename T> class ActivationOp,
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template<typename T> class BinaryOp,
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template<typename T> class UnaryOp,
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bool TestSplitK = false
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>
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void TestResidaulBlock() {
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using ElementA = cutlass::half_t;
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using ElementB = cutlass::half_t;
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using ElementC = cutlass::half_t;
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using ElementD = ElementC;
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using ElementCompute = ElementAccumulator;
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using EpilogueOutputOp = cutlass::epilogue::thread::LinearCombinationResidualBlock<
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ElementD,
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ElementAccumulator,
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ElementCompute,
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ElementC,
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8,
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ActivationOp,
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BinaryOp,
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UnaryOp
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>;
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using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFpropWithBroadcast<
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ElementA, cutlass::layout::TensorNHWC,
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ElementB, cutlass::layout::TensorNHWC,
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ElementC, cutlass::layout::TensorNHWC,
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ElementAccumulator,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm70,
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cutlass::gemm::GemmShape<128, 128, 32>,
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cutlass::gemm::GemmShape<64, 64, 32>,
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cutlass::gemm::GemmShape<8, 8, 4>,
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EpilogueOutputOp,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
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2,
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cutlass::arch::OpMultiplyAdd,
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cutlass::conv::IteratorAlgorithm::kOptimized
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>::Kernel;
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using Conv2dFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel>;
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struct ReferenceOp {
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using OutputOp = typename Conv2dFprop::EpilogueOutputOp;
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using ElementZ = typename OutputOp::ElementZ;
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ActivationOp<ElementCompute> activation;
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BinaryOp<ElementCompute> binary_op;
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UnaryOp<ElementCompute> unary_op;
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void operator()(ElementZ &Z, ElementZ&, ElementCompute conv2d, ElementCompute residual) {
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Z = ElementZ(unary_op(binary_op(activation(conv2d), residual)));
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}
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};
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bool passed = test::conv::device::TestAllConv2dWithBroadcast<Conv2dFprop, ReferenceOp, true, TestSplitK>();
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EXPECT_TRUE(passed);
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}
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TEST(SM70_Device_Conv2d_Fprop_With_Residual_Block_Plus_Optimized_ImplicitGemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32,
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128x128_32x2_64x64x32) {
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// Resnet
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TestResidaulBlock<float, cutlass::epilogue::thread::ReLu, cutlass::plus, cutlass::epilogue::thread::Identity>();
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}
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////////////////////////////////////////////////////////////////////////////////
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#endif // CUTLASS_ARCH_MMA_SM70_SUPPORTED
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////////////////////////////////////////////////////////////////////////////////
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