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https://github.com/ROCm/composable_kernel.git
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Gemm + bias + c_permute (#312)
* init commit
* add desc
* finished c permute
* fixed vector lens
[ROCm/composable_kernel commit: fa9a0a5cfb]
This commit is contained in:
zjing14
1
example/25_gemm_bias_c_permute/CMakeLists.txt
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1
example/25_gemm_bias_c_permute/CMakeLists.txt
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add_example_executable(example_gemm_bias_c_permute_xdl_fp16 gemm_bias_c_permute_xdl_fp16.cpp)
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284
example/25_gemm_bias_c_permute/gemm_bias_c_permute_xdl_fp16.cpp
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284
example/25_gemm_bias_c_permute/gemm_bias_c_permute_xdl_fp16.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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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 "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_bias_c_permute_xdl.hpp"
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#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
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#include "ck/library/host_tensor/device_memory.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/host_tensor/host_tensor_generator.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
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#include "ck/library/utility/check_err.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 Add = ck::tensor_operation::element_wise::Add;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F32;
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using DDataType = F16;
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using EDataType = F16;
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using ALayout = Row;
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using BLayout = Col;
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using DLayout = Row;
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using ELayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = Add;
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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 DeviceOpInstance = ck::tensor_operation::device::DeviceGemmBiasCPermute_Xdl
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//######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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//######| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//######| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>;
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// clang-format on
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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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ck::index_t M0 = 4;
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ck::index_t M1 = 32;
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ck::index_t M2 = 128;
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ck::index_t N0 = 16;
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ck::index_t N1 = 256;
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// GEMM shape
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ck::index_t M = M0 * M1 * M2;
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ck::index_t N = N0 * N1;
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ck::index_t K = 128;
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ck::index_t stride_A = K;
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ck::index_t stride_B = K;
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#if 1
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// E = [M0, N0, M1, N1, M2]
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ck::index_t stride_E_M0 = N0 * M1 * N1 * M2;
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ck::index_t stride_E_M1 = N1 * M2;
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ck::index_t stride_E_M2 = 1;
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ck::index_t stride_E_N0 = M1 * N1 * M2;
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ck::index_t stride_E_N1 = M2;
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// D = [0, N0, 0, N1, 0]
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ck::index_t stride_D_M0 = 0;
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ck::index_t stride_D_M1 = 0;
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ck::index_t stride_D_M2 = 0;
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ck::index_t stride_D_N0 = N1;
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ck::index_t stride_D_N1 = 1;
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#else
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// D = [0, 0, 0, N0, N1]
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ck::index_t stride_D_M0 = 0;
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ck::index_t stride_D_M1 = 0;
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ck::index_t stride_D_M2 = 0;
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ck::index_t stride_D_N0 = N1;
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ck::index_t stride_D_N1 = 1;
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// E = [M0, M1, M2, N0, N1]
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ck::index_t stride_E_M0 = M1 * M2 * N0 * N1;
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ck::index_t stride_E_M1 = M2 * N0 * N1;
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ck::index_t stride_E_M2 = N0 * N1;
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ck::index_t stride_E_N0 = N1;
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ck::index_t stride_E_N1 = 1;
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#endif
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const ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 d_grid_desc{
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M0, M1, M2, N0, N1, stride_D_M0, stride_D_M1, stride_D_M2, stride_D_N0, stride_D_N1};
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const ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 e_grid_desc{
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M0, M1, M2, N0, N1, stride_E_M0, stride_E_M1, stride_E_M2, stride_E_N0, stride_E_N1};
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if(argc == 1)
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{
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// use default case
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}
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else 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
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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: time kernel (0=no, 1=yes)\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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auto f_host_de_tensor_descriptor =
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[](ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 de_grid_desc) {
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std::size_t m0 = de_grid_desc.M0_;
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std::size_t m1 = de_grid_desc.M1_;
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std::size_t m2 = de_grid_desc.M2_;
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std::size_t n0 = de_grid_desc.N0_;
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std::size_t n1 = de_grid_desc.N1_;
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std::size_t stride_m0 = de_grid_desc.stride_M0_;
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std::size_t stride_m1 = de_grid_desc.stride_M1_;
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std::size_t stride_m2 = de_grid_desc.stride_M2_;
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std::size_t stride_n0 = de_grid_desc.stride_N0_;
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std::size_t stride_n1 = de_grid_desc.stride_N1_;
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return HostTensorDescriptor(
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std::vector<std::size_t>({m0, m1, m2, n0, n1}),
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std::vector<std::size_t>({stride_m0, stride_m1, stride_m2, stride_n0, stride_n1}));
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};
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
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Tensor<DDataType> d_m0_m1_m2_n0_n1(f_host_de_tensor_descriptor(d_grid_desc));
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Tensor<EDataType> e_m0_m1_m2_n0_n1_host_result(f_host_de_tensor_descriptor(e_grid_desc));
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Tensor<EDataType> e_m0_m1_m2_n0_n1_device_result(f_host_de_tensor_descriptor(e_grid_desc));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "d_m0_m1_m2_n0_n1: " << d_m0_m1_m2_n0_n1.mDesc << std::endl;
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std::cout << "e_m0_m1_m2_n0_n1: " << e_m0_m1_m2_n0_n1_host_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.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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d_m0_m1_m2_n0_n1.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
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break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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d_m0_m1_m2_n0_n1.GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
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}
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DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
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DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
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DeviceMem d_m0_m1_m2_n0_n1_device_buf(sizeof(DDataType) *
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d_m0_m1_m2_n0_n1.mDesc.GetElementSpace());
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DeviceMem e_m0_m1_m2_n0_n1_device_buf(sizeof(EDataType) *
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e_m0_m1_m2_n0_n1_device_result.mDesc.GetElementSpace());
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a_m_k_device_buf.ToDevice(a_m_k.mData.data());
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b_k_n_device_buf.ToDevice(b_k_n.mData.data());
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d_m0_m1_m2_n0_n1_device_buf.ToDevice(d_m0_m1_m2_n0_n1.mData.data());
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto cde_element_op = CDEElementOp{};
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// do GEMM
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auto device_op = DeviceOpInstance{};
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auto invoker = device_op.MakeInvoker();
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auto argument = device_op.MakeArgument(a_m_k_device_buf.GetDeviceBuffer(),
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b_k_n_device_buf.GetDeviceBuffer(),
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d_m0_m1_m2_n0_n1_device_buf.GetDeviceBuffer(),
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e_m0_m1_m2_n0_n1_device_buf.GetDeviceBuffer(),
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M,
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N,
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K,
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stride_A,
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stride_B,
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d_grid_desc,
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e_grid_desc,
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a_element_op,
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b_element_op,
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cde_element_op);
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if(!device_op.IsSupportedArgument(argument))
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{
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throw std::runtime_error("wrong! this device_op instance does not support this 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(2) * M * N * K;
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std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(DDataType) * N + sizeof(EDataType) * 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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<< device_op.GetTypeString() << std::endl;
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if(do_verification)
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{
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Tensor<AccDataType> c_m_n(HostTensorDescriptor(
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std::vector<std::size_t>{static_cast<std::size_t>(M), static_cast<std::size_t>(N)}));
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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AccDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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PassThrough>;
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument =
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ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
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ref_invoker.Run(ref_argument);
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for(int m0 = 0; m0 < M0; ++m0)
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for(int m1 = 0; m1 < M1; ++m1)
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for(int m2 = 0; m2 < M2; ++m2)
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for(int n0 = 0; n0 < N0; ++n0)
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for(int n1 = 0; n1 < N1; ++n1)
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{
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int m = m0 * M1 * M2 + m1 * M2 + m2;
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int n = n0 * N1 + n1;
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cde_element_op(e_m0_m1_m2_n0_n1_host_result(m0, m1, m2, n0, n1),
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ck::type_convert<EDataType>(c_m_n(m, n)),
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d_m0_m1_m2_n0_n1(m0, m1, m2, n0, n1));
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}
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e_m0_m1_m2_n0_n1_device_buf.FromDevice(e_m0_m1_m2_n0_n1_device_result.mData.data());
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return ck::utils::check_err(e_m0_m1_m2_n0_n1_device_result.mData,
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e_m0_m1_m2_n0_n1_host_result.mData)
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? 0
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: 1;
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}
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return 0;
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}
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@@ -42,3 +42,4 @@ add_subdirectory(20_convnd_bwd_weight_xdl)
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add_subdirectory(21_gemm_layernorm)
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add_subdirectory(22_cgemm)
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add_subdirectory(23_softmax)
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add_subdirectory(25_gemm_bias_c_permute)
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