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)
|
||||
|
||||
@@ -0,0 +1,85 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "device_base.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
// FIXME: DeviceGemmReduce type need to well define the problem
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename DELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType,
|
||||
typename RsDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
typename QsElementwiseOperation,
|
||||
typename RsElementwiseOperation>
|
||||
struct DeviceGemmMultipleDMultipleR : public BaseOperator
|
||||
{
|
||||
static constexpr index_t NumDTensor = DsDataType::Size();
|
||||
static constexpr index_t NumRTensor = RsDataType::Size();
|
||||
|
||||
virtual std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
std::array<const void*, NumDTensor> p_ds,
|
||||
void* p_e,
|
||||
std::array<void*, NumRTensor> p_rs,
|
||||
ck::index_t M,
|
||||
ck::index_t N,
|
||||
ck::index_t K,
|
||||
ck::index_t StrideA,
|
||||
ck::index_t StrideB,
|
||||
std::array<ck::index_t, NumDTensor> StrideDs,
|
||||
ck::index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op,
|
||||
QsElementwiseOperation qs_element_op,
|
||||
RsElementwiseOperation rs_element_op) = 0;
|
||||
|
||||
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
|
||||
};
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename DELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType,
|
||||
typename RsDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
typename QsElementwiseOperation,
|
||||
typename RsElementwiseOperation>
|
||||
using DeviceGemmMultipleDMultipleRPtr =
|
||||
std::unique_ptr<DeviceGemmMultipleDMultipleR<ALayout,
|
||||
BLayout,
|
||||
DELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
RsDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
QsElementwiseOperation,
|
||||
RsElementwiseOperation>>;
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,873 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_multiple_r.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <typename GridwiseGemm,
|
||||
typename FloatAB,
|
||||
typename FloatDsPointer,
|
||||
typename FloatE,
|
||||
typename FloatRsPointer,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
typename QsElementwiseOperation,
|
||||
typename RsElementwiseOperation,
|
||||
typename AGridDesc_AK0_M_AK1,
|
||||
typename BGridDesc_BK0_N_BK1,
|
||||
typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename RsGridDescriptor_MBlock_MPerBlock,
|
||||
typename Block2ETileMap,
|
||||
bool HasMainKBlockLoop>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_gemm_multiple_d_multiple_r_xdl_cshuffle(
|
||||
const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatDsPointer p_ds_grid,
|
||||
FloatE* __restrict__ p_e_grid,
|
||||
FloatRsPointer p_rs_grid,
|
||||
const AElementwiseOperation a_element_op,
|
||||
const BElementwiseOperation b_element_op,
|
||||
const CDEElementwiseOperation cde_element_op,
|
||||
const QsElementwiseOperation qs_element_op,
|
||||
const RsElementwiseOperation rs_element_op,
|
||||
const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
|
||||
const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const RsGridDescriptor_MBlock_MPerBlock rs_grid_desc_mblock_mperblock,
|
||||
const Block2ETileMap block_2_etile_map)
|
||||
{
|
||||
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
|
||||
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
|
||||
|
||||
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
|
||||
p_b_grid,
|
||||
p_ds_grid,
|
||||
p_e_grid,
|
||||
p_rs_grid,
|
||||
p_shared,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op,
|
||||
a_grid_desc_ak0_m_ak1,
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
rs_grid_desc_mblock_mperblock,
|
||||
block_2_etile_map);
|
||||
#else
|
||||
ignore = p_a_grid;
|
||||
ignore = p_b_grid;
|
||||
ignore = p_ds_grid;
|
||||
ignore = p_e_grid;
|
||||
ignore = p_rs_grid;
|
||||
ignore = a_element_op;
|
||||
ignore = b_element_op;
|
||||
ignore = cde_element_op;
|
||||
ignore = qs_element_op;
|
||||
ignore = rs_element_op;
|
||||
ignore = a_grid_desc_ak0_m_ak1;
|
||||
ignore = b_grid_desc_bk0_n_bk1;
|
||||
ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
|
||||
ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
|
||||
ignore = rs_grid_desc_mblock_mperblock;
|
||||
ignore = block_2_etile_map;
|
||||
#endif
|
||||
}
|
||||
|
||||
} // namespace ck
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
// GEMM:
|
||||
// input : A[AK0, M, AK1]
|
||||
// input : B[AK0, N, AK1]
|
||||
// input : D0[M, N], D1[M, N], ...
|
||||
// output : E[M, N]
|
||||
// output : R0[M], R1[M], ...
|
||||
// C = a_op(A) * b_op(B)
|
||||
// E = cde_op(C, D0, D1, ...)
|
||||
// Q0 = reduce0(q_op0(E)), Q1 = reduce1(q_op0(E)), ...
|
||||
// R0 = r_op0(Q0), R1 = r_op1(Q1), ...
|
||||
// Assume:
|
||||
// D0, D1, ... and E have the same layout
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename DELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename GemmAccDataType,
|
||||
typename CShuffleDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType,
|
||||
typename ReduceAccDataType,
|
||||
typename RsDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
typename QsElementwiseOperation,
|
||||
typename RsElementwiseOperation,
|
||||
typename ThreadReduceOperations,
|
||||
typename RsGlobalMemoryDataOperation,
|
||||
GemmSpecialization GemmSpec,
|
||||
index_t NumGemmKPrefetchStage,
|
||||
index_t BlockSize,
|
||||
index_t MPerBlock,
|
||||
index_t NPerBlock,
|
||||
index_t KPerBlock,
|
||||
index_t AK1,
|
||||
index_t BK1,
|
||||
index_t MPerXDL,
|
||||
index_t NPerXDL,
|
||||
index_t MXdlPerWave,
|
||||
index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
index_t ABlockTransferSrcVectorDim,
|
||||
index_t ABlockTransferSrcScalarPerVector,
|
||||
index_t ABlockTransferDstScalarPerVector_AK1,
|
||||
bool ABlockLdsExtraM,
|
||||
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
index_t BBlockTransferSrcVectorDim,
|
||||
index_t BBlockTransferSrcScalarPerVector,
|
||||
index_t BBlockTransferDstScalarPerVector_BK1,
|
||||
bool BBlockLdsExtraN,
|
||||
index_t CShuffleMXdlPerWavePerShuffle,
|
||||
index_t CShuffleNXdlPerWavePerShuffle,
|
||||
typename CDRThreadTransferClusterLengths_MPerBlock_NPerBlock,
|
||||
index_t CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
index_t RThreadTransferDstScalarPerVector_MPerBlock,
|
||||
LoopScheduler LoopSched = make_default_loop_scheduler()>
|
||||
struct DeviceGemmMultipleDMultipleR_Xdl_CShuffle
|
||||
: public DeviceGemmMultipleDMultipleR<ALayout,
|
||||
BLayout,
|
||||
DELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
RsDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
QsElementwiseOperation,
|
||||
RsElementwiseOperation>
|
||||
{
|
||||
using DeviceOp = DeviceGemmMultipleDMultipleR_Xdl_CShuffle;
|
||||
|
||||
static constexpr index_t NumDTensor = DsDataType::Size();
|
||||
static constexpr index_t NumRTensor = RsDataType::Size();
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
|
||||
static auto MakeAGridDescriptor_AK0_M_AK1(index_t MRaw, index_t KRaw, index_t StrideA)
|
||||
{
|
||||
const auto a_grid_desc_mraw_kraw = [&]() {
|
||||
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
|
||||
make_tuple(StrideA, I1));
|
||||
}
|
||||
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
|
||||
make_tuple(I1, StrideA));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
|
||||
|
||||
const auto MPad = M - MRaw;
|
||||
const auto KPad = K - KRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad both M and K
|
||||
assert(K % AK1 == 0);
|
||||
|
||||
const auto AK0 = K / AK1;
|
||||
|
||||
const auto a_grid_desc_m_k =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad),
|
||||
make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
// pad M, but not K
|
||||
assert(KRaw % AK1 == 0);
|
||||
|
||||
const auto AK0 = KRaw / AK1;
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_right_pad_transform(MRaw, MPad)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// pad K, but not M
|
||||
assert(K % AK1 == 0);
|
||||
|
||||
const auto AK0 = K / AK1;
|
||||
|
||||
const auto a_grid_desc_m_k = transform_tensor_descriptor(
|
||||
a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(MRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad M or K
|
||||
assert(KRaw % AK1 == 0);
|
||||
|
||||
const auto AK0 = KRaw / AK1;
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(MRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeBGridDescriptor_BK0_N_BK1(index_t KRaw, index_t NRaw, index_t StrideB)
|
||||
{
|
||||
const auto b_grid_desc_nraw_kraw = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
|
||||
make_tuple(I1, StrideB));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
|
||||
make_tuple(StrideB, I1));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
|
||||
const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
|
||||
|
||||
const auto NPad = N - NRaw;
|
||||
const auto KPad = K - KRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad both N and K
|
||||
assert(K % BK1 == 0);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
const auto b_grid_desc_n_k =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_right_pad_transform(NRaw, NPad),
|
||||
make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
// pad N, but not K
|
||||
assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad K, but not N
|
||||
assert(K % BK1 == 0);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
const auto b_grid_desc_n_k = transform_tensor_descriptor(
|
||||
b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_pass_through_transform(NRaw), make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad N or K
|
||||
assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
|
||||
{
|
||||
const auto e_grid_desc_mraw_nraw = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, DELayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
|
||||
make_tuple(StrideE, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, DELayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
|
||||
make_tuple(I1, StrideE));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
|
||||
|
||||
const auto MPad = M - MRaw;
|
||||
const auto NPad = N - NRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad M and N
|
||||
return transform_tensor_descriptor(e_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad M, but not N
|
||||
return transform_tensor_descriptor(
|
||||
e_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad), make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// pad N, but not M
|
||||
return transform_tensor_descriptor(
|
||||
e_grid_desc_mraw_nraw,
|
||||
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad M or N
|
||||
return e_grid_desc_mraw_nraw;
|
||||
}
|
||||
}
|
||||
|
||||
// assume D is packed tensor
|
||||
static auto MakeRGridDescriptor_M(index_t MRaw)
|
||||
{
|
||||
const auto r_grid_desc_mraw = make_naive_tensor_descriptor_packed(make_tuple(MRaw));
|
||||
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto MPad = M - MRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad M
|
||||
return transform_tensor_descriptor(r_grid_desc_mraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad)),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad M
|
||||
return r_grid_desc_mraw;
|
||||
}
|
||||
}
|
||||
|
||||
using AGridDesc_AK0_M_AK1 = decltype(MakeAGridDescriptor_AK0_M_AK1(1, 1, 1));
|
||||
using BGridDesc_BK0_N_BK1 = decltype(MakeBGridDescriptor_BK0_N_BK1(1, 1, 1));
|
||||
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N(1, 1, 1));
|
||||
using RGridDesc_M = decltype(MakeRGridDescriptor_M(1));
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemmMultipleDMultipleR_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
|
||||
ADataType, // TODO: distinguish A/B datatype
|
||||
GemmAccDataType,
|
||||
CShuffleDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
ReduceAccDataType,
|
||||
RsDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
QsElementwiseOperation,
|
||||
RsElementwiseOperation,
|
||||
ThreadReduceOperations,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
RsGlobalMemoryDataOperation,
|
||||
AGridDesc_AK0_M_AK1,
|
||||
BGridDesc_BK0_N_BK1,
|
||||
EGridDesc_M_N,
|
||||
RGridDesc_M,
|
||||
NumGemmKPrefetchStage,
|
||||
BlockSize,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
KPerBlock,
|
||||
AK1,
|
||||
BK1,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
false,
|
||||
ABlockLdsExtraM,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
false,
|
||||
BBlockLdsExtraN,
|
||||
CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock,
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
RThreadTransferDstScalarPerVector_MPerBlock,
|
||||
LoopSched>;
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
Argument(const void* p_a_grid,
|
||||
const void* p_b_grid,
|
||||
std::array<const void*, NumDTensor> p_ds_grid,
|
||||
void* p_e_grid,
|
||||
std::array<void*, NumRTensor> p_rs_grid,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
std::array<index_t, NumDTensor> StrideDs,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op,
|
||||
QsElementwiseOperation qs_element_op,
|
||||
RsElementwiseOperation rs_element_op)
|
||||
: p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
|
||||
p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
|
||||
p_ds_grid_{}, // FIXME
|
||||
p_e_grid_{static_cast<EDataType*>(p_e_grid)},
|
||||
p_rs_grid_{}, // FIXME
|
||||
a_grid_desc_ak0_m_ak1_{DeviceOp::MakeAGridDescriptor_AK0_M_AK1(MRaw, KRaw, StrideA)},
|
||||
b_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(KRaw, NRaw, StrideB)},
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
e_grid_desc_m_n_{DeviceOp::MakeEGridDescriptor_M_N(MRaw, NRaw, StrideE)},
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
r_grid_desc_m_{DeviceOp::MakeRGridDescriptor_M(MRaw)},
|
||||
rs_grid_desc_mblock_mperblock_{},
|
||||
block_2_etile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op},
|
||||
qs_element_op_{qs_element_op},
|
||||
rs_element_op_{rs_element_op}
|
||||
{
|
||||
if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
|
||||
b_grid_desc_bk0_n_bk1_,
|
||||
e_grid_desc_m_n_,
|
||||
r_grid_desc_m_,
|
||||
block_2_etile_map_))
|
||||
{
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_ =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
e_grid_desc_m_n_);
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
|
||||
|
||||
p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
|
||||
|
||||
const auto d_grid_desc_m_n =
|
||||
DeviceOp::MakeEGridDescriptor_M_N(MRaw, NRaw, StrideDs[i]);
|
||||
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_(i) =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
d_grid_desc_m_n);
|
||||
});
|
||||
|
||||
static_for<0, NumRTensor, 1>{}([&](auto i) {
|
||||
using RDataType = remove_cvref_t<tuple_element_t<i.value, RsDataType>>;
|
||||
|
||||
p_rs_grid_(i) = static_cast<RDataType*>(p_rs_grid[i]);
|
||||
|
||||
rs_grid_desc_mblock_mperblock_(i) =
|
||||
GridwiseGemm::MakeRGridDescriptor_MBlock_MPerBlock(r_grid_desc_m_);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// private:
|
||||
// pointers
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
typename GridwiseGemm::DsGridPointer p_ds_grid_;
|
||||
EDataType* p_e_grid_;
|
||||
typename GridwiseGemm::RsGridPointer p_rs_grid_;
|
||||
|
||||
// tensor descriptors
|
||||
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
|
||||
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
|
||||
StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_; // FIXME: Ds desc may be of different
|
||||
// type from E
|
||||
EGridDesc_M_N e_grid_desc_m_n_;
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_;
|
||||
|
||||
RGridDesc_M r_grid_desc_m_;
|
||||
StaticallyIndexedArray<typename GridwiseGemm::RGridDescriptor_MBlock_MPerBlock, NumRTensor>
|
||||
rs_grid_desc_mblock_mperblock_;
|
||||
|
||||
// block-to-e-tile map
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap block_2_etile_map_;
|
||||
|
||||
// element-wise op
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CDEElementwiseOperation cde_element_op_;
|
||||
QsElementwiseOperation qs_element_op_;
|
||||
RsElementwiseOperation rs_element_op_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceOp::Argument;
|
||||
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.e_grid_desc_m_n_,
|
||||
arg.r_grid_desc_m_,
|
||||
arg.block_2_etile_map_))
|
||||
{
|
||||
throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
|
||||
}
|
||||
|
||||
const index_t grid_size =
|
||||
arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
|
||||
|
||||
const auto K =
|
||||
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
|
||||
|
||||
auto launch_kernel = [&](auto has_main_k_block_loop) {
|
||||
constexpr bool has_main_loop = has_main_k_block_loop.value;
|
||||
|
||||
const auto kernel = kernel_gemm_multiple_d_multiple_r_xdl_cshuffle<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
typename GridwiseGemm::DsGridPointer,
|
||||
EDataType,
|
||||
typename GridwiseGemm::RsGridPointer,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
QsElementwiseOperation,
|
||||
RsElementwiseOperation,
|
||||
DeviceOp::AGridDesc_AK0_M_AK1,
|
||||
DeviceOp::BGridDesc_BK0_N_BK1,
|
||||
ck::StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>,
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
ck::StaticallyIndexedArray<
|
||||
typename GridwiseGemm::RGridDescriptor_MBlock_MPerBlock,
|
||||
NumRTensor>,
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap,
|
||||
has_main_loop>;
|
||||
|
||||
return launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_ds_grid_,
|
||||
arg.p_e_grid_,
|
||||
arg.p_rs_grid_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.cde_element_op_,
|
||||
arg.qs_element_op_,
|
||||
arg.rs_element_op_,
|
||||
arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.rs_grid_desc_mblock_mperblock_,
|
||||
arg.block_2_etile_map_);
|
||||
};
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, false>{});
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.e_grid_desc_m_n_,
|
||||
arg.r_grid_desc_m_,
|
||||
arg.block_2_etile_map_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const void* p_a,
|
||||
const void* p_b,
|
||||
std::array<const void*, NumDTensor> p_ds,
|
||||
void* p_e,
|
||||
std::array<void*, NumRTensor> p_rs,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
std::array<index_t, NumDTensor> StrideDs,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op,
|
||||
QsElementwiseOperation qs_element_op,
|
||||
RsElementwiseOperation rs_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_ds,
|
||||
p_e,
|
||||
p_rs,
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideDs,
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
std::array<const void*, NumDTensor> p_ds,
|
||||
void* p_e,
|
||||
std::array<void*, NumRTensor> p_rs,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
std::array<index_t, NumDTensor> StrideDs,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op,
|
||||
QsElementwiseOperation qs_element_op,
|
||||
RsElementwiseOperation rs_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(p_a,
|
||||
p_b,
|
||||
p_ds,
|
||||
p_e,
|
||||
p_rs,
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideDs,
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmMultipleDMultipleR_Xdl_CShuffle"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1 << ", "
|
||||
<< getGemmSpecializationString(GemmSpec)
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -130,6 +130,35 @@ struct AddHardswishAdd
|
||||
}
|
||||
};
|
||||
|
||||
// C = A * B
|
||||
// E = C + D0 + D1
|
||||
struct AddAdd
|
||||
{
|
||||
template <typename E, typename C, typename D0, typename D1>
|
||||
__host__ __device__ void operator()(E& e, const C& c, const D0& d0, const D1& d1) const
|
||||
{
|
||||
// Only support floating so far
|
||||
static_assert(is_same<E, half_t>::value || is_same<E, float>::value ||
|
||||
is_same<E, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
static_assert(is_same<C, half_t>::value || is_same<C, float>::value ||
|
||||
is_same<C, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
static_assert(is_same<D0, half_t>::value || is_same<D0, float>::value ||
|
||||
is_same<D0, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
static_assert(is_same<D1, half_t>::value || is_same<D1, float>::value ||
|
||||
is_same<D1, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
const C y = c + type_convert<C>(d0) + type_convert<C>(d1);
|
||||
e = type_convert<E>(y);
|
||||
}
|
||||
};
|
||||
|
||||
// C = A * B
|
||||
// E = FastGelu(C + D0 + D1)
|
||||
struct AddAddFastGelu
|
||||
|
||||
@@ -0,0 +1,901 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/multi_index_transform_helper.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
namespace ck {
|
||||
template <typename FloatAB,
|
||||
typename FloatGemmAcc,
|
||||
typename FloatCShuffle,
|
||||
typename DsDataType,
|
||||
typename FloatE,
|
||||
typename FloatReduceAcc,
|
||||
typename RsDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
typename QsElementwiseOperation,
|
||||
typename RsElementwiseOperation,
|
||||
typename ThreadReduceOperations,
|
||||
InMemoryDataOperationEnum EGlobalMemoryDataOperation,
|
||||
typename RsGlobalMemoryDataOperation,
|
||||
typename AGridDesc_AK0_M_AK1,
|
||||
typename BGridDesc_BK0_N_BK1,
|
||||
typename EGridDesc_M_N,
|
||||
typename RGridDesc_M,
|
||||
index_t NumGemmKPrefetchStage,
|
||||
index_t BlockSize,
|
||||
index_t MPerBlock,
|
||||
index_t NPerBlock,
|
||||
index_t KPerBlock,
|
||||
index_t AK1Value,
|
||||
index_t BK1Value,
|
||||
index_t MPerXdl,
|
||||
index_t NPerXdl,
|
||||
index_t MXdlPerWave,
|
||||
index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
index_t ABlockTransferSrcVectorDim,
|
||||
index_t ABlockTransferSrcScalarPerVector,
|
||||
index_t ABlockTransferDstScalarPerVector_AK1,
|
||||
bool AThreadTransferSrcResetCoordinateAfterRun,
|
||||
index_t ABlockLdsExtraM,
|
||||
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
index_t BBlockTransferSrcVectorDim,
|
||||
index_t BBlockTransferSrcScalarPerVector,
|
||||
index_t BBlockTransferDstScalarPerVector_BK1,
|
||||
bool BThreadTransferSrcResetCoordinateAfterRun,
|
||||
index_t BBlockLdsExtraN,
|
||||
index_t CShuffleMXdlPerWavePerShuffle,
|
||||
index_t CShuffleNXdlPerWavePerShuffle,
|
||||
typename CDRThreadTransferClusterLengths_MPerBlock_NPerBlock,
|
||||
index_t CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
index_t RThreadTransferDstScalarPerVector_MPerBlock,
|
||||
LoopScheduler LoopSched>
|
||||
struct GridwiseGemmMultipleDMultipleR_k0mk1_k0nk1_mn_xdl_cshuffle_v1
|
||||
{
|
||||
static constexpr index_t NumDTensor = DsDataType::Size();
|
||||
static constexpr index_t NumRTensor = RsDataType::Size();
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
static constexpr auto I4 = Number<4>{};
|
||||
static constexpr auto I5 = Number<5>{};
|
||||
static constexpr auto I6 = Number<6>{};
|
||||
static constexpr auto I7 = Number<7>{};
|
||||
|
||||
// K1 should be Number<...>
|
||||
static constexpr auto AK0 = Number<KPerBlock / AK1Value>{};
|
||||
static constexpr auto BK0 = Number<KPerBlock / BK1Value>{};
|
||||
static constexpr auto AK1 = Number<AK1Value>{};
|
||||
static constexpr auto BK1 = Number<BK1Value>{};
|
||||
|
||||
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
|
||||
|
||||
using GridwiseGemmPipe = GridwiseGemmPipeline_v1<NumGemmKPrefetchStage>;
|
||||
|
||||
__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
|
||||
{
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(AK0, Number<MPerBlock>{}, AK1),
|
||||
make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
|
||||
{
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(BK0, Number<NPerBlock>{}, BK1),
|
||||
make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
|
||||
{
|
||||
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
|
||||
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
|
||||
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
make_naive_tensor_descriptor_packed(
|
||||
make_tuple(I1,
|
||||
Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
|
||||
I1,
|
||||
Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
|
||||
|
||||
return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
|
||||
}
|
||||
|
||||
// ck::Tuple<const T0DataType*, const T1DataType*, ...>
|
||||
template <typename Ts, bool isConst = true>
|
||||
static constexpr auto MakeTsGridPointer()
|
||||
{
|
||||
return generate_tuple(
|
||||
[&](auto i) {
|
||||
using T = remove_cvref_t<tuple_element_t<i.value, Ts>>;
|
||||
if constexpr(isConst)
|
||||
return static_cast<const T*>(nullptr);
|
||||
else
|
||||
return static_cast<T*>(nullptr);
|
||||
},
|
||||
Number<Ts::Size()>{});
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
|
||||
{
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
|
||||
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
|
||||
|
||||
// lds max alignment
|
||||
constexpr auto max_lds_align = math::lcm(AK1, BK1);
|
||||
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
|
||||
b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
// LDS allocation for C shuffle in LDS
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
|
||||
|
||||
constexpr auto c_block_size =
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
|
||||
|
||||
return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
|
||||
sizeof(FloatAB),
|
||||
c_block_size * sizeof(FloatCShuffle));
|
||||
}
|
||||
|
||||
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
|
||||
template <typename Block2ETileMap>
|
||||
__host__ __device__ static constexpr bool
|
||||
CheckValidity(const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
|
||||
const EGridDesc_M_N& e_grid_desc_m_n,
|
||||
const RGridDesc_M& r_grid_desc_m,
|
||||
const Block2ETileMap& block_2_etile_map)
|
||||
{
|
||||
static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
|
||||
(NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
|
||||
"Invalid tuning param!");
|
||||
|
||||
const auto M = a_grid_desc_ak0_m_ak1.GetLength(I1);
|
||||
const auto N = b_grid_desc_bk0_n_bk1.GetLength(I1);
|
||||
const auto K = a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2);
|
||||
|
||||
if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1)))
|
||||
return false;
|
||||
|
||||
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
|
||||
return false;
|
||||
|
||||
if(M != r_grid_desc_m.GetLength(I0))
|
||||
return false;
|
||||
|
||||
// check gridwise gemm pipeline
|
||||
const auto num_k_loop = K / KPerBlock;
|
||||
|
||||
if(!GridwiseGemmPipe::IsSupported(num_k_loop))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
if(!block_2_etile_map.CheckValidity(e_grid_desc_m_n))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
|
||||
return true;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
|
||||
{
|
||||
const index_t num_loop = K / KPerBlock;
|
||||
|
||||
return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
const auto M = e_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = e_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
const auto MBlock = M / MPerBlock;
|
||||
const auto NBlock = N / NPerBlock;
|
||||
|
||||
const auto e_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
|
||||
e_grid_desc_m_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
|
||||
make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
|
||||
|
||||
return e_grid_desc_mblock_mperblock_nblock_nperblock;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeRGridDescriptor_MBlock_MPerBlock(const RGridDesc_M& r_grid_desc_m)
|
||||
{
|
||||
const auto M = r_grid_desc_m.GetLength(I0);
|
||||
const auto MBlock = M / MPerBlock;
|
||||
|
||||
const auto r_grid_desc_mblock_mperblock = transform_tensor_descriptor(
|
||||
r_grid_desc_m,
|
||||
make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{}))),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1>{}));
|
||||
|
||||
return r_grid_desc_mblock_mperblock;
|
||||
}
|
||||
|
||||
// return block_id to E matrix tile idx (m0, n0) mapping
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeDefaultBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, EGridDesc_M_N>(
|
||||
e_grid_desc_m_n);
|
||||
}
|
||||
|
||||
using EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
|
||||
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}))>;
|
||||
|
||||
// Support 2 dimension in the future. Not only M
|
||||
using RGridDescriptor_MBlock_MPerBlock =
|
||||
remove_cvref_t<decltype(MakeRGridDescriptor_MBlock_MPerBlock(RGridDesc_M{}))>;
|
||||
|
||||
using DefaultBlock2ETileMap =
|
||||
remove_cvref_t<decltype(MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
|
||||
|
||||
using DsGridPointer = decltype(MakeTsGridPointer<DsDataType, true>());
|
||||
using RsGridPointer = decltype(MakeTsGridPointer<RsDataType, false>());
|
||||
|
||||
template <bool HasMainKBlockLoop, typename Block2ETileMap>
|
||||
__device__ static void
|
||||
Run(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
DsGridPointer p_ds_grid,
|
||||
FloatE* __restrict__ p_e_grid,
|
||||
RsGridPointer p_rs_grid,
|
||||
void* __restrict__ p_shared,
|
||||
const AElementwiseOperation& a_element_op,
|
||||
const BElementwiseOperation& b_element_op,
|
||||
const CDEElementwiseOperation& cde_element_op,
|
||||
const QsElementwiseOperation& qs_element_op,
|
||||
const RsElementwiseOperation& rs_element_op,
|
||||
const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
|
||||
const StaticallyIndexedArray<EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>&
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock, // FIXME: Ds desc may be of different
|
||||
const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const StaticallyIndexedArray<RGridDescriptor_MBlock_MPerBlock,
|
||||
NumRTensor>&
|
||||
rs_grid_desc_mblock_mperblock, // FIXME: Rs desc may be of different
|
||||
const Block2ETileMap& block_2_etile_map)
|
||||
{
|
||||
// FIXME - Share code with other gemm kernel
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
|
||||
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
const auto ds_grid_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_ds_grid[i],
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock[i].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
auto e_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_e_grid, e_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
auto rs_grid_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_rs_grid(i), rs_grid_desc_mblock_mperblock[i].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumRTensor>{});
|
||||
|
||||
// divide block work by [M, N]
|
||||
const auto block_work_idx =
|
||||
block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
if(!block_2_etile_map.ValidCTileIndex(
|
||||
block_work_idx,
|
||||
make_tuple(e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// HACK: this force m/n_block_data_idx_on_grid into SGPR
|
||||
const index_t m_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
|
||||
|
||||
const index_t n_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
|
||||
|
||||
// lds max alignment
|
||||
constexpr auto max_lds_align = math::lcm(AK1, BK1);
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
|
||||
|
||||
// A matrix blockwise copy
|
||||
auto a_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
|
||||
AElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
Sequence<AK0, MPerBlock, AK1>,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(a_grid_desc_ak0_m_ak1),
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
ABlockTransferSrcAccessOrder,
|
||||
Sequence<1, 0, 2>,
|
||||
ABlockTransferSrcVectorDim,
|
||||
2,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
1,
|
||||
1,
|
||||
AThreadTransferSrcResetCoordinateAfterRun,
|
||||
true,
|
||||
NumGemmKPrefetchStage>(
|
||||
a_grid_desc_ak0_m_ak1,
|
||||
make_multi_index(0, m_block_data_idx_on_grid, 0),
|
||||
a_element_op,
|
||||
a_block_desc_ak0_m_ak1,
|
||||
make_multi_index(0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
// B matrix blockwise copy
|
||||
auto b_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
|
||||
BElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
Sequence<BK0, NPerBlock, BK1>,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(b_grid_desc_bk0_n_bk1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
BBlockTransferSrcAccessOrder,
|
||||
Sequence<1, 0, 2>,
|
||||
BBlockTransferSrcVectorDim,
|
||||
2,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
1,
|
||||
1,
|
||||
BThreadTransferSrcResetCoordinateAfterRun,
|
||||
true,
|
||||
NumGemmKPrefetchStage>(
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
make_multi_index(0, n_block_data_idx_on_grid, 0),
|
||||
b_element_op,
|
||||
b_block_desc_bk0_n_bk1,
|
||||
make_multi_index(0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
// GEMM definition
|
||||
// c_mtx += transpose(a_mtx) * b_mtx
|
||||
// a_mtx[K0PerBlock, MPerBlock] is in LDS
|
||||
// b_mtx[K0PerBlock, NPerBlock] is in LDS
|
||||
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
|
||||
// register
|
||||
// sanity check
|
||||
constexpr index_t KPack = math::max(
|
||||
math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
|
||||
|
||||
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
|
||||
BlockSize,
|
||||
FloatAB,
|
||||
FloatGemmAcc,
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
MPerXdl,
|
||||
NPerXdl,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
KPack,
|
||||
LoopSched>();
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<FloatAB*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
|
||||
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<FloatAB*>(p_shared) + a_block_space_size_aligned,
|
||||
b_block_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
|
||||
|
||||
// gridwise GEMM pipeline
|
||||
const auto gridwise_gemm_pipeline =
|
||||
GridwiseGemmPipeline_v1_Selector<NumGemmKPrefetchStage, LoopSched>();
|
||||
|
||||
const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
|
||||
(a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
|
||||
KPerBlock);
|
||||
|
||||
gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
|
||||
a_block_desc_ak0_m_ak1,
|
||||
a_blockwise_copy,
|
||||
a_grid_buf,
|
||||
a_block_buf,
|
||||
a_block_slice_copy_step,
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
b_block_desc_bk0_n_bk1,
|
||||
b_blockwise_copy,
|
||||
b_grid_buf,
|
||||
b_block_buf,
|
||||
b_block_slice_copy_step,
|
||||
blockwise_gemm,
|
||||
c_thread_buf,
|
||||
num_k_block_main_loop);
|
||||
|
||||
// shuffle C + Ds + reduction + write out
|
||||
{
|
||||
static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
|
||||
NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
|
||||
"wrong!");
|
||||
|
||||
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
|
||||
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
|
||||
|
||||
// TODO: hacky, fix it!
|
||||
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
// TODO: hacky, fix it!
|
||||
// c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
|
||||
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
|
||||
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
|
||||
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
|
||||
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
|
||||
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
|
||||
constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
|
||||
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
|
||||
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
|
||||
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
|
||||
|
||||
auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<FloatCShuffle*>(p_shared),
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
|
||||
M1, // M1 = MWave
|
||||
M2, // M2 * M3 * M4 = MPerXdl
|
||||
M3,
|
||||
M4)),
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
|
||||
N1, // N1 = NWave
|
||||
N2))), // N2 = NPerXdl
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(
|
||||
Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
|
||||
|
||||
// calculate origin of thread output tensor on global memory
|
||||
// blockwise GEMM c matrix starting index
|
||||
const auto c_thread_mtx_on_block =
|
||||
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
|
||||
|
||||
const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
|
||||
const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
|
||||
|
||||
const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto m_thread_data_on_block_idx =
|
||||
m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_block));
|
||||
|
||||
const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto n_thread_data_on_block_idx =
|
||||
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_block));
|
||||
|
||||
// shuffle: threadwise copy C from VGPR to LDS
|
||||
auto c_thread_copy_vgpr_to_lds =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
|
||||
FloatCShuffle,
|
||||
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
I1,
|
||||
I1,
|
||||
M2,
|
||||
I1,
|
||||
M4,
|
||||
I1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
7,
|
||||
1,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>{
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_multi_index(0,
|
||||
0,
|
||||
m_thread_data_on_block_idx[I1],
|
||||
n_thread_data_on_block_idx[I1],
|
||||
m_thread_data_on_block_idx[I2],
|
||||
m_thread_data_on_block_idx[I3],
|
||||
m_thread_data_on_block_idx[I4],
|
||||
n_thread_data_on_block_idx[I2]),
|
||||
ck::tensor_operation::element_wise::PassThrough{}};
|
||||
|
||||
// space filling curve for threadwise C in VGPR
|
||||
constexpr auto sfc_c_vgpr =
|
||||
SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
1,
|
||||
1,
|
||||
M2,
|
||||
1,
|
||||
M4,
|
||||
1>>{};
|
||||
|
||||
// space filling curve for shuffled blockwise C in global mem
|
||||
constexpr auto sfc_der_global =
|
||||
SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
Sequence<1,
|
||||
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
|
||||
1,
|
||||
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
|
||||
|
||||
// TODO: this should be implemented as a blockwise reduction
|
||||
// LDS c_reduce_block_desc_mperblock_nperblock
|
||||
constexpr auto c_reduce_block_desc_mperblock_nperblock = transform_tensor_descriptor(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(
|
||||
make_freeze_transform(I0),
|
||||
make_pass_through_transform(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetLength(I1)),
|
||||
make_freeze_transform(I0),
|
||||
make_pass_through_transform(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetLength(I3))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<>{}, Sequence<1>{}));
|
||||
|
||||
static_assert(CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I0) *
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I1) ==
|
||||
BlockSize,
|
||||
"wrong!");
|
||||
|
||||
static_assert((CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl) %
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I0) ==
|
||||
0 &&
|
||||
(CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl) %
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I1) ==
|
||||
0,
|
||||
"wrong!");
|
||||
|
||||
constexpr index_t mreduce_per_thread =
|
||||
(CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl) /
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I0);
|
||||
|
||||
constexpr index_t nreduce_per_thread =
|
||||
(CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl) /
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I1);
|
||||
|
||||
constexpr auto c_reduce_thread_lengths_mperblock_nperblock =
|
||||
Sequence<mreduce_per_thread, nreduce_per_thread>{};
|
||||
|
||||
// VGPR cde_reduce_thread_desc_mperblock_nperblock
|
||||
constexpr auto cde_reduce_thread_desc_mperblock_nperblock =
|
||||
make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<mreduce_per_thread>{}, Number<nreduce_per_thread>{}));
|
||||
|
||||
constexpr auto r_thread_desc_mperblock =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<mreduce_per_thread>{}));
|
||||
|
||||
constexpr auto r_thread_desc_mblock_mperblock =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(I1, Number<mreduce_per_thread>{}));
|
||||
|
||||
auto e_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
|
||||
cde_reduce_thread_desc_mperblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
// reduce: threadwise copy from LDS to VGPR
|
||||
constexpr auto c_reduce_thread_cluster_desc = make_cluster_descriptor(
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock{}, Sequence<1, 0>{});
|
||||
|
||||
const auto c_reduce_thread_cluster_idx =
|
||||
c_reduce_thread_cluster_desc.CalculateBottomIndex(
|
||||
make_multi_index(get_thread_local_1d_id()));
|
||||
|
||||
const auto c_reduce_thread_data_idx_begin =
|
||||
c_reduce_thread_cluster_idx * c_reduce_thread_lengths_mperblock_nperblock;
|
||||
|
||||
// To apply D0, D1, ... and reduction.
|
||||
// Copy c shuffle from LDS back to VGPR
|
||||
auto c_reduce_thread_copy_lds_to_vgpr = ThreadwiseTensorSliceTransfer_v2<
|
||||
FloatCShuffle,
|
||||
FloatReduceAcc,
|
||||
decltype(c_reduce_block_desc_mperblock_nperblock),
|
||||
decltype(cde_reduce_thread_desc_mperblock_nperblock),
|
||||
decltype(c_reduce_thread_lengths_mperblock_nperblock),
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
1,
|
||||
true>{c_reduce_block_desc_mperblock_nperblock, c_reduce_thread_data_idx_begin};
|
||||
|
||||
// Copy result of reduction back from VGPR to global
|
||||
auto reduce_tuple_thread_copy_vgpr_to_global = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto p_r_grid = p_rs_grid[I];
|
||||
auto r_element_op = rs_element_op[I];
|
||||
auto r_grid_desc_mblock_mperblock = rs_grid_desc_mblock_mperblock[I];
|
||||
|
||||
return ThreadwiseTensorSliceTransfer_v1r3<
|
||||
FloatReduceAcc,
|
||||
remove_pointer_t<decltype(p_r_grid)>,
|
||||
decltype(r_thread_desc_mblock_mperblock),
|
||||
decltype(r_grid_desc_mblock_mperblock),
|
||||
decltype(r_element_op),
|
||||
Sequence<1, mreduce_per_thread>,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
RThreadTransferDstScalarPerVector_MPerBlock,
|
||||
RsGlobalMemoryDataOperation::At(I),
|
||||
1,
|
||||
false>{r_grid_desc_mblock_mperblock,
|
||||
make_multi_index(block_work_idx[I0], // mblock
|
||||
c_reduce_thread_data_idx_begin[I0]), // mperblock
|
||||
r_element_op};
|
||||
},
|
||||
Number<NumRTensor>{});
|
||||
|
||||
// D0, D1, ..., Dn
|
||||
constexpr auto cde_reduce_thread_desc_I1_mperblock_I1_nperblock =
|
||||
make_naive_tensor_descriptor_packed(
|
||||
make_tuple(I1, Number<mreduce_per_thread>{}, I1, Number<nreduce_per_thread>{}));
|
||||
|
||||
// FIXME: Decrease usage of VGPR
|
||||
// Apply pointwise lambda function from multi-source (Global and LDS) into VGPR
|
||||
auto ds_thread_buf = generate_tuple(
|
||||
[&](auto) {
|
||||
return make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
|
||||
cde_reduce_thread_desc_I1_mperblock_I1_nperblock.GetElementSpaceSize());
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
// Copy D0, D1, ..., Dn from global to VGPR
|
||||
auto ds_thread_copy_global_to_vgpr = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<I.value, DsDataType>>;
|
||||
return ThreadwiseTensorSliceTransfer_v2<
|
||||
DDataType,
|
||||
FloatReduceAcc,
|
||||
decltype(ds_grid_desc_mblock_mperblock_nblock_nperblock[I]),
|
||||
decltype(cde_reduce_thread_desc_I1_mperblock_I1_nperblock),
|
||||
Sequence<I1, mreduce_per_thread, I1, nreduce_per_thread>,
|
||||
Sequence<0, 1, 2, 3>,
|
||||
3,
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
1,
|
||||
true>(ds_grid_desc_mblock_mperblock_nblock_nperblock[I],
|
||||
make_multi_index(
|
||||
I0,
|
||||
m_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I0],
|
||||
I0,
|
||||
n_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I1]));
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
auto e_thread_copy_vgpr_to_global = ThreadwiseTensorSliceTransfer_v1r3<
|
||||
FloatReduceAcc,
|
||||
FloatE,
|
||||
decltype(cde_reduce_thread_desc_I1_mperblock_I1_nperblock),
|
||||
decltype(e_grid_desc_mblock_mperblock_nblock_nperblock),
|
||||
tensor_operation::element_wise::PassThrough,
|
||||
Sequence<I1, mreduce_per_thread, I1, nreduce_per_thread>, // SliceLengths
|
||||
Sequence<0, 1, 2, 3>, // DimAccessOrder
|
||||
3, // DstVectorDim
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>{
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_multi_index(I0,
|
||||
m_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I0],
|
||||
I0,
|
||||
n_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I1]),
|
||||
tensor_operation::element_wise::PassThrough{}};
|
||||
|
||||
constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
|
||||
|
||||
static_assert(num_access == sfc_der_global.GetNumOfAccess(), "wrong!");
|
||||
|
||||
static_for<0, num_access, 1>{}([&](auto access_id) {
|
||||
// make sure it's safe to read from LDS
|
||||
if constexpr(access_id > 0)
|
||||
block_sync_lds();
|
||||
|
||||
// each thread shuffle data from VGPR to LDS
|
||||
c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
|
||||
c_thread_buf,
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_shuffle_block_buf);
|
||||
|
||||
// make sure it's safe to write to LDS
|
||||
block_sync_lds();
|
||||
|
||||
// Get shuffle data from LDS to VGPR
|
||||
c_reduce_thread_copy_lds_to_vgpr.Run(c_reduce_block_desc_mperblock_nperblock,
|
||||
c_shuffle_block_buf,
|
||||
cde_reduce_thread_desc_mperblock_nperblock,
|
||||
make_tuple(I0, I0),
|
||||
e_thread_buf);
|
||||
|
||||
// Global read D0, D1, ...
|
||||
static_for<0, NumDTensor, 1>{}([&](auto Id) {
|
||||
auto& d_thread_copy_global_to_vgpr = ds_thread_copy_global_to_vgpr(Id);
|
||||
d_thread_copy_global_to_vgpr.Run(
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock[Id],
|
||||
ds_grid_buf[Id],
|
||||
cde_reduce_thread_desc_I1_mperblock_I1_nperblock,
|
||||
make_tuple(I0, I0, I0, I0),
|
||||
ds_thread_buf(Id));
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
// move on D0, D1, ...
|
||||
constexpr auto de_global_step = sfc_der_global.GetForwardStep(access_id);
|
||||
d_thread_copy_global_to_vgpr.MoveSrcSliceWindow(
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock[Id], de_global_step);
|
||||
}
|
||||
});
|
||||
|
||||
// cde_element_op(e, c, d0, d1, ...);
|
||||
static_for<0, cde_reduce_thread_desc_mperblock_nperblock.GetElementSize(), 1>{}(
|
||||
[&](auto i) {
|
||||
const auto c_ds_src_data_refs = concat_tuple_of_reference(
|
||||
tie(e_thread_buf[i]),
|
||||
generate_tie(
|
||||
[&](auto Id) -> const auto& { return ds_thread_buf[Id][i]; },
|
||||
Number<NumDTensor>{}));
|
||||
auto e_dst_data_refs = tie(e_thread_buf(i));
|
||||
unpack2(cde_element_op, e_dst_data_refs, c_ds_src_data_refs);
|
||||
});
|
||||
|
||||
// Global write E
|
||||
e_thread_copy_vgpr_to_global.Run(cde_reduce_thread_desc_I1_mperblock_I1_nperblock,
|
||||
make_tuple(I0, I0, I0, I0),
|
||||
e_thread_buf,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
e_grid_buf);
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
// move on E
|
||||
constexpr auto de_global_step = sfc_der_global.GetForwardStep(access_id);
|
||||
e_thread_copy_vgpr_to_global.MoveDstSliceWindow(
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock, de_global_step);
|
||||
}
|
||||
|
||||
// reduction
|
||||
static_for<0, NumRTensor, 1>{}([&](auto Ir) {
|
||||
auto r_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
|
||||
r_thread_desc_mperblock.GetElementSpaceSize());
|
||||
|
||||
auto& reduce_thread_copy_vgpr_to_global =
|
||||
reduce_tuple_thread_copy_vgpr_to_global(Ir);
|
||||
|
||||
using ThreadReduceOperation =
|
||||
remove_cvref_t<decltype(ThreadReduceOperations{}[Ir])>;
|
||||
|
||||
using ThreadwiseReduce =
|
||||
ThreadwiseReduction<FloatReduceAcc,
|
||||
decltype(cde_reduce_thread_desc_mperblock_nperblock),
|
||||
decltype(r_thread_desc_mperblock),
|
||||
ThreadReduceOperation,
|
||||
false>;
|
||||
|
||||
// threadwise reduction
|
||||
const auto reduce_identityVal =
|
||||
ThreadReduceOperation::template GetIdentityValue<FloatReduceAcc>();
|
||||
static_for<0, mreduce_per_thread, 1>{}(
|
||||
[&](auto I) { r_thread_buf(I) = reduce_identityVal; });
|
||||
static_for<0, mreduce_per_thread, 1>{}([&](auto im) {
|
||||
static_for<0, nreduce_per_thread, 1>{}([&](auto in) {
|
||||
constexpr auto offset =
|
||||
Number<cde_reduce_thread_desc_mperblock_nperblock.CalculateOffset(
|
||||
make_tuple(im, in))>{};
|
||||
|
||||
qs_element_op[Ir](e_thread_buf(offset), e_thread_buf(offset));
|
||||
});
|
||||
});
|
||||
ThreadwiseReduce::Reduce(e_thread_buf, r_thread_buf);
|
||||
|
||||
// gridwise reduction
|
||||
reduce_thread_copy_vgpr_to_global.Run(r_thread_desc_mblock_mperblock,
|
||||
make_tuple(I0, I0),
|
||||
r_thread_buf,
|
||||
rs_grid_desc_mblock_mperblock[Ir],
|
||||
rs_grid_buf(Ir));
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
// move on R0, R1, ...
|
||||
constexpr auto de_global_step = sfc_der_global.GetForwardStep(access_id);
|
||||
reduce_thread_copy_vgpr_to_global.MoveDstSliceWindow(
|
||||
rs_grid_desc_mblock_mperblock[Ir],
|
||||
make_tuple(de_global_step[I0], de_global_step[I1]));
|
||||
}
|
||||
});
|
||||
}); // copy c, d, e + reduction
|
||||
|
||||
} // shuffle C + Ds + reduction + write out
|
||||
} // Run
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user