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https://github.com/ROCm/composable_kernel.git
synced 2026-06-05 20:55:59 +00:00
Example added
This commit is contained in:
myamlak
@@ -11,10 +11,272 @@
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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_gemm_xdl_cshuffle.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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// stub only
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int main() { return 0; }
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using BF16 = ck::bhalf_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 = BF16;
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using BDataType = BF16;
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using CDataType = BF16;
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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<float, float, float, PassThrough, PassThrough, PassThrough>;
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int main(int argc, char* argv[])
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{
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bool do_verification = 0;
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int init_method = 0;
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int nrepeat = 5;
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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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nrepeat = 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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nrepeat = 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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Tensor<CDataType> aux(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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std::cout << "aux: " << aux.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>{-5, 5});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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break;
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default:
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a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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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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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 aux_device_buf(sizeof(CDataType) * aux.mDesc.GetElementSpace());
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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 cgemm = DeviceCGemmInstance{};
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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*>(aux_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, nrepeat);
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std::size_t flop = std::size_t(8) * M * N * K;
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std::size_t num_btype = std::size_t(2) * sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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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<float> a_f32_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<float> a_f32_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<float> b_f32_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<float> b_f32_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<float> c_m_n_real_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<float> c_m_n_imag_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<float> c_m_n_real_device_f32_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<float> c_m_n_imag_device_f32_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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bf16_to_f32_(a_m_k_real, a_f32_m_k_real);
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bf16_to_f32_(a_m_k_imag, a_f32_m_k_imag);
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bf16_to_f32_(b_k_n_real, b_f32_k_n_real);
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bf16_to_f32_(b_k_n_imag, b_f32_k_n_imag);
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bf16_to_f32_(c_m_n_real_device_result, c_m_n_real_device_f32_result);
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bf16_to_f32_(c_m_n_imag_device_result, c_m_n_imag_device_f32_result);
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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_f32_m_k_real,
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a_f32_m_k_imag,
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b_f32_k_n_real,
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b_f32_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_f32_result.mData, c_m_n_real_host_result.mData);
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ck::utils::check_err(c_m_n_imag_device_f32_result.mData, c_m_n_imag_host_result.mData);
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}
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return 0;
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}
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