mirror of
https://github.com/ROCm/composable_kernel.git
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Gemm multiple d multiple r (#335)
* Imitate XXX_gemm_multiple_d, add XXX_gemm_multiple_d_multiple_r for gemm + reduction
* Implement run of kernel
* Add example
* Fix parameter of typo
* Rewrite the reduceMax example
* Rewrite the reduceMean + reduceMeanSquare example
* Refine naming
* Refine folder name
* refine naming
* Rewrite the gemm + bias + relu + add + layernorm example
* Rewrite the gemm + layernorm example
* clang-format
* Fix bug if sync lds
* Fix compile error
[ROCm/composable_kernel commit: 6c3c06bf1f]
This commit is contained in:
rocking5566
3
example/16_gemm_multi_d_multi_reduces/CMakeLists.txt
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3
example/16_gemm_multi_d_multi_reduces/CMakeLists.txt
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@@ -0,0 +1,3 @@
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add_example_executable(example_gemm_add_add_mean_meansquare_xdl_fp16 gemm_add_add_mean_meansquare_xdl_fp16.cpp)
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add_example_executable(example_gemm_max_xdl_fp16 gemm_max_xdl_fp16.cpp)
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add_example_executable(example_gemm_mean_meansquare_xdl_fp16 gemm_mean_meansquare_xdl_fp16.cpp)
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@@ -0,0 +1,279 @@
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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_multiple_d_multiple_r_xdl_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/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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// DataType
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using ADataType = F16;
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using BDataType = F16;
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using GemmAccDataType = F32;
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using CShuffleDataType = F32;
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using D0DataType = F16;
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using D1DataType = F16;
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using DsDataType = ck::Tuple<D0DataType, D1DataType>;
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using EDataType = F16;
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using ReduceAccDataType = F32;
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using R0DataType = F32;
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using R1DataType = F32;
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using RsDataType = ck::Tuple<R0DataType, R1DataType>;
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// Layout
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using ALayout = Row;
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using BLayout = Col;
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using D1Layout = Row;
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using ELayout = D1Layout;
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// Elementwise op
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using AddAdd = ck::tensor_operation::element_wise::AddAdd;
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using Square = ck::tensor_operation::element_wise::UnarySquare;
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using Div = ck::tensor_operation::element_wise::UnaryDivide;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = AddAdd;
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using QsElementOp = ck::Tuple<PassThrough, Square>;
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using RsElementOp = ck::Tuple<Div, Div>;
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// ReduceOp
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using R0ThreadReduceOp = ck::reduce::Add;
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using R1ThreadReduceOp = ck::reduce::Add;
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using RsThreadReduceOp = ck::Tuple<R0ThreadReduceOp, R1ThreadReduceOp>;
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static constexpr auto R0GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
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static constexpr auto R1GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
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using RsGlobalReduceOp = ck::InMemoryDataOperationEnumSequence<R0GlobalReduceOp, R1GlobalReduceOp>;
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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::DeviceGemmMultipleDMultipleR_Xdl_CShuffle
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//######| ALayout| BLayout| ELayout| AData| BData| GemmAccData| CShuffle| DsData| EData| ReduceAccData| RsData| A| B| CDE| Qs| Rs| Thread| Global| 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| CDRThreadTransfer| CDE| RThreadTransfer|
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//######| | | | Type| Type| Type| DataType| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Reduce| Reduce| 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| ClusterLengths| ReduceThreadTransfer| DstScalarPerVector|
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//######| | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _MPerBlock_NPerBlock| ScalarPerVector| _MPerBlock|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | _NPerBlock| |
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< ALayout, BLayout, ELayout, ADataType, BDataType, GemmAccDataType, CShuffleDataType, DsDataType, EDataType, ReduceAccDataType, RsDataType, AElementOp, BElementOp, CDEElementOp, QsElementOp, RsElementOp, RsThreadReduceOp, RsGlobalReduceOp, 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<64, 4>, 4, 1>;
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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EDataType,
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GemmAccDataType,
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AElementOp,
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BElementOp,
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PassThrough>;
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template <typename ADataType,
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typename BDataType,
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typename D0DataType,
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typename D1DataType,
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typename EDataType,
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typename R0DataType,
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typename R1DataType>
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void DumpPerf(float ave_time, int M, int N, int K)
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{
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std::size_t flop = std::size_t(2) * M * N * K + std::size_t(2) * M * N;
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std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(D0DataType) * M * N + sizeof(D1DataType) * M * N +
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sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
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sizeof(R1DataType) * M;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gemm_gb_per_sec = gemm_num_byte / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gemm_gb_per_sec
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<< " GB/s, " << std::endl;
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}
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auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
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return HostTensorDescriptor(std::vector<std::size_t>({len}),
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std::vector<std::size_t>({stride}));
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};
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auto f_host_tensor_descriptor2d =
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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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int main()
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{
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ck::index_t M = 1024;
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ck::index_t N = 1024;
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ck::index_t K = 1024;
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ck::index_t StrideA = 1024;
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ck::index_t StrideB = 1024;
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ck::index_t StrideD0 = 0;
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ck::index_t StrideD1 = 1024;
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ck::index_t StrideE = 1024;
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
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Tensor<D0DataType> d0_n(f_host_tensor_descriptor1d(N, 1));
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Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor2d(M, N, StrideD1, D1Layout{}));
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Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
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Tensor<R0DataType> r0_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<R1DataType> r1_m(f_host_tensor_descriptor1d(M, 1));
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
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d0_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-1, 1});
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d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{-1, 1});
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem d0_device_buf(sizeof(D0DataType) * d0_n.mDesc.GetElementSpaceSize());
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DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_m_n.mDesc.GetElementSpaceSize());
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DeviceMem r0_device_buf(sizeof(R0DataType) * r0_m.mDesc.GetElementSpaceSize());
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DeviceMem r1_device_buf(sizeof(R1DataType) * r1_m.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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d0_device_buf.ToDevice(d0_n.mData.data());
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d1_device_buf.ToDevice(d1_m_n.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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auto qs_element_op = QsElementOp{};
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auto rs_element_op = RsElementOp{N, N};
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// Prepare GEMM, mean, mean_square
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auto device_op = DeviceOpInstance{};
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auto invoker = device_op.MakeInvoker();
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auto argument =
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device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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{d0_device_buf.GetDeviceBuffer(), d1_device_buf.GetDeviceBuffer()},
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e_device_buf.GetDeviceBuffer(),
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{r0_device_buf.GetDeviceBuffer(), r1_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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{StrideD0, StrideD1},
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StrideE,
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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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qs_element_op,
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rs_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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// init reducetion buffer to 0
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r0_device_buf.SetZero();
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r1_device_buf.SetZero();
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invoker.Run(argument, StreamConfig{nullptr, false});
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bool do_verification = true;
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bool pass = true;
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if(do_verification)
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{
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auto I0 = ck::Number<0>{};
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auto I1 = ck::Number<1>{};
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Tensor<EDataType> e_m_n_host(e_m_n.mDesc);
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Tensor<R0DataType> r0_m_host(r0_m.mDesc);
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Tensor<R1DataType> r1_m_host(r1_m.mDesc);
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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 = ref_gemm.MakeArgument(
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a_m_k, b_k_n, e_m_n_host, a_element_op, b_element_op, PassThrough{});
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ref_invoker.Run(ref_argument);
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auto reduce0_op = R0ThreadReduceOp{};
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auto reduce1_op = R1ThreadReduceOp{};
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for(int m = 0; m < M; ++m)
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{
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auto reduce0_acc = reduce0_op.GetIdentityValue<ReduceAccDataType>();
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auto reduce1_acc = reduce1_op.GetIdentityValue<ReduceAccDataType>();
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for(int n = 0; n < N; ++n)
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{
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ReduceAccDataType square_e_val;
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auto e_val = ck::type_convert<GemmAccDataType>(e_m_n_host(m, n));
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auto d0_val = ck::type_convert<GemmAccDataType>(d0_n(n));
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auto d1_val = ck::type_convert<GemmAccDataType>(d1_m_n(m, n));
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cde_element_op(e_val, e_val, d0_val, d1_val);
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e_m_n_host(m, n) = ck::type_convert<EDataType>(e_val);
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auto e_val_reduce = ck::type_convert<ReduceAccDataType>(e_val);
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qs_element_op[I1](square_e_val, e_val_reduce);
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reduce0_op(reduce0_acc, e_val_reduce);
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reduce1_op(reduce1_acc, square_e_val);
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}
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rs_element_op[I0](reduce0_acc, reduce0_acc);
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rs_element_op[I1](reduce1_acc, reduce1_acc);
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r0_m_host(m) = ck::type_convert<R0DataType>(reduce0_acc);
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r1_m_host(m) = ck::type_convert<R1DataType>(reduce1_acc);
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}
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e_device_buf.FromDevice(e_m_n.mData.data());
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r0_device_buf.FromDevice(r0_m.mData.data());
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r1_device_buf.FromDevice(r1_m.mData.data());
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pass = ck::utils::check_err(
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e_m_n.mData, e_m_n_host.mData, "Error: Incorrect results c", 1e-2, 1e-2);
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pass &= ck::utils::check_err(
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r0_m.mData, r0_m_host.mData, "Error: Incorrect results d0", 1e-2, 1e-2);
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pass &= ck::utils::check_err(
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r1_m.mData, r1_m_host.mData, "Error: Incorrect results d1", 1e-2, 1e-2);
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}
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bool time_kernel = true;
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if(time_kernel)
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{
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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DumpPerf<ADataType, BDataType, D0DataType, D1DataType, EDataType, R0DataType, R1DataType>(
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ave_time, M, N, K);
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}
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return pass ? 0 : 1;
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}
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227
example/16_gemm_multi_d_multi_reduces/gemm_max_xdl_fp16.cpp
Normal file
227
example/16_gemm_multi_d_multi_reduces/gemm_max_xdl_fp16.cpp
Normal file
@@ -0,0 +1,227 @@
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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_multiple_d_multiple_r_xdl_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/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 F64 = double;
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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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// DataType
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using ADataType = F16;
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using BDataType = F16;
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using GemmAccDataType = F32;
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using CShuffleDataType = F32;
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using DsDataType = ck::Tuple<>;
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using EDataType = F16;
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using ReduceAccDataType = F32;
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using R0DataType = F32;
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using RsDataType = ck::Tuple<R0DataType>;
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// Layout
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using ALayout = Row;
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using BLayout = Col;
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using ELayout = Row;
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// Elementwise op
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = PassThrough;
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using QsElementOp = ck::Tuple<PassThrough>;
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using RsElementOp = ck::Tuple<PassThrough>;
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// ReduceOp
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using RsThreadReduceOp = ck::Tuple<ck::reduce::Max>;
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using RsGlobalReduceOp =
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ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicMax>;
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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::DeviceGemmMultipleDMultipleR_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| ELayout| AData| BData| GemmAccData| CShuffle| DsData| EData| ReduceAccData| RsData| A| B| CDE| Qs| Rs| Thread| Global| 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| CDRThreadTransfer| CDE| RThreadTransfer|
|
||||
//######| | | | Type| Type| Type| DataType| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Reduce| Reduce| 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| ClusterLengths| ReduceThreadTransfer| DstScalarPerVector|
|
||||
//######| | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _MPerBlock_NPerBlock| ScalarPerVector| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | _NPerBlock| |
|
||||
< ALayout, BLayout, ELayout, ADataType, BDataType, GemmAccDataType, CShuffleDataType, DsDataType, EDataType, ReduceAccDataType, RsDataType, AElementOp, BElementOp, CDEElementOp, QsElementOp, RsElementOp, RsThreadReduceOp, RsGlobalReduceOp, 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<64, 4>, 4, 1>;
|
||||
// clang-format on
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
EDataType,
|
||||
GemmAccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CDEElementOp>;
|
||||
|
||||
template <typename ADataType, typename BDataType, typename EDataType, typename R0DataType>
|
||||
void DumpPerf(float ave_time, int M, int N, int K)
|
||||
{
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(EDataType) * M * N + sizeof(R0DataType) * M;
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
float gemm_gb_per_sec = gemm_num_byte / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gemm_gb_per_sec
|
||||
<< " GB/s, " << std::endl;
|
||||
}
|
||||
|
||||
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({len}),
|
||||
std::vector<std::size_t>({stride}));
|
||||
};
|
||||
|
||||
auto f_host_tensor_descriptor2d =
|
||||
[](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}));
|
||||
}
|
||||
};
|
||||
|
||||
int main()
|
||||
{
|
||||
ck::index_t M = 1024;
|
||||
ck::index_t N = 1024;
|
||||
ck::index_t K = 1024;
|
||||
|
||||
ck::index_t StrideA = 1024;
|
||||
ck::index_t StrideB = 1024;
|
||||
ck::index_t StrideE = 1024;
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> r0_m(f_host_tensor_descriptor1d(M, 1));
|
||||
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r0_device_buf(sizeof(R0DataType) * r0_m.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
auto qs_element_op = QsElementOp{};
|
||||
auto rs_element_op = RsElementOp{};
|
||||
|
||||
// Prepare GEMM, max
|
||||
auto device_op = DeviceOpInstance{};
|
||||
auto invoker = device_op.MakeInvoker();
|
||||
auto argument = device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
{r0_device_buf.GetDeviceBuffer()},
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
{},
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op);
|
||||
|
||||
if(!device_op.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error("wrong! this device_op instance does not support this problem");
|
||||
}
|
||||
|
||||
// [CAUSION]: launch_and_time_kernel will not initialize D.
|
||||
// If we evaluate kernel multiple time but without initialize D. Verification will fail
|
||||
r0_device_buf.SetValue(ck::NumericLimits<R0DataType>::Lowest());
|
||||
|
||||
invoker.Run(argument, StreamConfig{nullptr, false});
|
||||
|
||||
bool do_verification = true;
|
||||
bool pass = true;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
auto I0 = ck::Number<0>{};
|
||||
|
||||
Tensor<EDataType> e_m_n_host(e_m_n.mDesc);
|
||||
Tensor<R0DataType> r0_m_host(r0_m.mDesc);
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_m_k, b_k_n, e_m_n_host, a_element_op, b_element_op, cde_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
auto reduce0_op = RsThreadReduceOp{}[I0];
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
auto reduce0_acc = reduce0_op.GetIdentityValue<ReduceAccDataType>();
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
auto e_val = ck::type_convert<ReduceAccDataType>(e_m_n_host(m, n));
|
||||
reduce0_op(reduce0_acc, e_val);
|
||||
};
|
||||
|
||||
r0_m_host(m) = ck::type_convert<R0DataType>(reduce0_acc);
|
||||
}
|
||||
|
||||
e_device_buf.FromDevice(e_m_n.mData.data());
|
||||
r0_device_buf.FromDevice(r0_m.mData.data());
|
||||
|
||||
pass = ck::utils::check_err(
|
||||
e_m_n.mData, e_m_n_host.mData, "Error: Incorrect results c", 1e-2, 1e-2);
|
||||
pass &= ck::utils::check_err(
|
||||
r0_m.mData, r0_m_host.mData, "Error: Incorrect results d0", 1e-2, 1e-2);
|
||||
}
|
||||
|
||||
bool time_kernel = true;
|
||||
if(time_kernel)
|
||||
{
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
DumpPerf<ADataType, BDataType, EDataType, R0DataType>(ave_time, M, N, K);
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
@@ -0,0 +1,254 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
#include <numeric>
|
||||
#include <initializer_list>
|
||||
#include <cstdlib>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
// DataType
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using GemmAccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using DsDataType = ck::Tuple<>;
|
||||
using EDataType = F16;
|
||||
using ReduceAccDataType = F32;
|
||||
using R0DataType = F32;
|
||||
using R1DataType = F32;
|
||||
using RsDataType = ck::Tuple<R0DataType, R1DataType>;
|
||||
|
||||
// Layout
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using ELayout = Row;
|
||||
|
||||
// Elementwise op
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Square = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using Div = ck::tensor_operation::element_wise::UnaryDivide;
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = PassThrough;
|
||||
using QsElementOp = ck::Tuple<PassThrough, Square>;
|
||||
using RsElementOp = ck::Tuple<Div, Div>;
|
||||
|
||||
// ReduceOp
|
||||
using R0ThreadReduceOp = ck::reduce::Add;
|
||||
using R1ThreadReduceOp = ck::reduce::Add;
|
||||
using RsThreadReduceOp = ck::Tuple<R0ThreadReduceOp, R1ThreadReduceOp>;
|
||||
|
||||
static constexpr auto R0GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
static constexpr auto R1GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
using RsGlobalReduceOp = ck::InMemoryDataOperationEnumSequence<R0GlobalReduceOp, R1GlobalReduceOp>;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleDMultipleR_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| ELayout| AData| BData| GemmAccData| CShuffle| DsData| EData| ReduceAccData| RsData| A| B| CDE| Qs| Rs| Thread| Global| 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| CDRThreadTransfer| CDE| RThreadTransfer|
|
||||
//######| | | | Type| Type| Type| DataType| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Reduce| Reduce| 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| ClusterLengths| ReduceThreadTransfer| DstScalarPerVector|
|
||||
//######| | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _MPerBlock_NPerBlock| ScalarPerVector| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | _NPerBlock| |
|
||||
< ALayout, BLayout, ELayout, ADataType, BDataType, GemmAccDataType, CShuffleDataType, DsDataType, EDataType, ReduceAccDataType, RsDataType, AElementOp, BElementOp, CDEElementOp, QsElementOp, RsElementOp, RsThreadReduceOp, RsGlobalReduceOp, 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<64, 4>, 4, 1>;
|
||||
// clang-format on
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
EDataType,
|
||||
GemmAccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CDEElementOp>;
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename EDataType,
|
||||
typename R0DataType,
|
||||
typename R1DataType>
|
||||
void DumpPerf(float ave_time, int M, int N, int K)
|
||||
{
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
|
||||
sizeof(R1DataType) * M;
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
float gemm_gb_per_sec = gemm_num_byte / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gemm_gb_per_sec
|
||||
<< " GB/s, " << std::endl;
|
||||
}
|
||||
|
||||
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({len}),
|
||||
std::vector<std::size_t>({stride}));
|
||||
};
|
||||
|
||||
auto f_host_tensor_descriptor2d =
|
||||
[](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}));
|
||||
}
|
||||
};
|
||||
|
||||
int main()
|
||||
{
|
||||
ck::index_t M = 1024;
|
||||
ck::index_t N = 1024;
|
||||
ck::index_t K = 1024;
|
||||
|
||||
ck::index_t StrideA = 1024;
|
||||
ck::index_t StrideB = 1024;
|
||||
ck::index_t StrideE = 1024;
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> r0_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> r1_m(f_host_tensor_descriptor1d(M, 1));
|
||||
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r0_device_buf(sizeof(R0DataType) * r0_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r1_device_buf(sizeof(R1DataType) * r1_m.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
auto qs_element_op = QsElementOp{};
|
||||
auto rs_element_op = RsElementOp{N, N};
|
||||
|
||||
// Prepare GEMM, mean, mean_square
|
||||
auto device_op = DeviceOpInstance{};
|
||||
auto invoker = device_op.MakeInvoker();
|
||||
auto argument =
|
||||
device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
{r0_device_buf.GetDeviceBuffer(), r1_device_buf.GetDeviceBuffer()},
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
{},
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op);
|
||||
|
||||
if(!device_op.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error("wrong! this device_op instance does not support this problem");
|
||||
}
|
||||
|
||||
// init reducetion buffer to 0
|
||||
r0_device_buf.SetZero();
|
||||
r1_device_buf.SetZero();
|
||||
|
||||
invoker.Run(argument, StreamConfig{nullptr, false});
|
||||
|
||||
bool do_verification = true;
|
||||
bool pass = true;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
auto I0 = ck::Number<0>{};
|
||||
auto I1 = ck::Number<1>{};
|
||||
|
||||
Tensor<EDataType> e_m_n_host(e_m_n.mDesc);
|
||||
Tensor<R0DataType> r0_m_host(r0_m.mDesc);
|
||||
Tensor<R1DataType> r1_m_host(r1_m.mDesc);
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_m_k, b_k_n, e_m_n_host, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
auto reduce0_op = R0ThreadReduceOp{};
|
||||
auto reduce1_op = R1ThreadReduceOp{};
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
auto reduce0_acc = reduce0_op.GetIdentityValue<ReduceAccDataType>();
|
||||
auto reduce1_acc = reduce1_op.GetIdentityValue<ReduceAccDataType>();
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
ReduceAccDataType square_e_val;
|
||||
auto e_val = ck::type_convert<ReduceAccDataType>(e_m_n_host(m, n));
|
||||
qs_element_op[I1](square_e_val, e_val);
|
||||
|
||||
reduce0_op(reduce0_acc, e_val);
|
||||
reduce1_op(reduce1_acc, square_e_val);
|
||||
}
|
||||
|
||||
rs_element_op[I0](reduce0_acc, reduce0_acc);
|
||||
rs_element_op[I1](reduce1_acc, reduce1_acc);
|
||||
r0_m_host(m) = ck::type_convert<R0DataType>(reduce0_acc);
|
||||
r1_m_host(m) = ck::type_convert<R1DataType>(reduce1_acc);
|
||||
}
|
||||
|
||||
e_device_buf.FromDevice(e_m_n.mData.data());
|
||||
r0_device_buf.FromDevice(r0_m.mData.data());
|
||||
r1_device_buf.FromDevice(r1_m.mData.data());
|
||||
|
||||
pass = ck::utils::check_err(
|
||||
e_m_n.mData, e_m_n_host.mData, "Error: Incorrect results c", 1e-2, 1e-2);
|
||||
pass &= ck::utils::check_err(
|
||||
r0_m.mData, r0_m_host.mData, "Error: Incorrect results d0", 1e-2, 1e-2);
|
||||
pass &= ck::utils::check_err(
|
||||
r1_m.mData, r1_m_host.mData, "Error: Incorrect results d1", 1e-2, 1e-2);
|
||||
}
|
||||
|
||||
bool time_kernel = true;
|
||||
if(time_kernel)
|
||||
{
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
DumpPerf<ADataType, BDataType, EDataType, R0DataType, R1DataType>(ave_time, M, N, K);
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
Reference in New Issue
Block a user