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https://github.com/ROCm/composable_kernel.git
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7b1e2c379e
* Reference CGEMM + test stub * Format. * Incomplete simple implementation * Library instances * Sketch of tests * Test fixes. * Example added * Cosmetics * Add elementwise operation kernel and example * Add comment * Add template argument of dim . Prepare to support multiple dimension * Rename example * Support 1 dimension * Add static assert * Add comment * Second auxiliary buffer added * Extract pad * Remove redundant argument * Support any dimension for elementwise operation * Remove line * Let it be the multiple number of CU * Move thread per block to the parameter of constructor * Consuming binary ops to do A+B / A-B * Fix + cosmetics + bf16 test commented out temporarily * Format * Enabling bf16 test * Revert "Enabling bf16 test" This reverts commitf497e2ba44. * Fix + test reenabled * fix build * Revert "fix build" This reverts commitd73102384b. * post PR #235 merge fix * amend * Single workspace for cgemm + helper * Perf calc fix * Review remarks: static_cast * Review remarks: binary ops templated * Cleaning * Removal of instances and their tests * Review remarks from aosew addressed * Review remark: unnecessary attribute * Post-merge fixes * Restrict 4gemm to PassThrough + bug fix * Review remarks * update licence * change cgemm example to fp16 Co-authored-by: rocking <chunylai@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com> Co-authored-by: Anthony Chang <ac.chang@outlook.com>
303 lines
13 KiB
C++
303 lines
13 KiB
C++
/*******************************************************************************
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*
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* MIT License
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*
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* Copyright (c) 2022 Advanced Micro Devices, Inc.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*
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*******************************************************************************/
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <stdlib.h>
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#include <half.hpp>
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#include "check_err.hpp"
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#include "config.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "device_tensor.hpp"
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#include "device_cgemm_4gemm_xdl_cshuffle.hpp"
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#include "element_wise_operation.hpp"
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#include "reference_cgemm.hpp"
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#include "gemm_specialization.hpp"
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F16 = ck::half_t;
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using F32 = float;
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ADataType = F16;
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using BDataType = F16;
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using CDataType = F16;
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using AccDataType = F32;
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using ALayout = ck::tensor_layout::gemm::RowMajor;
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using BLayout = ck::tensor_layout::gemm::ColumnMajor;
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using CLayout = ck::tensor_layout::gemm::RowMajor;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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// clang-format off
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using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_CShuffle
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<ALayout, // typename ALayout
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BLayout, // typename BLayout
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CLayout, // typename CLayout
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ADataType, // typename ADataType
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BDataType, // typename BDataType
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CDataType, // typename CDataType
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AccDataType, // typename GemmAccDataType
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CDataType, // typename CShuffleDataType
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PassThrough, // typename AElementwiseOperation
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PassThrough, // typename BElementwiseOperation
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PassThrough, // typename CElementwiseOperation
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GemmDefault, // GemmSpecialization GemmSpec
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1, // index_t NumGemmKPrefetchStage
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256, // index_t BlockSize
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256, // index_t MPerBlock
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128, // index_t NPerBlock
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32, // index_t KPerBlock
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8, // index_t AK1
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8, // index_t BK1
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32, // index_t MPerXDL
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32, // index_t NPerXDL
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4, // index_t MXdlPerWave
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2, // index_t NXdlPerWave
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S<4, 64, 1>, // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
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S<1, 0, 2>, // typename ABlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // typename ABlockTransferSrcAccessOrder
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2, // index_t ABlockTransferSrcVectorDim
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8, // index_t ABlockTransferSrcScalarPerVector
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8, // index_t ABlockTransferDstScalarPerVector_AK1
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1, // index_t ABlockLdsExtraM
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S<4, 64, 1>, // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
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S<1, 0, 2>, // typename BBlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // typename BBlockTransferSrcAccessOrder
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2, // index_t BBlockTransferSrcVectorDim
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8, // index_t BBlockTransferSrcScalarPerVector
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8, // index_t BBlockTransferDstScalarPerVector_BK1
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1, // index_t BBlockLdsExtraN
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1, // index_t CShuffleMXdlPerWavePerShuffle
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1, // index_t CShuffleNXdlPerWavePerShuffle
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S<1, 32, 1, 8>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
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8>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
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// clang-format on
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using ReferenceCGemmInstance = ck::tensor_operation::host::
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ReferenceCGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
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int main(int argc, char* argv[])
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = false;
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// CGEMM shape
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ck::index_t M = 3840;
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ck::index_t N = 4096;
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ck::index_t K = 4096;
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ck::index_t StrideA = 4096;
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ck::index_t StrideB = 4096;
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ck::index_t StrideC = 4096;
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if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else if(argc == 10)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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M = std::stoi(argv[4]);
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N = std::stoi(argv[5]);
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K = std::stoi(argv[6]);
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StrideA = std::stoi(argv[7]);
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StrideB = std::stoi(argv[8]);
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StrideC = std::stoi(argv[9]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: run kernel # of times (>1)\n");
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printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
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exit(0);
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}
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1, stride}));
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}
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};
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Tensor<ADataType> a_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<ADataType> a_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<BDataType> b_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_m_n_real_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_imag_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k_real: " << a_m_k_real.mDesc << std::endl;
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std::cout << "a_m_k_imag: " << a_m_k_imag.mDesc << std::endl;
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std::cout << "b_k_n_real: " << b_k_n_real.mDesc << std::endl;
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std::cout << "b_k_n_imag: " << b_k_n_imag.mDesc << std::endl;
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std::cout << "c_m_n_real: " << c_m_n_real_device_result.mDesc << std::endl;
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std::cout << "c_m_n_imag: " << c_m_n_imag_device_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_m_k_real.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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break;
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default:
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a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
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b_k_n_real.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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b_k_n_imag.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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}
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auto cgemm = DeviceCGemmInstance{};
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DeviceMem a_m_k_real_device_buf(sizeof(ADataType) * a_m_k_real.mDesc.GetElementSpace());
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DeviceMem a_m_k_imag_device_buf(sizeof(ADataType) * a_m_k_imag.mDesc.GetElementSpace());
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DeviceMem b_k_n_real_device_buf(sizeof(BDataType) * b_k_n_real.mDesc.GetElementSpace());
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DeviceMem b_k_n_imag_device_buf(sizeof(BDataType) * b_k_n_imag.mDesc.GetElementSpace());
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DeviceMem c_m_n_real_device_buf(sizeof(CDataType) *
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c_m_n_real_device_result.mDesc.GetElementSpace());
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DeviceMem c_m_n_imag_device_buf(sizeof(CDataType) *
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c_m_n_imag_device_result.mDesc.GetElementSpace());
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DeviceMem workspace_device_buf(cgemm.GetWorkspaceSize(M, N, K, StrideA, StrideB, StrideC));
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a_m_k_real_device_buf.ToDevice(a_m_k_real.mData.data());
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a_m_k_imag_device_buf.ToDevice(a_m_k_imag.mData.data());
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b_k_n_real_device_buf.ToDevice(b_k_n_real.mData.data());
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b_k_n_imag_device_buf.ToDevice(b_k_n_imag.mData.data());
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auto a_element_op = PassThrough{};
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auto b_element_op = PassThrough{};
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auto c_element_op = PassThrough{};
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// do GEMM
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auto invoker = cgemm.MakeInvoker();
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auto argument =
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cgemm.MakeArgument(static_cast<ADataType*>(a_m_k_real_device_buf.GetDeviceBuffer()),
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static_cast<ADataType*>(a_m_k_imag_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_real_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_imag_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_real_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_imag_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(workspace_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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if(!cgemm.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_cgemm with the specified compilation parameters does "
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"not support this CGEMM problem");
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(8) * M * N * K;
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std::size_t num_btype =
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std::size_t(2) *
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(sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N);
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< cgemm.GetTypeString() << std::endl;
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c_m_n_real_device_buf.FromDevice(c_m_n_real_device_result.mData.data());
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c_m_n_imag_device_buf.FromDevice(c_m_n_imag_device_result.mData.data());
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if(do_verification)
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{
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Tensor<CDataType> c_m_n_real_host_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_imag_host_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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auto ref_cgemm = ReferenceCGemmInstance{};
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auto ref_invoker = ref_cgemm.MakeInvoker();
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auto ref_argument = ref_cgemm.MakeArgument(a_m_k_real,
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a_m_k_imag,
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b_k_n_real,
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b_k_n_imag,
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c_m_n_real_host_result,
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c_m_n_imag_host_result,
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a_element_op,
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b_element_op,
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c_element_op);
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ref_invoker.Run(ref_argument);
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ck::utils::check_err(c_m_n_real_device_result.mData,
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c_m_n_real_host_result.mData,
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"Verification error: incorrect results in real part!",
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1e-2f,
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1e-1f);
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ck::utils::check_err(c_m_n_imag_device_result.mData,
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c_m_n_imag_host_result.mData,
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"Verification error: incorrect results in imaginary part!",
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1e-2f,
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1e-1f);
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}
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return 0;
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}
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