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			7.2 KiB
		
	
	
	
		
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			178 lines
		
	
	
		
			7.2 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
| /***************************************************************************************************
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|  * Copyright (c) 2017 - 2024 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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| 
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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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| 
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| #include "conv2d_with_broadcast_testbed.h"
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| 
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| #if defined(CUTLASS_ARCH_MMA_SM75_SUPPORTED)
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| 
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| TEST(SM75_Device_Conv2d_Fprop_With_Broadcast_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32,
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|   128x128_32x2_64x64x32) {
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| 
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|   /// Conv operation element types for the Gemm equivalent (ImplicitGemm)
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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 ElementAccumulator = float;
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|   using ElementCompute     = float;
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| 
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|   using EpilogueOutputOp = cutlass::epilogue::thread::LinearCombinationBiasElementwise<
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|     cutlass::half_t,
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|     float,
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|     float,
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|     cutlass::half_t,
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|     cutlass::half_t,
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|     8,
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|     cutlass::epilogue::thread::ReLu<float>
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|   >;
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| 
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|   /// Device-level Conv2d instance
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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::Sm75,
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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<16, 8, 8>,
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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::kAnalytic
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|   >::Kernel;
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| 
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|   using Conv2dFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel>;
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| 
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|   /// Run all unit test sizes with device-level Conv2d instance
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|   EXPECT_TRUE(test::conv::device::TestAllConv2dWithBroadcast<Conv2dFprop>());
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| }
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| 
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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 = true
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| >
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| void Conv2dFpropSM75TestResidualBlock() {
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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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| 
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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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| 
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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::Sm75,
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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<16, 8, 8>,
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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::kAnalytic
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|   >::Kernel;
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| 
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|   using Conv2dFprop = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel>;
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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| TEST(SM75_Device_Conv2d_Fprop_With_Residual_Block_Plus_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32,
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|      128x128_32x2_64x64x32) {
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|   // Resnet
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|   Conv2dFpropSM75TestResidualBlock<cutlass::half_t, cutlass::epilogue::thread::Identity, cutlass::plus, cutlass::epilogue::thread::ReLu>();
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| }
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| 
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| TEST(SM75_Device_Conv2d_Fprop_With_Residual_Block_Multiply_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32,
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|      128x128_32x2_64x64x32) {
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|   // EfficientNet V2
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|   // Do not run split-K tests since the activation op is not Identity.
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|   Conv2dFpropSM75TestResidualBlock<float, cutlass::epilogue::thread::Sigmoid, cutlass::multiplies, cutlass::epilogue::thread::Identity, false>();
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| }
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| 
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| ////////////////////////////////////////////////////////////////////////////////
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| 
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| #endif  // CUTLASS_ARCH_MMA_SM75_SUPPORTED
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| 
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| ////////////////////////////////////////////////////////////////////////////////
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