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Add FP64 XDL GEMM built-in function #199
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c19ea8e
add intrin_mfma_f64_16x16x4f64
ltqin bf5af9f
add example
ltqin 6c8ca54
gemm reference add double data type
ltqin 10a2ae2
chang init data
ltqin 873d095
fix M N PerXdlops
ltqin eff586a
fix ifdef
ltqin d443a7a
add comparsion config
ltqin dcdbed2
add conv fwd example
ltqin ef77a1c
format log out
ltqin 7e8e54d
change rc matrix egister layout
ltqin 3991a1c
reorganize example
ltqin ea97054
reorganize example 2
ltqin 3680271
Merge branch 'develop' into add_mfma_f64
ltqin b615d65
format,because merge develop
ltqin 6bf08bb
fix call impl adding acc data type
ltqin 2959f94
lost ;
ltqin bc7b533
add compiler warning
ltqin 85ef3f2
change example tunning parameters
ltqin 4a77f45
add test for fp64
ltqin 75ef75b
add instance
ltqin 94fad45
add test/gemm/gemm_fp64.cpp
ltqin 1286072
fix get name issue
ltqin 04397fa
remove some tunning parameter
ltqin ccf0188
Merge branch 'develop' into add_mfma_f64
ltqin 345acac
fix conflict
ltqin caa0d9c
Merge branch 'add_mfma_f64' of https://github.com/ROCmSoftwarePlatfor…
ltqin 58f4d82
format
ltqin 579e8e7
use integer value for GEMM test
ebfa392
Merge remote-tracking branch 'origin/fix_test' into add_mfma_f64
9e8cb76
add acc data type
ltqin 67de476
remove typeid because fp16
ltqin b38fb37
Merge branch 'develop' into add_mfma_f64
ltqin 42b0321
fix streamconfig etc bug from merging develop
ltqin 1df7eba
format
ltqin bb4b9f0
remove test_gemm_xdl_fp64
ltqin 2b6d3e4
add AccDataType
ltqin 888acf3
AccDataType problem
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,240 @@ | ||
| #include <iostream> | ||
| #include <numeric> | ||
| #include <initializer_list> | ||
| #include <cstdlib> | ||
| #include <stdlib.h> | ||
| #include <half.hpp> | ||
|
|
||
| #include "check_err.hpp" | ||
| #include "config.hpp" | ||
| #include "device.hpp" | ||
| #include "host_tensor.hpp" | ||
| #include "host_tensor_generator.hpp" | ||
| #include "device_tensor.hpp" | ||
| #include "device_gemm_xdl.hpp" | ||
| #include "device_gemm_xdl_cshuffle.hpp" | ||
| #include "element_wise_operation.hpp" | ||
| #include "reference_gemm.hpp" | ||
| #include "gemm_specialization.hpp" | ||
|
|
||
| template <ck::index_t... Is> | ||
| using S = ck::Sequence<Is...>; | ||
|
|
||
| using F64 = double; | ||
| using F32 = float; | ||
| using F16 = ck::half_t; | ||
|
|
||
| using ADataType = double; | ||
| using BDataType = double; | ||
| using CDataType = double; | ||
| using AccDataType = double; | ||
|
|
||
| using Row = ck::tensor_layout::gemm::RowMajor; | ||
| using Col = ck::tensor_layout::gemm::ColumnMajor; | ||
|
|
||
| using PassThrough = ck::tensor_operation::element_wise::PassThrough; | ||
|
|
||
| using ALayout = ck::tensor_layout::gemm::RowMajor; | ||
| using BLayout = ck::tensor_layout::gemm::ColumnMajor; | ||
| using CLayout = ck::tensor_layout::gemm::RowMajor; | ||
|
|
||
| using AElementOp = ck::tensor_operation::element_wise::PassThrough; | ||
| using BElementOp = ck::tensor_operation::element_wise::PassThrough; | ||
| using CElementOp = ck::tensor_operation::element_wise::PassThrough; | ||
|
|
||
| static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; | ||
|
|
||
| // clang-format off | ||
| using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl | ||
| //##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer| | ||
| //##########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar| | ||
| //##########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector| | ||
| //##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ||
| #if 0 | ||
| < F64, F64, F64, F64, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 32, 32, 4, 1, 16, 16, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, true, 7, 1>; | ||
| #else | ||
| < F64, F64, F64, F64, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 2, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, 7, 1>; | ||
| #endif | ||
| // clang-format on | ||
|
|
||
| using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType, | ||
| BDataType, | ||
| CDataType, | ||
| AccDataType, | ||
| AElementOp, | ||
| BElementOp, | ||
| CElementOp>; | ||
|
|
||
| template <typename DataType> | ||
| std::ostream& show_2d_matrix(std::ostream& os, Tensor<DataType>& matrix) | ||
| { | ||
| os << "[" << std::endl; | ||
| for(int x = 0; x < matrix.mDesc.GetLengths()[0]; x++) | ||
| { | ||
| os << "["; | ||
| for(int y = 0; y < matrix.mDesc.GetLengths()[1]; y++) | ||
| { | ||
| os << std::setw(4) << static_cast<float>(matrix(x, y)); | ||
| } | ||
| os << "]" << std::endl; | ||
| } | ||
| os << "]"; | ||
| return os; | ||
| } | ||
|
|
||
| int main(int argc, char* argv[]) | ||
| { | ||
| bool do_verification = 0; | ||
| int init_method = 0; | ||
| bool time_kernel = false; | ||
|
|
||
| // GEMM shape | ||
| ck::index_t M = 3840; | ||
| ck::index_t N = 4096; | ||
| ck::index_t K = 4096; | ||
|
|
||
| ck::index_t StrideA = 4096; | ||
| ck::index_t StrideB = 4096; | ||
| ck::index_t StrideC = 4096; | ||
|
|
||
| if(argc == 4) | ||
| { | ||
| do_verification = std::stoi(argv[1]); | ||
| init_method = std::stoi(argv[2]); | ||
| time_kernel = std::stoi(argv[3]); | ||
| } | ||
| else if(argc == 10) | ||
| { | ||
| do_verification = std::stoi(argv[1]); | ||
| init_method = std::stoi(argv[2]); | ||
| time_kernel = std::stoi(argv[3]); | ||
|
|
||
| M = std::stoi(argv[4]); | ||
| N = std::stoi(argv[5]); | ||
| K = std::stoi(argv[6]); | ||
|
|
||
| StrideA = std::stoi(argv[7]); | ||
| StrideB = std::stoi(argv[8]); | ||
| StrideC = std::stoi(argv[9]); | ||
| } | ||
| else | ||
| { | ||
| printf("arg1: verification (0=no, 1=yes)\n"); | ||
| printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"); | ||
| printf("arg3: run kernel # of times (>1)\n"); | ||
| printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n"); | ||
| exit(0); | ||
| } | ||
|
|
||
| auto f_host_tensor_descriptor = | ||
| [](std::size_t row, std::size_t col, std::size_t stride, auto layout) { | ||
| if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) | ||
| { | ||
| return HostTensorDescriptor(std::vector<std::size_t>({row, col}), | ||
| std::vector<std::size_t>({stride, 1})); | ||
| } | ||
| else | ||
| { | ||
| return HostTensorDescriptor(std::vector<std::size_t>({row, col}), | ||
| std::vector<std::size_t>({1, stride})); | ||
| } | ||
| }; | ||
|
|
||
| Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{})); | ||
| Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{})); | ||
| Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); | ||
| Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); | ||
|
|
||
| std::cout << "data type: " << typeid(ADataType{}).name() << std::endl; | ||
| std::cout << "a_m_k: " << a_m_k.mDesc << std::endl; | ||
| std::cout << "b_k_n: " << b_k_n.mDesc << std::endl; | ||
| std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl; | ||
|
|
||
| switch(init_method) | ||
| { | ||
| case 0: break; | ||
| case 1: | ||
| a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}); | ||
| b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}); | ||
| break; | ||
| case 2: | ||
| a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}); | ||
| b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}); | ||
| break; | ||
| default: | ||
| a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1}); | ||
| b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1}); | ||
| } | ||
|
|
||
| DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace()); | ||
| DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace()); | ||
| DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace()); | ||
|
|
||
| a_m_k_device_buf.ToDevice(a_m_k.mData.data()); | ||
| b_k_n_device_buf.ToDevice(b_k_n.mData.data()); | ||
|
|
||
| auto a_element_op = AElementOp{}; | ||
| auto b_element_op = BElementOp{}; | ||
| auto c_element_op = CElementOp{}; | ||
|
|
||
| // do GEMM | ||
| auto gemm = DeviceGemmInstance{}; | ||
| auto invoker = gemm.MakeInvoker(); | ||
| auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()), | ||
| static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()), | ||
| static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()), | ||
| M, | ||
| N, | ||
| K, | ||
| StrideA, | ||
| StrideB, | ||
| StrideC, | ||
| a_element_op, | ||
| b_element_op, | ||
| c_element_op); | ||
|
|
||
| if(!gemm.IsSupportedArgument(argument)) | ||
| { | ||
| throw std::runtime_error( | ||
| "wrong! device_gemm with the specified compilation parameters does " | ||
| "not support this GEMM problem"); | ||
| } | ||
|
|
||
| float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel}); | ||
|
|
||
| std::size_t flop = std::size_t(2) * M * N * K; | ||
| std::size_t num_btype = | ||
| sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N; | ||
|
|
||
| float tflops = static_cast<float>(flop) / 1.E9 / ave_time; | ||
|
|
||
| float gb_per_sec = num_btype / 1.E6 / ave_time; | ||
|
|
||
| std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " | ||
| << gemm.GetTypeString() << std::endl; | ||
|
|
||
| c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data()); | ||
|
|
||
| if(do_verification) | ||
| { | ||
| auto ref_gemm = ReferenceGemmInstance{}; | ||
| auto ref_invoker = ref_gemm.MakeInvoker(); | ||
|
|
||
| auto ref_argument = ref_gemm.MakeArgument( | ||
| a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op); | ||
|
|
||
| ref_invoker.Run(ref_argument); | ||
|
|
||
| #if 0 | ||
| { | ||
| show_2d_matrix(std::cout << "a : ", a_m_k) << std::endl; | ||
| show_2d_matrix(std::cout << "b: ", b_k_n) << std::endl; | ||
| show_2d_matrix(std::cout << "c_device: ", c_m_n_device_result) << std::endl; | ||
| show_2d_matrix(std::cout << "c_host :", c_m_n_host_result) << std::endl; | ||
| } | ||
| #endif | ||
| ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData); | ||
| } | ||
|
|
||
| return 0; | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,8 @@ | ||
| add_example_executable(example_convnd_fwd_xdl_fp32 convnd_fwd_xdl_fp32.cpp) | ||
| add_example_executable(example_convnd_fwd_xdl_int8 convnd_fwd_xdl_int8.cpp) | ||
| add_example_executable(example_convnd_fwd_xdl_fp16 convnd_fwd_xdl_fp16.cpp) | ||
| add_example_executable(example_convnd_fwd_xdl_fp64 convnd_fwd_xdl_fp64.cpp) | ||
| target_link_libraries(example_convnd_fwd_xdl_fp64 PRIVATE conv_util) | ||
| target_link_libraries(example_convnd_fwd_xdl_fp32 PRIVATE conv_util) | ||
| target_link_libraries(example_convnd_fwd_xdl_int8 PRIVATE conv_util) | ||
| target_link_libraries(example_convnd_fwd_xdl_fp16 PRIVATE conv_util) |
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