mirror of
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N-D Tensor Contraction example, instance, and client example (#270)
* adding contraction * add contraction example * update examle * update example * format * update readme * clean header * clean header * contraction with multiple D * rename * fix naming issue; add instances for contraction+bilinear * change assumed virtual layout of contraction; add client example * update example * update * contraction+scale * use type_convert * rename
This commit is contained in:
Chao Liu
2
example/26_contraction/CMakeLists.txt
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2
example/26_contraction/CMakeLists.txt
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add_example_executable(example_contraction_bilinear_xdl_fp32 contraction_bilinear_xdl_fp32.cpp)
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add_example_executable(example_contraction_scale_xdl_fp32 contraction_scale_xdl_fp32.cpp)
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20
example/26_contraction/README.md
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example/26_contraction/README.md
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# Instructions for ```example_contraction_bilinear_xdl_fp32```
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## Run
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```bash
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#arg1: verification (0=no, 1=yes)
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#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
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#arg3: time kernel (0=no, 1=yes)
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./bin/example_contraction_bilinear_xdl_fp32 1 1 1
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```
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Result (MI100 @ dynammic freq, 46TFlops peak FP32)
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```
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a_ms_ks: dim 4, lengths {30, 128, 32, 64}, strides {524288, 4096, 128, 1}
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b_ks_ns: dim 4, lengths {32, 64, 32, 64}, strides {128, 1, 524288, 4096}
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c_ms_ns: dim 4, lengths {30, 128, 32, 64}, strides {524288, 4096, 128, 1}
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launch_and_time_kernel: grid_dim {240, 1, 1}, block_dim {256, 1, 1}
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Warm up 1 time
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Start running 10 times...
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Perf: 0.843286 ms, 38.1985 TFlops, 94.5014 GB/s, DeviceContractionMultipleD_Xdl_CShuffle<256, 256, 128, 16, 4, 4>
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```
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444
example/26_contraction/contraction_bilinear_xdl_fp32.cpp
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example/26_contraction/contraction_bilinear_xdl_fp32.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/device_contraction_multiple_d_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/check_err.hpp"
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#include "ck/library/host_tensor/device_memory.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/host_tensor/host_tensor_generator.hpp"
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F32 = float;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ADataType = F32;
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using BDataType = F32;
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using AccDataType = F32;
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using CShuffleDataType = F32;
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using DDataType = F32;
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using DsDataType = ck::Tuple<DDataType>;
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using EDataType = F32;
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static constexpr ck::index_t NumDimM = 2;
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static constexpr ck::index_t NumDimN = 2;
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static constexpr ck::index_t NumDimK = 2;
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CDEElementOp = ck::tensor_operation::element_wise::Bilinear;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
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// clang-format off
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using DeviceOpInstanceKKNN = ck::tensor_operation::device::
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//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
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//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>;
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using DeviceOpInstanceKNNN = ck::tensor_operation::device::
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//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
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//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 1, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4>;
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using DeviceOpInstanceMKNN = ck::tensor_operation::device::
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//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
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//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 1, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>;
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using DeviceOpInstanceMNNN = ck::tensor_operation::device::
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//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
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//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 1, 1, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4>;
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// clang-format on
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using DeviceOpInstance = DeviceOpInstanceKKNN;
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// hardcoded for NumDimM == NumDimN == NumDimK == 2
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template <ck::index_t NumDimM,
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ck::index_t NumDimN,
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ck::index_t NumDimK,
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typename ADataType,
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typename BDataType,
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typename EDataType,
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typename AccDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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ck::enable_if_t<NumDimM == 2 && NumDimN == 2 && NumDimK == 2, bool> = false>
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struct ReferenceContraction_M2_N2_K2 : public ck::tensor_operation::device::BaseOperator
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{
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// Argument
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struct Argument : public ck::tensor_operation::device::BaseArgument
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{
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Argument(const Tensor<ADataType>& a_ms_ks,
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const Tensor<BDataType>& b_ns_ks,
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Tensor<EDataType>& e_ms_ns,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op)
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: a_ms_ks_{a_ms_ks},
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b_ns_ks_{b_ns_ks},
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e_ms_ns_{e_ms_ns},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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cde_element_op_{cde_element_op}
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{
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}
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const Tensor<ADataType>& a_ms_ks_;
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const Tensor<BDataType>& b_ns_ks_;
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Tensor<EDataType>& e_ms_ns_;
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AElementwiseOperation a_element_op_;
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BElementwiseOperation b_element_op_;
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CDEElementwiseOperation cde_element_op_;
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};
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// Invoker
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struct Invoker : public ck::tensor_operation::device::BaseInvoker
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{
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using Argument = ReferenceContraction_M2_N2_K2::Argument;
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float Run(const Argument& arg)
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{
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auto f_ms_ns = [&](auto m0, auto m1, auto n0, auto n1) {
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const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[2];
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const int K1 = arg.a_ms_ks_.mDesc.GetLengths()[3];
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AccDataType v_acc = 0;
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for(int k0 = 0; k0 < K0; ++k0)
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{
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for(int k1 = 0; k1 < K1; ++k1)
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{
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AccDataType v_a;
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AccDataType v_b;
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arg.a_element_op_(
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v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, k0, k1)));
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arg.b_element_op_(
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v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, k0, k1)));
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v_acc += v_a * v_b;
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}
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}
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AccDataType v_c;
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arg.cde_element_op_(v_c, v_acc);
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arg.e_ms_ns_(m0, m1, n0, n1) = v_c;
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};
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make_ParallelTensorFunctor(f_ms_ns,
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arg.e_ms_ns_.mDesc.GetLengths()[0],
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arg.e_ms_ns_.mDesc.GetLengths()[1],
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arg.e_ms_ns_.mDesc.GetLengths()[2],
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arg.e_ms_ns_.mDesc.GetLengths()[3])(
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std::thread::hardware_concurrency());
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return 0;
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}
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float Run(const ck::tensor_operation::device::BaseArgument* p_arg,
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const StreamConfig& /* stream_config */ = StreamConfig{}) override
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{
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return Run(*dynamic_cast<const Argument*>(p_arg));
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}
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};
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static constexpr bool IsValidCompilationParameter()
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{
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// TODO: properly implement this check
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return true;
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}
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bool IsSupportedArgument(const ck::tensor_operation::device::BaseArgument*) override
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{
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return true;
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}
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static auto MakeArgument(const Tensor<ADataType>& a_ms_ks,
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const Tensor<BDataType>& b_ns_ks,
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Tensor<EDataType>& e_ms_ns,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op)
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{
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return Argument{a_ms_ks, b_ns_ks, e_ms_ns, a_element_op, b_element_op, cde_element_op};
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}
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static auto MakeInvoker() { return Invoker{}; }
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virtual std::unique_ptr<ck::tensor_operation::device::BaseInvoker> MakeInvokerPointer()
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{
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return std::make_unique<Invoker>(Invoker{});
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}
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std::string GetTypeString() const override
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{
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auto str = std::stringstream();
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// clang-format off
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str << "ReferenceContraction_M2_N2_K2"
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<< std::endl;
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// clang-format on
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return str.str();
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}
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};
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int main(int argc, char* argv[])
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = false;
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// A[M0, M1, K0, K1]
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std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
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std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
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// B[N0, N1, K0, K1]
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std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
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std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
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// D[M0, M1, N0, N1]
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std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
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std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
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// E[M0, M1, N0, N1]
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std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
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std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
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float alpha = 1.f;
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float beta = 1.f;
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if(argc == 1)
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{
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// use default case
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}
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else if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else if(argc == 28)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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const ck::index_t M0 = std::stoi(argv[4]);
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const ck::index_t M1 = std::stoi(argv[5]);
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const ck::index_t N0 = std::stoi(argv[6]);
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const ck::index_t N1 = std::stoi(argv[7]);
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const ck::index_t K0 = std::stoi(argv[8]);
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const ck::index_t K1 = std::stoi(argv[9]);
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a_ms_ks_lengths = {M0, M1, K0, K1};
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a_ms_ks_strides = {
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std::stoi(argv[10]), std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13])};
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b_ns_ks_lengths = {N0, N1, K0, K1};
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b_ns_ks_strides = {
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std::stoi(argv[14]), std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17])};
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d_ms_ns_lengths = {M0, M1, N0, N1};
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d_ms_ns_strides = {
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std::stoi(argv[18]), std::stoi(argv[19]), std::stoi(argv[20]), std::stoi(argv[21])};
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e_ms_ns_lengths = {M0, M1, N0, N1};
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||||
e_ms_ns_strides = {
|
||||
std::stoi(argv[22]), std::stoi(argv[23]), std::stoi(argv[24]), std::stoi(argv[25])};
|
||||
|
||||
alpha = std::stof(argv[26]);
|
||||
beta = std::stof(argv[27]);
|
||||
}
|
||||
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=no, 1=yes)\n");
|
||||
printf("arg4 to 7: M0, M1, N0, N1, K0, K1\n");
|
||||
printf("arg10 to 13: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
|
||||
printf("arg14 to 17: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
|
||||
printf("arg18 to 21: Stride_D_M0, Stride_D_M1, Stride_D_N0, Stride_D_N1\n");
|
||||
printf("arg22 to 25: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
|
||||
printf("arg26 to 27: alpha, beta\n");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
Tensor<ADataType> a_ms_ks(
|
||||
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()),
|
||||
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end()));
|
||||
Tensor<BDataType> b_ns_ks(
|
||||
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
|
||||
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
|
||||
Tensor<EDataType> d_ms_ns(
|
||||
std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_ms_ns_device_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
|
||||
std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
|
||||
std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
|
||||
std::cout << "d_ms_ns: " << d_ms_ns.mDesc << std::endl;
|
||||
std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.GetElementSpace());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpace());
|
||||
DeviceMem d_device_buf(sizeof(DDataType) * d_ms_ns.mDesc.GetElementSpace());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpace());
|
||||
|
||||
a_device_buf.ToDevice(a_ms_ks.mData.data());
|
||||
b_device_buf.ToDevice(b_ns_ks.mData.data());
|
||||
d_device_buf.ToDevice(d_ms_ns.mData.data());
|
||||
|
||||
// set zero
|
||||
e_device_buf.SetZero();
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{alpha, beta};
|
||||
|
||||
// device operation
|
||||
auto op = DeviceOpInstance{};
|
||||
auto invoker = op.MakeInvoker();
|
||||
auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
a_ms_ks_lengths,
|
||||
a_ms_ks_strides,
|
||||
b_ns_ks_lengths,
|
||||
b_ns_ks_strides,
|
||||
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
|
||||
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
|
||||
e_ms_ns_lengths,
|
||||
e_ms_ns_strides,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
if(!op.IsSupportedArgument(argument))
|
||||
{
|
||||
std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
|
||||
e_ms_ns_lengths.begin() + NumDimM,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
|
||||
e_ms_ns_lengths.begin() + NumDimM + NumDimN,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
|
||||
a_ms_ks_lengths.begin() + NumDimM + NumDimK,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(DDataType) * M * N + sizeof(EDataType) * M * N;
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< op.GetTypeString() << std::endl;
|
||||
|
||||
e_device_buf.FromDevice(e_ms_ns_device_result.mData.data());
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
|
||||
using ReferenceOpInstance = ReferenceContraction_M2_N2_K2<NumDimM,
|
||||
NumDimN,
|
||||
NumDimK,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceOpInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_ms_ks, b_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(size_t m0 = 0; m0 < e_ms_ns_host_result.mDesc.GetLengths()[0]; ++m0)
|
||||
{
|
||||
for(size_t m1 = 0; m1 < e_ms_ns_host_result.mDesc.GetLengths()[1]; ++m1)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++n0)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n1)
|
||||
{
|
||||
cde_element_op(e_ms_ns_host_result(m0, m1, n0, n1),
|
||||
c_ms_ns_host_result(m0, m1, n0, n1),
|
||||
d_ms_ns(m0, m1, n0, n1));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
424
example/26_contraction/contraction_scale_xdl_fp32.cpp
Normal file
424
example/26_contraction/contraction_scale_xdl_fp32.cpp
Normal file
@@ -0,0 +1,424 @@
|
||||
// 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/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_contraction_multiple_d_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/host_tensor/device_memory.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor_generator.hpp"
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using F32 = float;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using ADataType = F32;
|
||||
using BDataType = F32;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using DsDataType = ck::Tuple<>;
|
||||
using EDataType = F32;
|
||||
|
||||
static constexpr ck::index_t NumDimM = 2;
|
||||
static constexpr ck::index_t NumDimN = 2;
|
||||
static constexpr ck::index_t NumDimK = 2;
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CDEElementOp = ck::tensor_operation::element_wise::Scale;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
||||
|
||||
// clang-format off
|
||||
using DeviceOpInstanceKKNN = ck::tensor_operation::device::
|
||||
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>;
|
||||
|
||||
using DeviceOpInstanceKNNN = ck::tensor_operation::device::
|
||||
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 1, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4>;
|
||||
|
||||
using DeviceOpInstanceMKNN = ck::tensor_operation::device::
|
||||
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 1, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>;
|
||||
|
||||
using DeviceOpInstanceMNNN = ck::tensor_operation::device::
|
||||
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 1, 1, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4>;
|
||||
// clang-format on
|
||||
|
||||
using DeviceOpInstance = DeviceOpInstanceKKNN;
|
||||
|
||||
// hardcoded for NumDimM == NumDimN == NumDimK == 2
|
||||
template <ck::index_t NumDimM,
|
||||
ck::index_t NumDimN,
|
||||
ck::index_t NumDimK,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename EDataType,
|
||||
typename AccDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
ck::enable_if_t<NumDimM == 2 && NumDimN == 2 && NumDimK == 2, bool> = false>
|
||||
struct ReferenceContraction_M2_N2_K2 : public ck::tensor_operation::device::BaseOperator
|
||||
{
|
||||
// Argument
|
||||
struct Argument : public ck::tensor_operation::device::BaseArgument
|
||||
{
|
||||
Argument(const Tensor<ADataType>& a_ms_ks,
|
||||
const Tensor<BDataType>& b_ns_ks,
|
||||
Tensor<EDataType>& e_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
: a_ms_ks_{a_ms_ks},
|
||||
b_ns_ks_{b_ns_ks},
|
||||
e_ms_ns_{e_ms_ns},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op}
|
||||
{
|
||||
}
|
||||
|
||||
const Tensor<ADataType>& a_ms_ks_;
|
||||
const Tensor<BDataType>& b_ns_ks_;
|
||||
Tensor<EDataType>& e_ms_ns_;
|
||||
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CDEElementwiseOperation cde_element_op_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public ck::tensor_operation::device::BaseInvoker
|
||||
{
|
||||
using Argument = ReferenceContraction_M2_N2_K2::Argument;
|
||||
|
||||
float Run(const Argument& arg)
|
||||
{
|
||||
auto f_ms_ns = [&](auto m0, auto m1, auto n0, auto n1) {
|
||||
const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[2];
|
||||
const int K1 = arg.a_ms_ks_.mDesc.GetLengths()[3];
|
||||
|
||||
AccDataType v_acc = 0;
|
||||
|
||||
for(int k0 = 0; k0 < K0; ++k0)
|
||||
{
|
||||
for(int k1 = 0; k1 < K1; ++k1)
|
||||
{
|
||||
AccDataType v_a;
|
||||
AccDataType v_b;
|
||||
|
||||
arg.a_element_op_(
|
||||
v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, k0, k1)));
|
||||
arg.b_element_op_(
|
||||
v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, k0, k1)));
|
||||
|
||||
v_acc += v_a * v_b;
|
||||
}
|
||||
}
|
||||
|
||||
AccDataType v_c;
|
||||
|
||||
arg.cde_element_op_(v_c, v_acc);
|
||||
|
||||
arg.e_ms_ns_(m0, m1, n0, n1) = v_c;
|
||||
};
|
||||
|
||||
make_ParallelTensorFunctor(f_ms_ns,
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[0],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[1],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[2],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[3])(
|
||||
std::thread::hardware_concurrency());
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
float Run(const ck::tensor_operation::device::BaseArgument* p_arg,
|
||||
const StreamConfig& /* stream_config */ = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
bool IsSupportedArgument(const ck::tensor_operation::device::BaseArgument*) override
|
||||
{
|
||||
return true;
|
||||
}
|
||||
|
||||
static auto MakeArgument(const Tensor<ADataType>& a_ms_ks,
|
||||
const Tensor<BDataType>& b_ns_ks,
|
||||
Tensor<EDataType>& e_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
{
|
||||
return Argument{a_ms_ks, b_ns_ks, e_ms_ns, a_element_op, b_element_op, cde_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
virtual std::unique_ptr<ck::tensor_operation::device::BaseInvoker> MakeInvokerPointer()
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "ReferenceContraction_M2_N2_K2"
|
||||
<< std::endl;
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
// A[M0, M1, K0, K1]
|
||||
std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
|
||||
std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
|
||||
// B[N0, N1, K0, K1]
|
||||
std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
|
||||
std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
|
||||
// E[M0, M1, N0, N1]
|
||||
std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
|
||||
std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
|
||||
|
||||
float scale = 1.f;
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// use default case
|
||||
}
|
||||
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 == 23)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
|
||||
const ck::index_t M0 = std::stoi(argv[4]);
|
||||
const ck::index_t M1 = std::stoi(argv[5]);
|
||||
|
||||
const ck::index_t N0 = std::stoi(argv[6]);
|
||||
const ck::index_t N1 = std::stoi(argv[7]);
|
||||
|
||||
const ck::index_t K0 = std::stoi(argv[8]);
|
||||
const ck::index_t K1 = std::stoi(argv[9]);
|
||||
|
||||
a_ms_ks_lengths = {M0, M1, K0, K1};
|
||||
a_ms_ks_strides = {
|
||||
std::stoi(argv[10]), std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13])};
|
||||
|
||||
b_ns_ks_lengths = {N0, N1, K0, K1};
|
||||
b_ns_ks_strides = {
|
||||
std::stoi(argv[14]), std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17])};
|
||||
|
||||
e_ms_ns_lengths = {M0, M1, N0, N1};
|
||||
e_ms_ns_strides = {
|
||||
std::stoi(argv[22]), std::stoi(argv[23]), std::stoi(argv[24]), std::stoi(argv[25])};
|
||||
|
||||
scale = std::stof(argv[26]);
|
||||
}
|
||||
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=no, 1=yes)\n");
|
||||
printf("arg4 to 7: M0, M1, N0, N1, K0, K1\n");
|
||||
printf("arg10 to 13: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
|
||||
printf("arg14 to 17: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
|
||||
printf("arg18 to 21: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
|
||||
printf("arg22: scale\n");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
Tensor<ADataType> a_ms_ks(
|
||||
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()),
|
||||
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end()));
|
||||
Tensor<BDataType> b_ns_ks(
|
||||
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
|
||||
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
|
||||
Tensor<EDataType> e_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_ms_ns_device_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
|
||||
std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
|
||||
std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
|
||||
std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.GetElementSpace());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpace());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpace());
|
||||
|
||||
a_device_buf.ToDevice(a_ms_ks.mData.data());
|
||||
b_device_buf.ToDevice(b_ns_ks.mData.data());
|
||||
|
||||
// set zero
|
||||
e_device_buf.SetZero();
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{scale};
|
||||
|
||||
// device operation
|
||||
auto op = DeviceOpInstance{};
|
||||
auto invoker = op.MakeInvoker();
|
||||
auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 0>{},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
a_ms_ks_lengths,
|
||||
a_ms_ks_strides,
|
||||
b_ns_ks_lengths,
|
||||
b_ns_ks_strides,
|
||||
std::array<std::vector<ck::index_t>, 0>{},
|
||||
std::array<std::vector<ck::index_t>, 0>{},
|
||||
e_ms_ns_lengths,
|
||||
e_ms_ns_strides,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
if(!op.IsSupportedArgument(argument))
|
||||
{
|
||||
std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
|
||||
e_ms_ns_lengths.begin() + NumDimM,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
|
||||
e_ms_ns_lengths.begin() + NumDimM + NumDimN,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
|
||||
a_ms_ks_lengths.begin() + NumDimM + NumDimK,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
std::size_t num_btype =
|
||||
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + +sizeof(EDataType) * M * N;
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< op.GetTypeString() << std::endl;
|
||||
|
||||
e_device_buf.FromDevice(e_ms_ns_device_result.mData.data());
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
|
||||
using ReferenceOpInstance = ReferenceContraction_M2_N2_K2<NumDimM,
|
||||
NumDimN,
|
||||
NumDimK,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceOpInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_ms_ks, b_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(size_t m0 = 0; m0 < e_ms_ns_host_result.mDesc.GetLengths()[0]; ++m0)
|
||||
{
|
||||
for(size_t m1 = 0; m1 < e_ms_ns_host_result.mDesc.GetLengths()[1]; ++m1)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++n0)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n1)
|
||||
{
|
||||
cde_element_op(e_ms_ns_host_result(m0, m1, n0, n1),
|
||||
c_ms_ns_host_result(m0, m1, n0, n1));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user