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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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@@ -0,0 +1 @@
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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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@@ -0,0 +1,284 @@
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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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@@ -0,0 +1,57 @@
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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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#pragma once
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#include <array>
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#include "device_base.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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struct DEGridDesc_M0_M1_M2_N0_N1
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{
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ck::index_t M0_, M1_, M2_, N0_, N1_;
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ck::index_t stride_M0_, stride_M1_, stride_M2_, stride_N0_, stride_N1_;
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};
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// input : A[M, K], B[K, N],
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// input : D[M, N], ...
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// output : E[M, N]
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// C = a_op(A) * b_op(B)
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// E = cde_op(C, D)
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template <typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation>
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struct DeviceGemmBiasCPermute : public BaseOperator
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{
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virtual std::unique_ptr<BaseArgument>
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MakeArgumentPointer(const void* p_a,
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const void* p_b,
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const void* p_d,
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void* p_e,
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ck::index_t M,
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ck::index_t N,
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ck::index_t K,
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ck::index_t StrideA,
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ck::index_t StrideB,
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DEGridDesc_M0_M1_M2_N0_N1 d_gride_desc,
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DEGridDesc_M0_M1_M2_N0_N1 e_gride_desc,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op) = 0;
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virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
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};
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template <typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation>
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using DeviceGemmBiasCPermutePtr = std::unique_ptr<
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DeviceGemmBiasCPermute<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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@@ -0,0 +1,761 @@
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// SPDX-License-Identifier: MIT
|
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_bias_c_permute.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
|
||||
#include "ck/device_utility/device_prop.hpp"
|
||||
#include "ck/device_utility/kernel_launch.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <typename GridwiseGemm,
|
||||
typename FloatAB,
|
||||
typename FloatDsPointer,
|
||||
typename FloatE,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
typename AGridDesc_AK0_M_AK1,
|
||||
typename BGridDesc_BK0_N_BK1,
|
||||
typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename Block2ETileMap,
|
||||
bool HasMainKBlockLoop>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_gemm_bias_c_permute(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatDsPointer p_ds_grid,
|
||||
FloatE* __restrict__ p_e_grid,
|
||||
const AElementwiseOperation a_element_op,
|
||||
const BElementwiseOperation b_element_op,
|
||||
const CDEElementwiseOperation cde_element_op,
|
||||
const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
|
||||
const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const Block2ETileMap block_2_etile_map)
|
||||
{
|
||||
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
|
||||
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
|
||||
|
||||
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
|
||||
p_b_grid,
|
||||
p_ds_grid,
|
||||
p_e_grid,
|
||||
p_shared,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
a_grid_desc_ak0_m_ak1,
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
block_2_etile_map);
|
||||
#else
|
||||
ignore = p_a_grid;
|
||||
ignore = p_b_grid;
|
||||
ignore = p_ds_grid;
|
||||
ignore = p_e_grid;
|
||||
ignore = a_element_op;
|
||||
ignore = b_element_op;
|
||||
ignore = cde_element_op;
|
||||
ignore = a_grid_desc_ak0_m_ak1;
|
||||
ignore = b_grid_desc_bk0_n_bk1;
|
||||
ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
|
||||
ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
|
||||
ignore = block_2_etile_map;
|
||||
#endif
|
||||
}
|
||||
|
||||
} // namespace ck
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
// input : A[M, K], or A[K, N]
|
||||
// input : B[K, N], or A[N, K]
|
||||
// input : D0[M, N], D1[M, N], ...
|
||||
// output : E[M, N]
|
||||
// C = a_op(A) * b_op(B)
|
||||
// E = cde_op(C, D0, D1, ...)
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename CDELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename GemmAccDataType,
|
||||
typename CShuffleDataType,
|
||||
typename DDataType,
|
||||
typename EDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
GemmSpecialization GemmSpec,
|
||||
index_t NumGemmKPrefetchStage,
|
||||
index_t BlockSize,
|
||||
index_t MPerBlock,
|
||||
index_t NPerBlock,
|
||||
index_t KPerBlock,
|
||||
index_t AK1,
|
||||
index_t BK1,
|
||||
index_t MPerXDL,
|
||||
index_t NPerXDL,
|
||||
index_t MXdlPerWave,
|
||||
index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
index_t ABlockTransferSrcVectorDim,
|
||||
index_t ABlockTransferSrcScalarPerVector,
|
||||
index_t ABlockTransferDstScalarPerVector_AK1,
|
||||
bool ABlockLdsExtraM,
|
||||
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
index_t BBlockTransferSrcVectorDim,
|
||||
index_t BBlockTransferSrcScalarPerVector,
|
||||
index_t BBlockTransferDstScalarPerVector_BK1,
|
||||
bool BBlockLdsExtraN,
|
||||
index_t CShuffleMXdlPerWavePerShuffle,
|
||||
index_t CShuffleNXdlPerWavePerShuffle,
|
||||
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
index_t CDEBlockTransferScalarPerVector_NPerBlock,
|
||||
LoopScheduler LoopSched = make_default_loop_scheduler()>
|
||||
struct DeviceGemmBiasCPermute_Xdl : public DeviceGemmBiasCPermute<AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation>
|
||||
{
|
||||
using DeviceOp = DeviceGemmBiasCPermute_Xdl;
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
|
||||
static constexpr index_t NumDTensor = I1;
|
||||
|
||||
static auto MakeAGridDescriptor_AK0_M_AK1(index_t MRaw, index_t KRaw, index_t StrideA)
|
||||
{
|
||||
const auto a_grid_desc_mraw_kraw = [&]() {
|
||||
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
|
||||
make_tuple(StrideA, I1));
|
||||
}
|
||||
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
|
||||
make_tuple(I1, StrideA));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
|
||||
|
||||
const auto MPad = M - MRaw;
|
||||
const auto KPad = K - KRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad both M and K
|
||||
assert(K % AK1 == 0);
|
||||
|
||||
const auto AK0 = K / AK1;
|
||||
|
||||
const auto a_grid_desc_m_k =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad),
|
||||
make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
// pad M, but not K
|
||||
assert(KRaw % AK1 == 0);
|
||||
|
||||
const auto AK0 = KRaw / AK1;
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_right_pad_transform(MRaw, MPad)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// pad K, but not M
|
||||
assert(K % AK1 == 0);
|
||||
|
||||
const auto AK0 = K / AK1;
|
||||
|
||||
const auto a_grid_desc_m_k = transform_tensor_descriptor(
|
||||
a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(MRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad M or K
|
||||
assert(KRaw % AK1 == 0);
|
||||
|
||||
const auto AK0 = KRaw / AK1;
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(MRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeBGridDescriptor_BK0_N_BK1(index_t KRaw, index_t NRaw, index_t StrideB)
|
||||
{
|
||||
const auto b_grid_desc_nraw_kraw = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
|
||||
make_tuple(I1, StrideB));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
|
||||
make_tuple(StrideB, I1));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
|
||||
const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
|
||||
|
||||
const auto NPad = N - NRaw;
|
||||
const auto KPad = K - KRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad both N and K
|
||||
assert(K % BK1 == 0);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
const auto b_grid_desc_n_k =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_right_pad_transform(NRaw, NPad),
|
||||
make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
// pad N, but not K
|
||||
assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad K, but not N
|
||||
assert(K % BK1 == 0);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
const auto b_grid_desc_n_k = transform_tensor_descriptor(
|
||||
b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_pass_through_transform(NRaw), make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad N or K
|
||||
assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeEGridDescriptor_M_N(DEGridDesc_M0_M1_M2_N0_N1 d_e_grid_desc)
|
||||
{
|
||||
index_t M0 = d_e_grid_desc.M0_;
|
||||
index_t M1 = d_e_grid_desc.M1_;
|
||||
index_t M2 = d_e_grid_desc.M2_;
|
||||
index_t N0 = d_e_grid_desc.N0_;
|
||||
index_t N1 = d_e_grid_desc.N1_;
|
||||
|
||||
index_t stride_M0 = d_e_grid_desc.stride_M0_;
|
||||
index_t stride_M1 = d_e_grid_desc.stride_M1_;
|
||||
index_t stride_M2 = d_e_grid_desc.stride_M2_;
|
||||
index_t stride_N0 = d_e_grid_desc.stride_N0_;
|
||||
index_t stride_N1 = d_e_grid_desc.stride_N1_;
|
||||
|
||||
const auto MRaw = M0 * M1 * M2;
|
||||
const auto NRaw = N0 * N1;
|
||||
|
||||
const auto c_grid_desc_mraw_nraw = [&]() {
|
||||
const auto c_grid_desc_m0_m1_m2_n0_n1 = make_naive_tensor_descriptor(
|
||||
make_tuple(M0, M1, M2, N0, N1),
|
||||
make_tuple(stride_M0, stride_M1, stride_M2, stride_N0, stride_N1));
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_m0_m1_m2_n0_n1,
|
||||
make_tuple(make_merge_transform(make_tuple(M0, M1, M2)),
|
||||
make_merge_transform(make_tuple(N0, N1))),
|
||||
make_tuple(Sequence<0, 1, 2>{}, Sequence<3, 4>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}();
|
||||
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
|
||||
|
||||
const auto MPad = M - MRaw;
|
||||
const auto NPad = N - NRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad M and N
|
||||
return transform_tensor_descriptor(c_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad M, but not N
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad), make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// pad N, but not M
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_mraw_nraw,
|
||||
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad M or N
|
||||
return c_grid_desc_mraw_nraw;
|
||||
}
|
||||
}
|
||||
|
||||
using AGridDesc_AK0_M_AK1 = decltype(MakeAGridDescriptor_AK0_M_AK1(1, 1, 1));
|
||||
using BGridDesc_BK0_N_BK1 = decltype(MakeBGridDescriptor_BK0_N_BK1(1, 1, 1));
|
||||
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N(DEGridDesc_M0_M1_M2_N0_N1{}));
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemmMultipleD_k0mk1_k0nk1_mn_xdl_cshuffle<
|
||||
ADataType, // TODO: distinguish A/B datatype
|
||||
GemmAccDataType,
|
||||
CShuffleDataType,
|
||||
ck::Tuple<DDataType>,
|
||||
EDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
AGridDesc_AK0_M_AK1,
|
||||
BGridDesc_BK0_N_BK1,
|
||||
EGridDesc_M_N,
|
||||
NumGemmKPrefetchStage,
|
||||
BlockSize,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
KPerBlock,
|
||||
AK1,
|
||||
BK1,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
false,
|
||||
ABlockLdsExtraM,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
false,
|
||||
BBlockLdsExtraN,
|
||||
CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
CDEBlockTransferScalarPerVector_NPerBlock,
|
||||
LoopSched>;
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
Argument(const void* p_a_grid,
|
||||
const void* p_b_grid,
|
||||
const void* p_d_grid,
|
||||
void* p_e_grid,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
DEGridDesc_M0_M1_M2_N0_N1 d_grid_desc,
|
||||
DEGridDesc_M0_M1_M2_N0_N1 e_grid_desc,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
: p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
|
||||
p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
|
||||
p_ds_grid_{}, // FIXME
|
||||
p_e_grid_{static_cast<EDataType*>(p_e_grid)},
|
||||
a_grid_desc_ak0_m_ak1_{DeviceOp::MakeAGridDescriptor_AK0_M_AK1(MRaw, KRaw, StrideA)},
|
||||
b_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(KRaw, NRaw, StrideB)},
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
e_grid_desc_m_n_{DeviceOp::MakeEGridDescriptor_M_N(e_grid_desc)},
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
block_2_etile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op}
|
||||
{
|
||||
|
||||
if(MRaw != d_grid_desc.M0_ * d_grid_desc.M1_ * d_grid_desc.M2_)
|
||||
{
|
||||
throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
|
||||
}
|
||||
|
||||
if(NRaw != d_grid_desc.N0_ * d_grid_desc.N1_)
|
||||
{
|
||||
throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
|
||||
}
|
||||
|
||||
if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
|
||||
b_grid_desc_bk0_n_bk1_,
|
||||
e_grid_desc_m_n_,
|
||||
block_2_etile_map_))
|
||||
{
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_ =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
e_grid_desc_m_n_);
|
||||
|
||||
p_ds_grid_(I0) = static_cast<const DDataType*>(p_d_grid);
|
||||
|
||||
const auto d_grid_desc_m_n = DeviceOp::MakeEGridDescriptor_M_N(d_grid_desc);
|
||||
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_(I0) =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
d_grid_desc_m_n);
|
||||
}
|
||||
}
|
||||
|
||||
// private:
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
typename GridwiseGemm::DsGridPointer p_ds_grid_;
|
||||
EDataType* p_e_grid_;
|
||||
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
|
||||
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
|
||||
StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_; // FIXME: Ds desc may be of different
|
||||
// type from E
|
||||
EGridDesc_M_N e_grid_desc_m_n_;
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_;
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap block_2_etile_map_;
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CDEElementwiseOperation cde_element_op_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceOp::Argument;
|
||||
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.e_grid_desc_m_n_,
|
||||
arg.block_2_etile_map_))
|
||||
{
|
||||
throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
|
||||
}
|
||||
|
||||
const index_t grid_size =
|
||||
arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
|
||||
|
||||
const auto K =
|
||||
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
|
||||
|
||||
auto launch_kernel = [&](auto has_main_k_block_loop) {
|
||||
constexpr bool has_main_loop = has_main_k_block_loop.value;
|
||||
|
||||
const auto kernel = kernel_gemm_bias_c_permute<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
typename GridwiseGemm::DsGridPointer,
|
||||
EDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
DeviceOp::AGridDesc_AK0_M_AK1,
|
||||
DeviceOp::BGridDesc_BK0_N_BK1,
|
||||
ck::StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>,
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap,
|
||||
has_main_loop>;
|
||||
|
||||
return launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_ds_grid_,
|
||||
arg.p_e_grid_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.cde_element_op_,
|
||||
arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.block_2_etile_map_);
|
||||
};
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, false>{});
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.e_grid_desc_m_n_,
|
||||
arg.block_2_etile_map_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const void* p_a,
|
||||
const void* p_b,
|
||||
const void* p_d,
|
||||
void* p_e,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
DEGridDesc_M0_M1_M2_N0_N1 d_grid_desc,
|
||||
DEGridDesc_M0_M1_M2_N0_N1 e_grid_desc,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_d,
|
||||
p_e,
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
d_grid_desc,
|
||||
e_grid_desc,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
const void* p_d,
|
||||
void* p_e,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
DEGridDesc_M0_M1_M2_N0_N1 d_grid_desc,
|
||||
DEGridDesc_M0_M1_M2_N0_N1 e_grid_desc,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(p_a,
|
||||
p_b,
|
||||
p_d,
|
||||
p_e,
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
d_grid_desc,
|
||||
e_grid_desc,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmBiasCPermute_Xdl"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -11,8 +11,8 @@ namespace element_wise {
|
||||
|
||||
struct Add
|
||||
{
|
||||
template <typename T>
|
||||
__host__ __device__ constexpr void operator()(T& y, const T& x0, const T& x1) const;
|
||||
template <typename Y, typename X0, typename X1>
|
||||
__host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const;
|
||||
|
||||
template <>
|
||||
__host__ __device__ constexpr void
|
||||
@@ -28,6 +28,13 @@ struct Add
|
||||
y = x0 + x1;
|
||||
};
|
||||
|
||||
template <>
|
||||
__host__ __device__ constexpr void
|
||||
operator()<half_t>(half_t& y, const float& x0, const half_t& x1) const
|
||||
{
|
||||
y = type_convert<half_t>(x0) + x1;
|
||||
};
|
||||
|
||||
// Question: should half_t be supported ?
|
||||
template <>
|
||||
__host__ __device__ constexpr void
|
||||
|
||||
Reference in New Issue
Block a user