
CUTLASS 1.3 Release - Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.
140 lines
5.3 KiB
C++
140 lines
5.3 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (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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////////////////////////////////////////////////////////////////////////////////////////////////////
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#pragma once
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#include "cutlass/gemm/device_gemm.h"
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#include "cutlass/gemm/volta884_gemm_traits.h"
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#include "tools/test/perf/cutlass_perf_test.h"
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#include "tools/test/perf/gemm/gemm_profiler.h"
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#include "tools/test/perf/gemm/cutlass_dispatch.h"
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#include "tools/test/perf/gemm/gemm_perf_testbed.h"
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////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename Traits>
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struct Volta884GemmDispatchSplitKPI {
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typedef cutlass::gemm::DeviceGemm<Traits> Gemm;
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typedef typename Gemm::Params Params;
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typedef typename Traits::ScalarC ScalarC;
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typedef typename Traits::ScalarD ScalarD;
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typedef typename Traits::Scalar ScalarEpilogue;
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/// Indicate warp-level GEMM
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static bool const kThreadMultiplyAdd = false;
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#if CUTLASS_ENABLE_CUBLAS
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static bool const kRunCuBLAS = true;
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#else
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static bool const kRunCuBLAS = false;
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#endif
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static cutlass::MatrixLayout::Kind const kLayoutA = Traits::kLayoutA;
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static cutlass::MatrixLayout::Kind const kLayoutB = Traits::kLayoutB;
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//
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// Data members
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//
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/// Params argument
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Params params;
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/// splitK PI require workspace
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typename cutlass::TypeTraits<typename Traits::ScalarAccum>::device_type *workspace_ptr;
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//
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// Methods
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//
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Volta884GemmDispatchSplitKPI() {}
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/// Initializes params object
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Volta884GemmDispatchSplitKPI(int m, int n, int k, ScalarEpilogue alpha, half const* d_a, int lda,
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half const* d_b, int ldb, ScalarEpilogue beta, ScalarC const* d_c, int ldc,
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ScalarD* d_d, int ldd) {
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params.init_problem(m, n, k);
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size_t workspace_size_in_byte = params.required_workspace_memory_in_byte();
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size_t available_device_memory_in_byte = 0;
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size_t device_memory_in_byte = 0;
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cudaError_t cudaMemGetInfo_err = cudaMemGetInfo(&available_device_memory_in_byte, &device_memory_in_byte);
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if (cudaMemGetInfo_err != cudaSuccess) {
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std::cout << "\ncudaMemGetInfo error: " << cudaGetErrorString(cudaMemGetInfo_err)
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<< "\n";
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}
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if (workspace_size_in_byte > available_device_memory_in_byte) {
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std::cout << "reqested workspace memory size(" << workspace_size_in_byte <<
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") is larger than available memory size(" << available_device_memory_in_byte << "). Abort." << std::endl;
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throw std::runtime_error("reqested workspace memory size is larger than available memory size. Abort.");
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}
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cudaError_t workspace_err = cudaMalloc(&workspace_ptr, workspace_size_in_byte);
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if (workspace_err != cudaSuccess) {
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std::cout << "\nCUDA workspace malloc error: " << cudaGetErrorString(workspace_err)
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<< "\n";
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}
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params.initialize(alpha, d_a, lda, d_b, ldb, beta, d_c, ldc, d_d, ldd, workspace_ptr, 8 /*volta884 requires leading dim to be mulitiple of 8*/);
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}
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Volta884GemmDispatchSplitKPI(int m,
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int n,
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int k,
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ScalarEpilogue alpha,
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half const* d_a,
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int lda,
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long long int batch_stride_A,
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half const* d_b,
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int ldb,
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long long int batch_stride_B,
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ScalarEpilogue beta,
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ScalarC const* d_c,
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int ldc,
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long long int batch_stride_C,
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ScalarD* d_d,
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int ldd,
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long long int batch_stride_D,
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int batch_count) {
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assert(0);//not yet supported
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}
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/// Initializes params object
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Volta884GemmDispatchSplitKPI(Params const& _params) : params(_params) {}
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/// Launches kernel
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cudaError_t operator()() {
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return Gemm::launch(params);
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}
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};
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