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
Normal file
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;
|
||||
}
|
||||
@@ -1,2 +0,0 @@
|
||||
add_example_executable(example_gemm_reduce_xdl_max_fp16 gemm_reduce_xdl_max_fp16.cpp)
|
||||
add_example_executable(example_gemm_reduce_xdl_mean_squaremean_fp16 gemm_reduce_xdl_mean_squaremean_fp16.cpp)
|
||||
@@ -1,276 +0,0 @@
|
||||
// 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_reduce_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 F64 = double;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using CDataType = F16;
|
||||
using GemmAccDataType = F32;
|
||||
using ReduceAccDataType = F32;
|
||||
using ReduceDataType = F64;
|
||||
using ReducePtrsGlobal = ck::Tuple<ReduceDataType*>;
|
||||
|
||||
using ALayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
|
||||
using CLayout = ck::tensor_layout::gemm::RowMajor;
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using ReduceOps = ck::Tuple<ck::reduce::Max>;
|
||||
using ReduceElementOps = ck::Tuple<ck::tensor_operation::element_wise::PassThrough>;
|
||||
using ReduceGlobalMemOps =
|
||||
ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicMax>;
|
||||
|
||||
static constexpr auto GemmSpecialization =
|
||||
ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| ReduceData| A| B| C| Reduce| ReduceInEleOp| ReduceAccEleOp| Reduce| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
|
||||
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Operation| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
|
||||
//######| | | | | | | | | | | 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| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< Row, Col, Row, F16, F16, F16, F32, F32, ReduceAccDataType, ReducePtrsGlobal, AElementOp, BElementOp, CElementOp, ReduceOps, ReduceElementOps, ReduceElementOps, ReduceGlobalMemOps, GemmSpecialization, 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>, 8, S<64, 4>, 4, 1>;
|
||||
// clang-format on
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
GemmAccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
|
||||
template <typename ADataType, typename BDataType, typename CDataType, typename ReduceDataType>
|
||||
void DumpGemmLayerNormPerf(float gemm_reduce_time, int M, int N, int K)
|
||||
{
|
||||
std::size_t gemm_flop = std::size_t(2) * M * N * K;
|
||||
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M;
|
||||
|
||||
float tflops = static_cast<float>(gemm_flop) / 1.E9 / gemm_reduce_time;
|
||||
float gemm_gb_per_sec = gemm_num_byte / 1.E6 / gemm_reduce_time;
|
||||
|
||||
std::cout << "gemm + reduceMax Perf: " << gemm_reduce_time << " ms, " << tflops << " TFlops, "
|
||||
<< gemm_gb_per_sec << " GB/s, " << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
// GEMM shape
|
||||
ck::index_t M = 3840;
|
||||
ck::index_t N = 4096;
|
||||
ck::index_t K = 4096;
|
||||
|
||||
ck::index_t StrideA = 4096;
|
||||
ck::index_t StrideB = 4096;
|
||||
ck::index_t StrideC = 4096;
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// do nothing
|
||||
}
|
||||
else if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
else if(argc == 10)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
|
||||
M = std::stoi(argv[4]);
|
||||
N = std::stoi(argv[5]);
|
||||
K = std::stoi(argv[6]);
|
||||
|
||||
StrideA = std::stoi(argv[7]);
|
||||
StrideB = std::stoi(argv[8]);
|
||||
StrideC = std::stoi(argv[9]);
|
||||
}
|
||||
else
|
||||
{
|
||||
printf("arg1: verification (0=no, 1=yes)\n");
|
||||
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
||||
printf("arg3: run kernel # of times (>1)\n");
|
||||
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
||||
|
||||
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> reduce_m_host_result(
|
||||
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
||||
|
||||
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> reduce_m_device_result(
|
||||
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
||||
|
||||
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
||||
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
||||
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
|
||||
std::cout << "reduce_m: " << reduce_m_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduce_device_buf(sizeof(ReduceDataType) *
|
||||
reduce_m_device_result.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 c_element_op = CElementOp{};
|
||||
auto reduce_element_op = ReduceElementOps{}[ck::Number<0>{}];
|
||||
std::array<void*, 3> gemm_element_ops = {&a_element_op, &b_element_op, &c_element_op};
|
||||
std::array<void*, 1> reduce_element_ops = {&reduce_element_op};
|
||||
std::array<void*, 1> p_reduces = {reduce_device_buf.GetDeviceBuffer()};
|
||||
|
||||
// do GEMM
|
||||
auto gemm = DeviceGemmReduceInstance{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
{},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
p_reduces,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
{},
|
||||
gemm_element_ops,
|
||||
{},
|
||||
reduce_element_ops,
|
||||
reduce_element_ops);
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
// [CAUSION]: launch_and_time_kernel will not initialize D.
|
||||
// If we evaluate kernel multiple time but without initialize D. Verification will fail
|
||||
reduce_device_buf.SetValue(ck::NumericLimits<ReduceDataType>::Lowest());
|
||||
invoker.Run(argument, StreamConfig{nullptr, false});
|
||||
|
||||
bool pass = true;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
|
||||
reduce_device_buf.FromDevice(reduce_m_device_result.mData.data());
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
auto reduce_op = ReduceOps{}[ck::Number<0>{}];
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
ReduceAccDataType reduce_acc = reduce_op.GetIdentityValue<ReduceAccDataType>();
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
ReduceAccDataType curr_val =
|
||||
ck::type_convert<ReduceAccDataType>(c_m_n_host_result(m, n));
|
||||
reduce_op(reduce_acc, curr_val);
|
||||
};
|
||||
|
||||
reduce_m_host_result(m) = reduce_acc;
|
||||
}
|
||||
|
||||
pass = ck::utils::check_err(c_m_n_device_result.mData,
|
||||
c_m_n_host_result.mData,
|
||||
"Error: Incorrect results c") &&
|
||||
ck::utils::check_err(reduce_m_device_result.mData,
|
||||
reduce_m_host_result.mData,
|
||||
"Error: Incorrect results d",
|
||||
1e-3,
|
||||
1e-3);
|
||||
}
|
||||
|
||||
if(time_kernel)
|
||||
{
|
||||
float gemm_reduceMax_ave_time = invoker.Run(argument, StreamConfig{nullptr, true});
|
||||
|
||||
DumpGemmLayerNormPerf<ADataType, BDataType, CDataType, ReduceDataType>(
|
||||
gemm_reduceMax_ave_time, M, N, K);
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
@@ -1,314 +0,0 @@
|
||||
// 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_reduce_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
#include "ck/utility/reduction_operator.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.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"
|
||||
|
||||
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;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using CDataType = F16;
|
||||
using GemmAccDataType = F32;
|
||||
using ReduceAccDataType = F32;
|
||||
using ReduceDataType = F32;
|
||||
using ReducePtrsGlobal = ck::Tuple<ReduceDataType*, ReduceDataType*>;
|
||||
|
||||
using ALayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
|
||||
using CLayout = ck::tensor_layout::gemm::RowMajor;
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using ReduceOp0 = ck::reduce::Add;
|
||||
using ReduceOp1 = ck::reduce::Add;
|
||||
using ReduceOps = ck::Tuple<ReduceOp0, ReduceOp1>;
|
||||
|
||||
using UnaryIdenticElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
|
||||
using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using ReduceInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
|
||||
using ReduceOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
|
||||
|
||||
using ReduceGlobalMemOps =
|
||||
ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
|
||||
ck::InMemoryDataOperationEnum::AtomicAdd>;
|
||||
|
||||
static constexpr auto GemmSpecialization =
|
||||
ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| ReduceDData| A| B| C| Reduce| ReduceInEleOp| ReduceOutEleOp| Reduce| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
|
||||
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Operation| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
|
||||
//######| | | | | | | | | | | 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| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< Row, Col, Row, F16, F16, F16, F32, F32, F32, ReducePtrsGlobal, AElementOp, BElementOp, CElementOp, ReduceOps, ReduceInElementOps, ReduceOutElementOps, ReduceGlobalMemOps, GemmSpecialization, 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>, 8, S<64, 4>, 4, 1>;
|
||||
// clang-format on
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
GemmAccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
|
||||
template <typename ADataType, typename BDataType, typename CDataType, typename ReduceDataType>
|
||||
void DumpGemmLayerNormPerf(float gemm_reduce_time, int M, int N, int K)
|
||||
{
|
||||
std::size_t gemm_flop = std::size_t(2) * M * N * K;
|
||||
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M +
|
||||
sizeof(ReduceDataType) * M;
|
||||
|
||||
float tflops = static_cast<float>(gemm_flop) / 1.E9 / gemm_reduce_time;
|
||||
float gemm_gb_per_sec = gemm_num_byte / 1.E6 / gemm_reduce_time;
|
||||
|
||||
std::cout << "gemm + reduce_mean + reduce_mean_square Perf: " << gemm_reduce_time << " ms, "
|
||||
<< tflops << " TFlops, " << gemm_gb_per_sec << " GB/s, " << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
// GEMM shape
|
||||
ck::index_t M = 3840;
|
||||
ck::index_t N = 4096;
|
||||
ck::index_t K = 4096;
|
||||
|
||||
ck::index_t StrideA = 4096;
|
||||
ck::index_t StrideB = 4096;
|
||||
ck::index_t StrideC = 4096;
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// do nothing
|
||||
}
|
||||
else if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
else if(argc == 10)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
|
||||
M = std::stoi(argv[4]);
|
||||
N = std::stoi(argv[5]);
|
||||
K = std::stoi(argv[6]);
|
||||
|
||||
StrideA = std::stoi(argv[7]);
|
||||
StrideB = std::stoi(argv[8]);
|
||||
StrideC = std::stoi(argv[9]);
|
||||
}
|
||||
else
|
||||
{
|
||||
printf("arg1: verification (0=no, 1=yes)\n");
|
||||
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
||||
printf("arg3: time kernel (0=n0, 1=yes)\n");
|
||||
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
||||
|
||||
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> reduce0_m_host_result(
|
||||
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
||||
Tensor<ReduceDataType> reduce1_m_host_result(
|
||||
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
||||
|
||||
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> reduce0_m_device_result(
|
||||
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
||||
Tensor<ReduceDataType> reduce1_m_device_result(
|
||||
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
||||
|
||||
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
||||
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
||||
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
|
||||
std::cout << "reduce0_m: " << reduce0_m_host_result.mDesc << std::endl;
|
||||
std::cout << "reduce1_m: " << reduce1_m_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduce0_device_buf(sizeof(ReduceDataType) *
|
||||
reduce0_m_device_result.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduce1_device_buf(sizeof(ReduceDataType) *
|
||||
reduce1_m_device_result.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 c_element_op = CElementOp{};
|
||||
std::array<void*, 3> gemm_element_ops = {&a_element_op, &b_element_op, &c_element_op};
|
||||
|
||||
auto passthrough = UnaryIdenticElementOp{};
|
||||
auto square = UnarySquareElementOp{};
|
||||
auto div = UnaryDivElementOp{N};
|
||||
std::array<void*, 2> reduce_in_element_ops = {&passthrough, &square};
|
||||
std::array<void*, 2> reduce_out_element_ops = {&div, &div};
|
||||
|
||||
std::array<void*, 2> p_reduces = {reduce0_device_buf.GetDeviceBuffer(),
|
||||
reduce1_device_buf.GetDeviceBuffer()};
|
||||
|
||||
// do GEMM
|
||||
auto gemm = DeviceGemmReduceInstance{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
{},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
p_reduces,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
{},
|
||||
gemm_element_ops,
|
||||
{},
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops);
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
// init reducetion buffer to 0
|
||||
reduce0_device_buf.SetZero();
|
||||
reduce1_device_buf.SetZero();
|
||||
|
||||
// if time_kernel == true, kernel will run multiple times. This kernel use atomic-add so result
|
||||
// will not be correct. need to set time_kernel = false for correctness test
|
||||
invoker.Run(argument, StreamConfig{nullptr, false});
|
||||
bool pass = true;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
|
||||
reduce0_device_buf.FromDevice(reduce0_m_device_result.mData.data());
|
||||
reduce1_device_buf.FromDevice(reduce1_m_device_result.mData.data());
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
auto reduce0_op = ReduceOp0{};
|
||||
auto reduce1_op = ReduceOp1{};
|
||||
|
||||
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)
|
||||
{
|
||||
auto c_val = ck::type_convert<ReduceAccDataType>(c_m_n_host_result(m, n));
|
||||
ReduceAccDataType square_c_val;
|
||||
square(square_c_val, c_val);
|
||||
|
||||
reduce0_op(reduce0_acc, c_val);
|
||||
reduce1_op(reduce1_acc, square_c_val);
|
||||
}
|
||||
|
||||
div(reduce0_acc, reduce0_acc);
|
||||
div(reduce1_acc, reduce1_acc);
|
||||
reduce0_m_host_result(m) = ck::type_convert<ReduceDataType>(reduce0_acc);
|
||||
reduce1_m_host_result(m) = ck::type_convert<ReduceDataType>(reduce1_acc);
|
||||
}
|
||||
|
||||
pass = ck::utils::check_err(c_m_n_device_result.mData,
|
||||
c_m_n_host_result.mData,
|
||||
"Error: Incorrect results c") &&
|
||||
ck::utils::check_err(reduce0_m_device_result.mData,
|
||||
reduce0_m_host_result.mData,
|
||||
"Error: Incorrect results d0",
|
||||
1e-4,
|
||||
1e-5) &&
|
||||
ck::utils::check_err(reduce1_m_device_result.mData,
|
||||
reduce1_m_host_result.mData,
|
||||
"Error: Incorrect results d1",
|
||||
1e-3,
|
||||
1e-5);
|
||||
}
|
||||
|
||||
if(time_kernel)
|
||||
{
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, true});
|
||||
|
||||
DumpGemmLayerNormPerf<ADataType, BDataType, CDataType, ReduceDataType>(ave_time, M, N, K);
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
@@ -9,7 +9,7 @@
|
||||
#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_bias_add_reduce_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_5ary_elementwise.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
@@ -28,57 +28,64 @@ 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 CDataType = F16;
|
||||
using BiasDataType = F32;
|
||||
using D0DataType = F16;
|
||||
using GemmAccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using D0DataType = F16;
|
||||
using D1DataType = F16;
|
||||
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
|
||||
using EDataType = F16;
|
||||
using ReduceAccDataType = F32;
|
||||
using ReduceDataType = F32;
|
||||
using ReducePtrsGlobal = ck::Tuple<ReduceDataType*, ReduceDataType*>;
|
||||
using R0DataType = F32;
|
||||
using R1DataType = F32;
|
||||
using RsDataType = ck::Tuple<R0DataType, R1DataType>;
|
||||
using GammaDataType = F16;
|
||||
using BetaDataType = F16;
|
||||
using LayerNormOutDataType = F16;
|
||||
using NormalizeComputeDataType = F32;
|
||||
|
||||
using ALayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
|
||||
using CLayout = ck::tensor_layout::gemm::RowMajor;
|
||||
// Layout
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using D1Layout = Row;
|
||||
using ELayout = D1Layout;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CElementOp = ck::tensor_operation::element_wise::Relu;
|
||||
using D0ElementOp = PassThrough;
|
||||
using ReduceSumOp = ck::reduce::Add;
|
||||
using ReduceOps = ck::Tuple<ReduceSumOp, ReduceSumOp>;
|
||||
// Elementwise op
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using AddReluAdd = ck::tensor_operation::element_wise::AddReluAdd;
|
||||
using Square = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using Div = ck::tensor_operation::element_wise::UnaryDivide;
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = AddReluAdd;
|
||||
using QsElementOp = ck::Tuple<PassThrough, Square>;
|
||||
using RsElementOp = ck::Tuple<Div, Div>;
|
||||
|
||||
using UnaryIdenticElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
|
||||
using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using ReduceInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
|
||||
using ReduceOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
|
||||
// ReduceOp
|
||||
using R0ThreadReduceOp = ck::reduce::Add;
|
||||
using R1ThreadReduceOp = ck::reduce::Add;
|
||||
using RsThreadReduceOp = ck::Tuple<R0ThreadReduceOp, R1ThreadReduceOp>;
|
||||
|
||||
using ReduceGlobalMemOps =
|
||||
ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
|
||||
ck::InMemoryDataOperationEnum::AtomicAdd>;
|
||||
static constexpr auto R0GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
static constexpr auto R1GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
using RsGlobalReduceOp = ck::InMemoryDataOperationEnumSequence<R0GlobalReduceOp, R1GlobalReduceOp>;
|
||||
|
||||
static constexpr auto GemmSpecialization =
|
||||
ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceGemmBiasAddReduceInstance = ck::tensor_operation::device::DeviceGemmBiasAddReduce_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| CLayout|AData| BData| CData|C0Data|C1Data| GemmAcc| CShuffle| ReduceAcc| ReduceData| A| B| C| C1| Reduce| ReduceInEleOp| ReduceAccEleOp| Reduce| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
|
||||
//######| | | | Type| Type| Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Elementwise| Operation| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
|
||||
//######| | | | | | | | | | | | | 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| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< Row, Col, Row, F16, F16, F16, F32, F16, F32, F32, F32, ReducePtrsGlobal, AElementOp, BElementOp, CElementOp, D0ElementOp, ReduceOps,ReduceInElementOps, ReduceOutElementOps, ReduceGlobalMemOps, GemmSpecialization, 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>, 8, S<64, 4>, 4, 1>;
|
||||
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,
|
||||
CDataType,
|
||||
EDataType,
|
||||
GemmAccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
@@ -88,9 +95,9 @@ using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
|
||||
|
||||
// A:x, B:E[x], C:E[x^2], D:Gamma, E:Beta , F:y
|
||||
using DeviceNormalizeInstance =
|
||||
ck::tensor_operation::device::Device5AryElementwise<CDataType,
|
||||
ReduceDataType,
|
||||
ReduceDataType,
|
||||
ck::tensor_operation::device::Device5AryElementwise<EDataType,
|
||||
R0DataType,
|
||||
R1DataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType,
|
||||
@@ -124,41 +131,31 @@ auto f_host_tensor_descriptor2d =
|
||||
}
|
||||
};
|
||||
|
||||
template <typename CDataType,
|
||||
typename ReduceDataType,
|
||||
typename AccDataType,
|
||||
typename BiasDataType,
|
||||
typename D0DataType,
|
||||
typename A_functor,
|
||||
typename B_functor,
|
||||
typename C_functor,
|
||||
typename C1_functor>
|
||||
void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
const Tensor<ADataType>& a_m_k,
|
||||
const Tensor<ADataType>& b_k_n,
|
||||
const Tensor<BiasDataType>& bias_n,
|
||||
const Tensor<D0DataType>& c1_m_n,
|
||||
const Tensor<BDataType>& b_k_n,
|
||||
const Tensor<D0DataType>& bias_n,
|
||||
const Tensor<D1DataType>& d1_m_n,
|
||||
const Tensor<GammaDataType>& gamma_n,
|
||||
const Tensor<GammaDataType>& beta_n,
|
||||
A_functor a_element_op,
|
||||
B_functor b_element_op,
|
||||
C_functor c_element_op,
|
||||
C1_functor c1_element_op,
|
||||
const Tensor<BetaDataType>& beta_n,
|
||||
AElementOp a_element_op,
|
||||
BElementOp b_element_op,
|
||||
CDEElementOp cde_element_op,
|
||||
int M,
|
||||
int N)
|
||||
{
|
||||
|
||||
int StrideC = N;
|
||||
Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<ReduceDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
auto averageOpInst = UnaryDivElementOp{N};
|
||||
int StrideE = N;
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
auto averageOpInst = Div{N};
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, e_m_n, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
@@ -166,38 +163,32 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
for(int m = 0; m < M; ++m)
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
AccDataType acc = ck::type_convert<AccDataType>(c_m_n(m, n)) +
|
||||
ck::type_convert<AccDataType>(bias_n(n));
|
||||
|
||||
AccDataType c1 = ck::type_convert<AccDataType>(c1_m_n(m, n));
|
||||
|
||||
c_element_op(acc, acc);
|
||||
c1_element_op(c1, c1);
|
||||
acc += c1;
|
||||
c_m_n(m, n) = ck::type_convert<CDataType>(acc);
|
||||
auto acc = ck::type_convert<GemmAccDataType>(e_m_n(m, n));
|
||||
cde_element_op(e_m_n(m, n), acc, bias_n(n), d1_m_n(m, n));
|
||||
}
|
||||
|
||||
// reduce_mean and reduce_square_mean
|
||||
auto reduceSumOpInst = ReduceSumOp{};
|
||||
auto r0Op = R0ThreadReduceOp{};
|
||||
auto r1Op = R1ThreadReduceOp{};
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
auto mean_acc = reduceSumOpInst.GetIdentityValue<AccDataType>();
|
||||
auto square_mean_acc = reduceSumOpInst.GetIdentityValue<AccDataType>();
|
||||
auto mean_acc = r0Op.GetIdentityValue<ReduceAccDataType>();
|
||||
auto mean_square_acc = r1Op.GetIdentityValue<ReduceAccDataType>();
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
AccDataType c_val = ck::type_convert<AccDataType>(c_m_n(m, n));
|
||||
AccDataType square_c_val = 0;
|
||||
UnarySquareElementOp{}(square_c_val, c_val);
|
||||
auto e_val = ck::type_convert<ReduceAccDataType>(e_m_n(m, n));
|
||||
ReduceAccDataType square_e_val = 0;
|
||||
Square{}(square_e_val, e_val);
|
||||
|
||||
reduceSumOpInst(mean_acc, c_val);
|
||||
reduceSumOpInst(square_mean_acc, square_c_val);
|
||||
r0Op(mean_acc, e_val);
|
||||
r1Op(mean_square_acc, square_e_val);
|
||||
}
|
||||
|
||||
averageOpInst(mean_acc, mean_acc);
|
||||
averageOpInst(square_mean_acc, square_mean_acc);
|
||||
mean_m(m) = ck::type_convert<ReduceDataType>(mean_acc);
|
||||
meanSquare_m(m) = ck::type_convert<ReduceDataType>(square_mean_acc);
|
||||
averageOpInst(mean_square_acc, mean_square_acc);
|
||||
mean_m(m) = ck::type_convert<R0DataType>(mean_acc);
|
||||
meanSquare_m(m) = ck::type_convert<R1DataType>(mean_square_acc);
|
||||
}
|
||||
|
||||
// LayerNorm
|
||||
@@ -206,24 +197,25 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
AccDataType out_acc = 0;
|
||||
NormalizeComputeDataType out_acc = 0;
|
||||
layerNormInst(out_acc,
|
||||
ck::type_convert<AccDataType>(c_m_n(m, n)),
|
||||
ck::type_convert<AccDataType>(mean_m(m)),
|
||||
ck::type_convert<AccDataType>(meanSquare_m(m)),
|
||||
ck::type_convert<AccDataType>(gamma_n(n)),
|
||||
ck::type_convert<AccDataType>(beta_n(n)));
|
||||
out_m_n(m, n) = ck::type_convert<ReduceDataType>(out_acc);
|
||||
ck::type_convert<NormalizeComputeDataType>(e_m_n(m, n)),
|
||||
ck::type_convert<NormalizeComputeDataType>(mean_m(m)),
|
||||
ck::type_convert<NormalizeComputeDataType>(meanSquare_m(m)),
|
||||
ck::type_convert<NormalizeComputeDataType>(gamma_n(n)),
|
||||
ck::type_convert<NormalizeComputeDataType>(beta_n(n)));
|
||||
out_m_n(m, n) = ck::type_convert<LayerNormOutDataType>(out_acc);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename BiasDataType,
|
||||
typename EDataType,
|
||||
typename D0DataType,
|
||||
typename ReduceDataType,
|
||||
typename D1DataType,
|
||||
typename R0DataType,
|
||||
typename R1DataType,
|
||||
typename GammaDataType,
|
||||
typename BetaDataType,
|
||||
typename NormalizeDataType>
|
||||
@@ -231,12 +223,12 @@ void DumpGemmLayerNormPerf(float gemm_reduce_time, float normalize_time, int M,
|
||||
{
|
||||
std::size_t gemm_flop = std::size_t(2) * M * N * K + std::size_t(2) * M * N;
|
||||
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(CDataType) * M * N + sizeof(BiasDataType) * M * N +
|
||||
sizeof(D0DataType) * M * N + sizeof(ReduceDataType) * M +
|
||||
sizeof(ReduceDataType) * M;
|
||||
sizeof(EDataType) * M * N + sizeof(D0DataType) * M * N +
|
||||
sizeof(D0DataType) * M * N + sizeof(R0DataType) * M +
|
||||
sizeof(R1DataType) * M;
|
||||
|
||||
std::size_t normalize_num_byte = sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M +
|
||||
sizeof(ReduceDataType) * M + sizeof(GammaDataType) * N +
|
||||
std::size_t normalize_num_byte = sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
|
||||
sizeof(R1DataType) * M + sizeof(GammaDataType) * N +
|
||||
sizeof(BetaDataType) * N + sizeof(NormalizeDataType) * M * N;
|
||||
|
||||
float tflops = static_cast<float>(gemm_flop) / 1.E9 / gemm_reduce_time;
|
||||
@@ -259,37 +251,37 @@ int main()
|
||||
|
||||
ck::index_t StrideA = 1024;
|
||||
ck::index_t StrideB = 1024;
|
||||
ck::index_t StrideC = 1024;
|
||||
ck::index_t StrideD0 = 1024;
|
||||
ck::index_t StrideD0 = 0;
|
||||
ck::index_t StrideD1 = 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<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<BiasDataType> bias_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<D0DataType> c1_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> reduceMean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<ReduceDataType> reduceMeanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<D0DataType> bias_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor2d(M, N, StrideD1, ELayout{}));
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> r0_Mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> r1_MeanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<LayerNormOutDataType> layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
|
||||
bias_n.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-1, 1});
|
||||
c1_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-5, 5});
|
||||
bias_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-1, 1});
|
||||
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{-5, 5});
|
||||
gamma_n.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-1, 1});
|
||||
beta_n.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-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 c_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem bias_device_buf(sizeof(BiasDataType) * bias_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d0_device_buf(sizeof(D0DataType) * c1_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMean_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMeanSquare_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem bias_device_buf(sizeof(D0DataType) * bias_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r0_Mean_device_buf(sizeof(R0DataType) * r0_Mean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r1_MeanSquare_device_buf(sizeof(R1DataType) *
|
||||
r1_MeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem layerNorm_device_buf(sizeof(LayerNormOutDataType) *
|
||||
@@ -298,60 +290,51 @@ int main()
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
bias_device_buf.ToDevice(bias_n.mData.data());
|
||||
d0_device_buf.ToDevice(c1_m_n.mData.data());
|
||||
d1_device_buf.ToDevice(d1_m_n.mData.data());
|
||||
gamma_device_buf.ToDevice(gamma_n.mData.data());
|
||||
beta_device_buf.ToDevice(beta_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto c_element_op = CElementOp{};
|
||||
auto d_element_op = D0ElementOp{};
|
||||
std::array<void*, 3> gemm_element_ops = {&a_element_op, &b_element_op, &c_element_op};
|
||||
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};
|
||||
|
||||
auto passthrough = UnaryIdenticElementOp{};
|
||||
auto square = UnarySquareElementOp{};
|
||||
auto div = UnaryDivElementOp{N};
|
||||
std::array<void*, 2> reduce_in_element_ops = {&passthrough, &square};
|
||||
std::array<void*, 2> reduce_out_element_ops = {&div, &div};
|
||||
|
||||
std::array<void*, 2> p_reduces = {reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer()};
|
||||
|
||||
// Prepare GEMM, reduce_mean, reduce_mean_square
|
||||
auto gemmReduce = DeviceGemmBiasAddReduceInstance{};
|
||||
// Prepare GEMM, mean, mean_square
|
||||
auto gemmReduce = DeviceOpInstance{};
|
||||
auto gemmReduce_invoker = gemmReduce.MakeInvoker();
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
bias_device_buf.GetDeviceBuffer(),
|
||||
{d0_device_buf.GetDeviceBuffer()},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
p_reduces,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
{StrideD0},
|
||||
gemm_element_ops,
|
||||
{&d_element_op},
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops);
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(
|
||||
a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{bias_device_buf.GetDeviceBuffer(), d1_device_buf.GetDeviceBuffer()},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
{r0_Mean_device_buf.GetDeviceBuffer(), r1_MeanSquare_device_buf.GetDeviceBuffer()},
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
{StrideD0, StrideD1},
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op);
|
||||
|
||||
if(!gemmReduce.IsSupportedArgument(gemmReduce_argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
throw std::runtime_error("wrong! this device_op instance does not support this problem");
|
||||
}
|
||||
|
||||
reduceMean_device_buf.SetZero();
|
||||
reduceMeanSquare_device_buf.SetZero();
|
||||
// init reducetion buffer to 0
|
||||
r0_Mean_device_buf.SetZero();
|
||||
r1_MeanSquare_device_buf.SetZero();
|
||||
|
||||
// Prepare LayerNorm
|
||||
std::array<const void*, 5> input = {c_device_buf.GetDeviceBuffer(),
|
||||
reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 5> input = {e_device_buf.GetDeviceBuffer(),
|
||||
r0_Mean_device_buf.GetDeviceBuffer(),
|
||||
r1_MeanSquare_device_buf.GetDeviceBuffer(),
|
||||
gamma_device_buf.GetDeviceBuffer(),
|
||||
beta_device_buf.GetDeviceBuffer()};
|
||||
std::array<void*, 1> output = {layerNorm_device_buf.GetDeviceBuffer()};
|
||||
@@ -361,12 +344,12 @@ int main()
|
||||
auto normalize_argument = normalize.MakeArgument(input,
|
||||
output,
|
||||
{M, N},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
{1, 0},
|
||||
{1, 0},
|
||||
{0, 1},
|
||||
{0, 1},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
NormalizeFunctor{});
|
||||
|
||||
if(!normalize.IsSupportedArgument(normalize_argument))
|
||||
@@ -383,21 +366,20 @@ int main()
|
||||
{
|
||||
// verification
|
||||
Tensor<LayerNormOutDataType> host_layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
host_gemm_layernorm<CDataType, ReduceDataType, ReduceAccDataType>(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
bias_n,
|
||||
c1_m_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
d_element_op,
|
||||
M,
|
||||
N);
|
||||
host_gemm_layernorm(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
bias_n,
|
||||
d1_m_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
M,
|
||||
N);
|
||||
|
||||
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
|
||||
pass &= ck::utils::check_err(layerNorm_m_n.mData,
|
||||
@@ -419,10 +401,11 @@ int main()
|
||||
if(time_kernel)
|
||||
DumpGemmLayerNormPerf<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
BiasDataType,
|
||||
EDataType,
|
||||
D0DataType,
|
||||
ReduceDataType,
|
||||
D1DataType,
|
||||
R0DataType,
|
||||
R1DataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType>(
|
||||
|
||||
@@ -9,7 +9,7 @@
|
||||
#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_reduce_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_5ary_elementwise.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
@@ -28,65 +28,73 @@ 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 CDataType = F16;
|
||||
using GemmAccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using DsDataType = ck::Tuple<>;
|
||||
using EDataType = F16;
|
||||
using ReduceAccDataType = F32;
|
||||
using ReduceDataType = F32;
|
||||
using ReducePtrsGlobal = ck::Tuple<ReduceDataType*, ReduceDataType*>;
|
||||
using R0DataType = F32;
|
||||
using R1DataType = F32;
|
||||
using RsDataType = ck::Tuple<R0DataType, R1DataType>;
|
||||
using GammaDataType = F16;
|
||||
using BetaDataType = F16;
|
||||
using LayerNormOutDataType = F16;
|
||||
using NormalizeComputeDataType = F32;
|
||||
|
||||
using ALayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
|
||||
using CLayout = ck::tensor_layout::gemm::RowMajor;
|
||||
// Layout
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using D1Layout = Row;
|
||||
using ELayout = D1Layout;
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using ReduceSumOp = ck::reduce::Add;
|
||||
using ReduceOps = ck::Tuple<ReduceSumOp, ReduceSumOp>;
|
||||
// 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>;
|
||||
|
||||
using UnaryIdenticElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
|
||||
using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using ReduceInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
|
||||
using ReduceOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
|
||||
// ReduceOp
|
||||
using R0ThreadReduceOp = ck::reduce::Add;
|
||||
using R1ThreadReduceOp = ck::reduce::Add;
|
||||
using RsThreadReduceOp = ck::Tuple<R0ThreadReduceOp, R1ThreadReduceOp>;
|
||||
|
||||
using ReduceGlobalMemOps =
|
||||
ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
|
||||
ck::InMemoryDataOperationEnum::AtomicAdd>;
|
||||
static constexpr auto R0GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
static constexpr auto R1GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
using RsGlobalReduceOp = ck::InMemoryDataOperationEnumSequence<R0GlobalReduceOp, R1GlobalReduceOp>;
|
||||
|
||||
static constexpr auto GemmSpecialization =
|
||||
ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| ReduceData| A| B| C| Reduce| ReduceInEleOp| ReduceAccEleOp| Reduce| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
|
||||
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Operation| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
|
||||
//######| | | | | | | | | | | 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| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< Row, Col, Row, F16, F16, F16, F32, F32, F32, ReducePtrsGlobal, AElementOp, BElementOp, CElementOp, ReduceOps,ReduceInElementOps, ReduceOutElementOps, ReduceGlobalMemOps, GemmSpecialization, 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>, 8, S<64, 4>, 4, 1>;
|
||||
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,
|
||||
CDataType,
|
||||
EDataType,
|
||||
GemmAccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
PassThrough>;
|
||||
|
||||
using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
|
||||
|
||||
// A:x, B:E[x], C:E[x^2], D:Gamma, E:Beta , F:y
|
||||
using DeviceNormalizeInstance =
|
||||
ck::tensor_operation::device::Device5AryElementwise<CDataType,
|
||||
ReduceDataType,
|
||||
ReduceDataType,
|
||||
ck::tensor_operation::device::Device5AryElementwise<EDataType,
|
||||
R0DataType,
|
||||
R1DataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType,
|
||||
@@ -120,60 +128,54 @@ auto f_host_tensor_descriptor2d =
|
||||
}
|
||||
};
|
||||
|
||||
template <typename CDataType,
|
||||
typename ReduceDataType,
|
||||
typename A_functor,
|
||||
typename B_functor,
|
||||
typename C_functor>
|
||||
void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
const Tensor<ADataType>& a_m_k,
|
||||
const Tensor<ADataType>& b_k_n,
|
||||
const Tensor<BDataType>& b_k_n,
|
||||
const Tensor<GammaDataType>& gamma_n,
|
||||
const Tensor<BetaDataType>& beta_n,
|
||||
A_functor a_element_op,
|
||||
B_functor b_element_op,
|
||||
C_functor c_element_op,
|
||||
AElementOp a_element_op,
|
||||
BElementOp b_element_op,
|
||||
CDEElementOp c_element_op,
|
||||
int M,
|
||||
int N)
|
||||
{
|
||||
using out_type = ck::remove_reference_t<decltype(out_m_n(0, 0))>;
|
||||
|
||||
int StrideC = N;
|
||||
Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<ReduceDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
auto averageOpInst = UnaryDivElementOp{N};
|
||||
int StrideE = N;
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
auto averageOpInst = Div{N};
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, c_element_op);
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, e_m_n, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
// reduce_mean and reduce_square_mean
|
||||
auto reduceSumOpInst = ReduceSumOp{};
|
||||
auto r0Op = R0ThreadReduceOp{};
|
||||
auto r1Op = R1ThreadReduceOp{};
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
auto mean_acc = reduceSumOpInst.GetIdentityValue<ReduceAccDataType>();
|
||||
auto square_mean_acc = reduceSumOpInst.GetIdentityValue<ReduceAccDataType>();
|
||||
auto mean_acc = r0Op.GetIdentityValue<ReduceAccDataType>();
|
||||
auto mean_square_acc = r1Op.GetIdentityValue<ReduceAccDataType>();
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
auto c_val = ck::type_convert<ReduceAccDataType>(c_m_n(m, n));
|
||||
auto square_c_val = reduceSumOpInst.GetIdentityValue<ReduceAccDataType>();
|
||||
auto e_val = ck::type_convert<ReduceAccDataType>(e_m_n(m, n));
|
||||
ReduceAccDataType square_e_val = 0;
|
||||
Square{}(square_e_val, e_val);
|
||||
|
||||
UnarySquareElementOp{}(square_c_val, c_val);
|
||||
|
||||
reduceSumOpInst(mean_acc, c_val);
|
||||
reduceSumOpInst(square_mean_acc, square_c_val);
|
||||
r0Op(mean_acc, e_val);
|
||||
r1Op(mean_square_acc, square_e_val);
|
||||
}
|
||||
|
||||
averageOpInst(mean_acc, mean_acc);
|
||||
averageOpInst(square_mean_acc, square_mean_acc);
|
||||
mean_m(m) = ck::type_convert<ReduceDataType>(mean_acc);
|
||||
meanSquare_m(m) = ck::type_convert<ReduceDataType>(square_mean_acc);
|
||||
averageOpInst(mean_square_acc, mean_square_acc);
|
||||
mean_m(m) = ck::type_convert<R0DataType>(mean_acc);
|
||||
meanSquare_m(m) = ck::type_convert<R1DataType>(mean_square_acc);
|
||||
}
|
||||
|
||||
// LayerNorm
|
||||
@@ -182,22 +184,23 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
float out_f32 = 0;
|
||||
layerNormInst(out_f32,
|
||||
static_cast<float>(c_m_n(m, n)),
|
||||
static_cast<float>(mean_m(m)),
|
||||
static_cast<float>(meanSquare_m(m)),
|
||||
static_cast<float>(gamma_n(n)),
|
||||
static_cast<float>(beta_n(n)));
|
||||
out_m_n(m, n) = static_cast<out_type>(out_f32);
|
||||
NormalizeComputeDataType out_acc = 0;
|
||||
layerNormInst(out_acc,
|
||||
ck::type_convert<NormalizeComputeDataType>(e_m_n(m, n)),
|
||||
ck::type_convert<NormalizeComputeDataType>(mean_m(m)),
|
||||
ck::type_convert<NormalizeComputeDataType>(meanSquare_m(m)),
|
||||
ck::type_convert<NormalizeComputeDataType>(gamma_n(n)),
|
||||
ck::type_convert<NormalizeComputeDataType>(beta_n(n)));
|
||||
out_m_n(m, n) = ck::type_convert<LayerNormOutDataType>(out_acc);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename ReduceDataType,
|
||||
typename EDataType,
|
||||
typename R0DataType,
|
||||
typename R1DataType,
|
||||
typename GammaDataType,
|
||||
typename BetaDataType,
|
||||
typename NormalizeDataType>
|
||||
@@ -205,11 +208,11 @@ void DumpGemmLayerNormPerf(float gemm_reduce_time, float normalize_time, int M,
|
||||
{
|
||||
std::size_t gemm_flop = std::size_t(2) * M * N * K;
|
||||
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M +
|
||||
sizeof(ReduceDataType) * M;
|
||||
sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
|
||||
sizeof(R1DataType) * M;
|
||||
|
||||
std::size_t normalize_num_btye = sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M +
|
||||
sizeof(ReduceDataType) * M + sizeof(GammaDataType) * N +
|
||||
std::size_t normalize_num_btye = sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
|
||||
sizeof(R1DataType) * M + sizeof(GammaDataType) * N +
|
||||
sizeof(BetaDataType) * N + sizeof(NormalizeDataType) * M * N;
|
||||
|
||||
float tflops = static_cast<float>(gemm_flop) / 1.E9 / gemm_reduce_time;
|
||||
@@ -232,17 +235,17 @@ int main()
|
||||
|
||||
ck::index_t StrideA = 1024;
|
||||
ck::index_t StrideB = 1024;
|
||||
ck::index_t StrideC = 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<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> reduceMean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<ReduceDataType> reduceMeanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> r0_Mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> r1_MeanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<LayerNormOutDataType> layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
|
||||
@@ -251,11 +254,10 @@ int main()
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMean_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMeanSquare_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r0_Mean_device_buf(sizeof(R0DataType) * r0_Mean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r1_MeanSquare_device_buf(sizeof(R1DataType) *
|
||||
r1_MeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem layerNorm_device_buf(sizeof(LayerNormOutDataType) *
|
||||
@@ -266,40 +268,33 @@ int main()
|
||||
gamma_device_buf.ToDevice(gamma_n.mData.data());
|
||||
beta_device_buf.ToDevice(beta_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto c_element_op = CElementOp{};
|
||||
std::array<void*, 3> gemm_element_ops = {&a_element_op, &b_element_op, &c_element_op};
|
||||
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};
|
||||
|
||||
auto passthrough = UnaryIdenticElementOp{};
|
||||
auto square = UnarySquareElementOp{};
|
||||
auto div = UnaryDivElementOp{N};
|
||||
std::array<void*, 2> reduce_in_element_ops = {&passthrough, &square};
|
||||
std::array<void*, 2> reduce_out_element_ops = {&div, &div};
|
||||
|
||||
std::array<void*, 2> p_reduces = {reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer()};
|
||||
|
||||
// Prepare GEMM, reduce_mean, reduce_mean_square
|
||||
auto gemmReduce = DeviceGemmReduceInstance{};
|
||||
// Prepare GEMM, mean, mean_square
|
||||
auto gemmReduce = DeviceOpInstance{};
|
||||
auto gemmReduce_invoker = gemmReduce.MakeInvoker();
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
{},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
p_reduces,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
{},
|
||||
gemm_element_ops,
|
||||
{},
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops);
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(
|
||||
a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
{r0_Mean_device_buf.GetDeviceBuffer(), r1_MeanSquare_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(!gemmReduce.IsSupportedArgument(gemmReduce_argument))
|
||||
{
|
||||
@@ -308,13 +303,13 @@ int main()
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
reduceMean_device_buf.SetZero();
|
||||
reduceMeanSquare_device_buf.SetZero();
|
||||
r0_Mean_device_buf.SetZero();
|
||||
r1_MeanSquare_device_buf.SetZero();
|
||||
|
||||
// Prepare LayerNorm
|
||||
std::array<const void*, 5> input = {c_device_buf.GetDeviceBuffer(),
|
||||
reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 5> input = {e_device_buf.GetDeviceBuffer(),
|
||||
r0_Mean_device_buf.GetDeviceBuffer(),
|
||||
r1_MeanSquare_device_buf.GetDeviceBuffer(),
|
||||
gamma_device_buf.GetDeviceBuffer(),
|
||||
beta_device_buf.GetDeviceBuffer()};
|
||||
std::array<void*, 1> output = {layerNorm_device_buf.GetDeviceBuffer()};
|
||||
@@ -324,12 +319,12 @@ int main()
|
||||
auto normalize_argument = normalize.MakeArgument(input,
|
||||
output,
|
||||
{M, N},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
{1, 0},
|
||||
{1, 0},
|
||||
{0, 1},
|
||||
{0, 1},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
NormalizeFunctor{});
|
||||
|
||||
if(!normalize.IsSupportedArgument(normalize_argument))
|
||||
@@ -346,18 +341,18 @@ int main()
|
||||
{
|
||||
// verification
|
||||
Tensor<LayerNormOutDataType> host_layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
host_gemm_layernorm<CDataType, ReduceDataType>(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
M,
|
||||
N);
|
||||
host_gemm_layernorm(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
M,
|
||||
N);
|
||||
|
||||
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
|
||||
pass &= ck::utils::check_err(layerNorm_m_n.mData,
|
||||
@@ -379,8 +374,9 @@ int main()
|
||||
if(time_kernel)
|
||||
DumpGemmLayerNormPerf<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
ReduceDataType,
|
||||
EDataType,
|
||||
R0DataType,
|
||||
R1DataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType>(
|
||||
|
||||
@@ -30,7 +30,7 @@ add_subdirectory(12_reduce)
|
||||
add_subdirectory(13_pool2d_fwd)
|
||||
add_subdirectory(14_gemm_xdl_requant_relu_requant)
|
||||
add_subdirectory(15_grouped_gemm)
|
||||
add_subdirectory(16_gemm_reduce)
|
||||
add_subdirectory(16_gemm_multi_d_multi_reduces)
|
||||
add_subdirectory(17_convnd_bwd_data)
|
||||
add_subdirectory(18_batched_gemm_reduce)
|
||||
add_subdirectory(19_binary_elementwise)
|
||||
|
||||
Reference in New Issue
Block a user