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https://github.com/ROCm/composable_kernel.git
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Add batched/grouped_gemm contraction deviceOps (#349)
* convnd_fwd fp16 example
* update example
* update example
* update instance
* updating refernce conv
* update reference conv
* update conv fwd profiler
* update conv 1d and 3d instance
* update include path
* clean
* update profiler for conv bwd data and weight
* update conv bwd weight
* clean
* update conv example
* update profiler for conv bwd weight
* update ckprofiler for conv bwd data
* fix reference conv bwd data bug; update conv bwd data test
* update examples
* fix initialization issue
* update test for conv fwd
* clean
* clean
* remove test case too sensitive to error threshhold
* fix test
* clean
* fix build
* adding conv multiple d
* adding conv multiple D
* add matrix padder
* add gemm padding to convnd
* adding group conv
* update gemm multi-d
* refactor
* refactor
* refactor
* clean
* clean
* refactor
* refactor
* reorg
* add ds
* add bias
* clean
* add G
* adding group
* adding group
* adding group
* update Tensor
* clean
* update example
* update DeviceGemmMultipleD_Xdl_CShuffle
* update conv bwd-data and bwd-weight
* upate contraction example
* update gemm and batch gemm with e permute
* fix example build
* instance for grouped conv1d
* update example
* adding group conv instance
* update gemm bilinear instance
* update gemm+add+add+fastgelu instance
* update profiler
* update profiler
* update test
* update test and client example
* clean
* add grouped conv into profiler
* update profiler
* clean
* add test grouped conv, update all conv test to gtest
* update test
* change gemm_c_permute with contraction
* add grouped_contraction
* add contraction in group_gemm
* add example of grouped_gemm with contraction
* add example of grouped_contraction_bias_e_permute
* clean
* fixed ds
* add m3n2 m2n3 examples into gemm_bias_e_permute
Co-authored-by: Chao Liu <chao.liu2@amd.com>
[ROCm/composable_kernel commit: e08d68d25d]
This commit is contained in:
zjing14
@@ -1 +1,2 @@
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add_example_executable(example_gemm_bias_e_permute_xdl_fp16 gemm_bias_e_permute_xdl_fp16.cpp)
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add_example_executable(example_gemm_bias_e_permute_m3n2_xdl_fp16 gemm_bias_e_permute_m3n2_xdl_fp16.cpp)
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add_example_executable(example_gemm_bias_e_permute_m2n3_xdl_fp16 gemm_bias_e_permute_m2n3_xdl_fp16.cpp)
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@@ -0,0 +1,396 @@
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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_batched_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/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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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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using Add = ck::tensor_operation::element_wise::Add;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F16;
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using DDataType = F16;
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using DsDataType = ck::Tuple<DDataType>;
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using EDataType = F16;
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static constexpr ck::index_t NumDimG = 0;
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static constexpr ck::index_t NumDimM = 2;
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static constexpr ck::index_t NumDimN = 3;
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static constexpr ck::index_t NumDimK = 1;
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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::Add;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
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static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
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static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Default;
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// clang-format off
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using DeviceOpInstanceKKNN = ck::tensor_operation::device::
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//############################################| NumDimG| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| Gemm| A| B| DE| 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| Spacialization| Spacialization| 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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DeviceBatchedContractionMultipleD_Xdl_CShuffle< NumDimG, NumDimM, NumDimN, NumDimK, F16, F16, F32, F16, DsDataType, F16, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 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, 4>, 8>;
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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 == 3 && NumDimK == 1, bool> = false>
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struct ReferenceContraction_M2_N3_K1 : 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_N3_K1::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, auto n2) {
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const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[2];
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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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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)));
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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, n2, k0)));
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v_acc += v_a * v_b;
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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, n2) = 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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arg.e_ms_ns_.mDesc.GetLengths()[4])(
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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_M3_N2_K1"
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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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ck::index_t M0 = 4;
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ck::index_t M1 = 256;
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ck::index_t N0 = 4;
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ck::index_t N1 = 8;
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ck::index_t N2 = 128;
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ck::index_t K0 = 256;
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// A[M0, M1, M2, K0]
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std::vector<ck::index_t> a_ms_ks_lengths{M0, M1, K0};
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std::vector<ck::index_t> a_ms_ks_strides{M1 * K0, K0, 1};
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// B[N0, N1, K0]
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std::vector<ck::index_t> b_ns_ks_lengths{N0, N1, N2, K0};
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std::vector<ck::index_t> b_ns_ks_strides{N1 * N2 * K0, N2 * K0, K0, 1};
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// D[N0, M0, N1, M1, N2]
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std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, N0, N1, N2};
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std::vector<ck::index_t> d_ms_ns_strides{0, 0, N1 * N2, N1, 1};
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// E[N0, M0, N1, M1, N2]
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std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, N0, N1, N2};
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std::vector<ck::index_t> e_ms_ns_strides{N1 * M1 * N2, N2, M0 * N1 * M1 * N2, M1 * N2, 1};
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if(argc == 1)
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{
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// use default case
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}
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else if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: time kernel (0=no, 1=yes)\n");
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exit(0);
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}
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Tensor<ADataType> a_ms_ks(
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std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()),
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std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end()));
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Tensor<BDataType> b_ns_ks(
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std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
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std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
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Tensor<DDataType> d_ms_ns(
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std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
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std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
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Tensor<EDataType> e_ms_ns_host_result(
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std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
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std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
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Tensor<EDataType> e_ms_ns_device_result(
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std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
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std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
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std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
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std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
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std::cout << "d_ms_ns: " << d_ms_ns.mDesc << std::endl;
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std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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d_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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break;
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default:
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a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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d_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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break;
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpaceSize());
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DeviceMem d_device_buf(sizeof(DDataType) * d_ms_ns.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_ms_ks.mData.data());
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b_device_buf.ToDevice(b_ns_ks.mData.data());
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d_device_buf.ToDevice(d_ms_ns.mData.data());
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// set zero
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e_device_buf.SetZero();
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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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// device operation
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auto op = DeviceOpInstance{};
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auto invoker = op.MakeInvoker();
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auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
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e_device_buf.GetDeviceBuffer(),
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a_ms_ks_lengths,
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a_ms_ks_strides,
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b_ns_ks_lengths,
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b_ns_ks_strides,
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std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
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std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
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e_ms_ns_lengths,
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e_ms_ns_strides,
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a_element_op,
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b_element_op,
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cde_element_op);
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if(!op.IsSupportedArgument(argument))
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{
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std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
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return 0;
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
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e_ms_ns_lengths.begin() + NumDimM,
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ck::index_t{1},
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std::multiplies<ck::index_t>{});
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ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
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e_ms_ns_lengths.begin() + NumDimM + NumDimN,
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ck::index_t{1},
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std::multiplies<ck::index_t>{});
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ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
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a_ms_ks_lengths.begin() + NumDimM + NumDimK,
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ck::index_t{1},
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std::multiplies<ck::index_t>{});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(DDataType) * 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_N3_K1<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)
|
||||
{
|
||||
for(size_t n2 = 0; n2 < e_ms_ns_host_result.mDesc.GetLengths()[4]; ++n2)
|
||||
{
|
||||
cde_element_op(e_ms_ns_host_result(m0, m1, n0, n1, n2),
|
||||
c_ms_ns_host_result(m0, m1, n0, n1, n2),
|
||||
d_ms_ns(m0, m1, n0, n1, n2));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -0,0 +1,404 @@
|
||||
// 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_batched_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/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Add = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F16;
|
||||
using DDataType = F16;
|
||||
using DsDataType = ck::Tuple<DDataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
static constexpr ck::index_t NumDimG = 0;
|
||||
static constexpr ck::index_t NumDimM = 3;
|
||||
static constexpr ck::index_t NumDimN = 2;
|
||||
static constexpr ck::index_t NumDimK = 1;
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CDEElementOp = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
|
||||
static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceOpInstanceKKNN = ck::tensor_operation::device::
|
||||
//############################################| NumDimG| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| Gemm| A| B| DE| 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| Spacialization| Spacialization| 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|
|
||||
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedContractionMultipleD_Xdl_CShuffle< NumDimG, NumDimM, NumDimN, NumDimK, F16, F16, F32, F16, DsDataType, F16, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 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, 4>, 1>;
|
||||
// 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 == 3 && NumDimN == 2 && NumDimK == 1, bool> = false>
|
||||
struct ReferenceContraction_M3_N2_K1 : 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_M3_N2_K1::Argument;
|
||||
|
||||
float Run(const Argument& arg)
|
||||
{
|
||||
auto f_ms_ns = [&](auto m0, auto m1, auto m2, auto n0, auto n1) {
|
||||
const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[3];
|
||||
|
||||
AccDataType v_acc = 0;
|
||||
|
||||
for(int k0 = 0; k0 < K0; ++k0)
|
||||
{
|
||||
AccDataType v_a;
|
||||
AccDataType v_b;
|
||||
|
||||
arg.a_element_op_(
|
||||
v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, m2, k0)));
|
||||
arg.b_element_op_(
|
||||
v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, k0)));
|
||||
|
||||
v_acc += v_a * v_b;
|
||||
}
|
||||
|
||||
AccDataType v_c;
|
||||
|
||||
arg.cde_element_op_(v_c, v_acc);
|
||||
|
||||
arg.e_ms_ns_(m0, m1, m2, 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],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[4])(
|
||||
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_M3_N2_K1"
|
||||
<< 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;
|
||||
|
||||
ck::index_t M0 = 4;
|
||||
ck::index_t M1 = 32;
|
||||
ck::index_t M2 = 128;
|
||||
|
||||
ck::index_t N0 = 16;
|
||||
ck::index_t N1 = 256;
|
||||
|
||||
ck::index_t K0 = 256;
|
||||
|
||||
// A[M0, M1, M2, K0]
|
||||
std::vector<ck::index_t> a_ms_ks_lengths{M0, M1, M2, K0};
|
||||
std::vector<ck::index_t> a_ms_ks_strides{M1 * M2 * K0, M2 * K0, K0, 1};
|
||||
// B[N0, N1, K0]
|
||||
std::vector<ck::index_t> b_ns_ks_lengths{N0, N1, K0};
|
||||
std::vector<ck::index_t> b_ns_ks_strides{N1 * K0, K0, 1};
|
||||
#if 1
|
||||
// D[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> d_ms_ns_strides{0, 0, 0, N1, 1};
|
||||
// E[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> e_ms_ns_strides{N0 * M1 * N1 * M2, N1 * M2, 1, M1 * N1 * M2, M2};
|
||||
#else
|
||||
// D[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> d_ms_ns_strides{0, 0, 0, N1, 1};
|
||||
// E[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> e_ms_ns_strides{M1 * M2 * N0 * N1, M2 * N0 * N1, N0 * N1, N1, 1};
|
||||
#endif
|
||||
|
||||
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
|
||||
{
|
||||
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");
|
||||
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<DDataType> 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.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d_device_buf(sizeof(DDataType) * d_ms_ns.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
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{};
|
||||
|
||||
// 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_M3_N2_K1<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 m2 = 0; m2 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++m2)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n0)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[4]; ++n1)
|
||||
{
|
||||
cde_element_op(e_ms_ns_host_result(m0, m1, m2, n0, n1),
|
||||
c_ms_ns_host_result(m0, m1, m2, n0, n1),
|
||||
d_ms_ns(m0, m1, m2, n0, n1));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -1,284 +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_bias_e_permute_xdl.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_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;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Add = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using DDataType = F16;
|
||||
using EDataType = F16;
|
||||
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using DLayout = Row;
|
||||
using ELayout = Row;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = Add;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmBiasEPermute_Xdl
|
||||
//######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//######| | | | 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|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>;
|
||||
// clang-format on
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
ck::index_t M0 = 4;
|
||||
ck::index_t M1 = 32;
|
||||
ck::index_t M2 = 128;
|
||||
ck::index_t N0 = 16;
|
||||
ck::index_t N1 = 256;
|
||||
|
||||
// GEMM shape
|
||||
ck::index_t M = M0 * M1 * M2;
|
||||
ck::index_t N = N0 * N1;
|
||||
ck::index_t K = 128;
|
||||
|
||||
ck::index_t stride_A = K;
|
||||
ck::index_t stride_B = K;
|
||||
|
||||
#if 1
|
||||
// E = [M0, N0, M1, N1, M2]
|
||||
ck::index_t stride_E_M0 = N0 * M1 * N1 * M2;
|
||||
ck::index_t stride_E_M1 = N1 * M2;
|
||||
ck::index_t stride_E_M2 = 1;
|
||||
ck::index_t stride_E_N0 = M1 * N1 * M2;
|
||||
ck::index_t stride_E_N1 = M2;
|
||||
|
||||
// D = [0, N0, 0, N1, 0]
|
||||
ck::index_t stride_D_M0 = 0;
|
||||
ck::index_t stride_D_M1 = 0;
|
||||
ck::index_t stride_D_M2 = 0;
|
||||
ck::index_t stride_D_N0 = N1;
|
||||
ck::index_t stride_D_N1 = 1;
|
||||
#else
|
||||
// D = [0, 0, 0, N0, N1]
|
||||
ck::index_t stride_D_M0 = 0;
|
||||
ck::index_t stride_D_M1 = 0;
|
||||
ck::index_t stride_D_M2 = 0;
|
||||
ck::index_t stride_D_N0 = N1;
|
||||
ck::index_t stride_D_N1 = 1;
|
||||
|
||||
// E = [M0, M1, M2, N0, N1]
|
||||
ck::index_t stride_E_M0 = M1 * M2 * N0 * N1;
|
||||
ck::index_t stride_E_M1 = M2 * N0 * N1;
|
||||
ck::index_t stride_E_M2 = N0 * N1;
|
||||
ck::index_t stride_E_N0 = N1;
|
||||
ck::index_t stride_E_N1 = 1;
|
||||
#endif
|
||||
|
||||
const ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 d_grid_desc{
|
||||
M0, M1, M2, N0, N1, stride_D_M0, stride_D_M1, stride_D_M2, stride_D_N0, stride_D_N1};
|
||||
const ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 e_grid_desc{
|
||||
M0, M1, M2, N0, N1, stride_E_M0, stride_E_M1, stride_E_M2, stride_E_N0, stride_E_N1};
|
||||
|
||||
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
|
||||
{
|
||||
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");
|
||||
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}));
|
||||
}
|
||||
};
|
||||
|
||||
auto f_host_de_tensor_descriptor =
|
||||
[](ck::tensor_operation::device::DEGridDesc_M0_M1_M2_N0_N1 de_grid_desc) {
|
||||
std::size_t m0 = de_grid_desc.M0_;
|
||||
std::size_t m1 = de_grid_desc.M1_;
|
||||
std::size_t m2 = de_grid_desc.M2_;
|
||||
std::size_t n0 = de_grid_desc.N0_;
|
||||
std::size_t n1 = de_grid_desc.N1_;
|
||||
std::size_t stride_m0 = de_grid_desc.stride_M0_;
|
||||
std::size_t stride_m1 = de_grid_desc.stride_M1_;
|
||||
std::size_t stride_m2 = de_grid_desc.stride_M2_;
|
||||
std::size_t stride_n0 = de_grid_desc.stride_N0_;
|
||||
std::size_t stride_n1 = de_grid_desc.stride_N1_;
|
||||
return HostTensorDescriptor(
|
||||
std::vector<std::size_t>({m0, m1, m2, n0, n1}),
|
||||
std::vector<std::size_t>({stride_m0, stride_m1, stride_m2, stride_n0, stride_n1}));
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
|
||||
Tensor<DDataType> d_m0_m1_m2_n0_n1(f_host_de_tensor_descriptor(d_grid_desc));
|
||||
Tensor<EDataType> e_m0_m1_m2_n0_n1_host_result(f_host_de_tensor_descriptor(e_grid_desc));
|
||||
Tensor<EDataType> e_m0_m1_m2_n0_n1_device_result(f_host_de_tensor_descriptor(e_grid_desc));
|
||||
|
||||
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
||||
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
||||
std::cout << "d_m0_m1_m2_n0_n1: " << d_m0_m1_m2_n0_n1.mDesc << std::endl;
|
||||
std::cout << "e_m0_m1_m2_n0_n1: " << e_m0_m1_m2_n0_n1_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});
|
||||
d_m0_m1_m2_n0_n1.GenerateTensorValue(GeneratorTensor_2<DDataType>{-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});
|
||||
d_m0_m1_m2_n0_n1.GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
|
||||
}
|
||||
|
||||
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d_m0_m1_m2_n0_n1_device_buf(sizeof(DDataType) *
|
||||
d_m0_m1_m2_n0_n1.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_m0_m1_m2_n0_n1_device_buf(
|
||||
sizeof(EDataType) * e_m0_m1_m2_n0_n1_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
|
||||
d_m0_m1_m2_n0_n1_device_buf.ToDevice(d_m0_m1_m2_n0_n1.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
|
||||
// do GEMM
|
||||
auto device_op = DeviceOpInstance{};
|
||||
auto invoker = device_op.MakeInvoker();
|
||||
auto argument = device_op.MakeArgument(a_m_k_device_buf.GetDeviceBuffer(),
|
||||
b_k_n_device_buf.GetDeviceBuffer(),
|
||||
d_m0_m1_m2_n0_n1_device_buf.GetDeviceBuffer(),
|
||||
e_m0_m1_m2_n0_n1_device_buf.GetDeviceBuffer(),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
stride_A,
|
||||
stride_B,
|
||||
d_grid_desc,
|
||||
e_grid_desc,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
if(!device_op.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error("wrong! this device_op instance does not support this problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
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) * 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, "
|
||||
<< device_op.GetTypeString() << std::endl;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<AccDataType> c_m_n(HostTensorDescriptor(
|
||||
std::vector<std::size_t>{static_cast<std::size_t>(M), static_cast<std::size_t>(N)}));
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
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_invoker.Run(ref_argument);
|
||||
|
||||
for(int m0 = 0; m0 < M0; ++m0)
|
||||
for(int m1 = 0; m1 < M1; ++m1)
|
||||
for(int m2 = 0; m2 < M2; ++m2)
|
||||
for(int n0 = 0; n0 < N0; ++n0)
|
||||
for(int n1 = 0; n1 < N1; ++n1)
|
||||
{
|
||||
int m = m0 * M1 * M2 + m1 * M2 + m2;
|
||||
int n = n0 * N1 + n1;
|
||||
|
||||
cde_element_op(e_m0_m1_m2_n0_n1_host_result(m0, m1, m2, n0, n1),
|
||||
ck::type_convert<EDataType>(c_m_n(m, n)),
|
||||
d_m0_m1_m2_n0_n1(m0, m1, m2, n0, n1));
|
||||
}
|
||||
|
||||
e_m0_m1_m2_n0_n1_device_buf.FromDevice(e_m0_m1_m2_n0_n1_device_result.mData.data());
|
||||
|
||||
return ck::utils::check_err(e_m0_m1_m2_n0_n1_device_result.mData,
|
||||
e_m0_m1_m2_n0_n1_host_result.mData)
|
||||
? 0
|
||||
: 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
add_example_executable(example_grouped_gemm_bias_xdl_fp16 grouped_gemm_bias_xdl_fp16.cpp)
|
||||
@@ -1,280 +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_grouped_gemm_xdl.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Add = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F16;
|
||||
using DDataType = F16;
|
||||
using DsDataType = ck::Tuple<DDataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using DLayout = Row;
|
||||
using DsLayout = ck::Tuple<DLayout>;
|
||||
using ELayout = Row;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = Add;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl
|
||||
// clang-format off
|
||||
//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//######| | | | | 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|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
|
||||
// clang-format on
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
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");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
int group_count = rand() % 16 + 1;
|
||||
|
||||
// GEMM shape
|
||||
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
|
||||
std::vector<const void*> p_a, p_b;
|
||||
std::vector<std::array<const void*, 1>> p_ds;
|
||||
std::vector<void*> p_c;
|
||||
|
||||
gemm_descs.reserve(group_count);
|
||||
|
||||
for(int i = 0; i < group_count; i++)
|
||||
{
|
||||
int M = 256 + 256 * i;
|
||||
int N = 128 + 128 * i;
|
||||
int K = 64 + 64 * i;
|
||||
|
||||
int stride_A = K;
|
||||
int stride_B = K;
|
||||
int stride_C = N;
|
||||
|
||||
std::vector<ck::index_t> stride_Ds = {0};
|
||||
|
||||
gemm_descs.push_back({M, N, K, stride_A, stride_B, stride_C, stride_Ds});
|
||||
}
|
||||
|
||||
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}));
|
||||
}
|
||||
};
|
||||
|
||||
std::vector<Tensor<ADataType>> a_tensors;
|
||||
std::vector<Tensor<BDataType>> b_tensors;
|
||||
std::vector<Tensor<DDataType>> d_tensors;
|
||||
std::vector<Tensor<EDataType>> e_host_tensors;
|
||||
std::vector<Tensor<EDataType>> e_device_tensors;
|
||||
|
||||
a_tensors.reserve(group_count);
|
||||
b_tensors.reserve(group_count);
|
||||
d_tensors.reserve(group_count);
|
||||
e_host_tensors.reserve(group_count);
|
||||
e_device_tensors.reserve(group_count);
|
||||
|
||||
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
|
||||
|
||||
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, d_tensors_device,
|
||||
e_tensors_device;
|
||||
|
||||
a_tensors_device.reserve(group_count);
|
||||
b_tensors_device.reserve(group_count);
|
||||
d_tensors_device.reserve(group_count);
|
||||
e_tensors_device.reserve(group_count);
|
||||
|
||||
std::size_t flop = 0, num_btype = 0;
|
||||
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].K_, gemm_descs[i].stride_A_, ALayout{})));
|
||||
b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].K_, gemm_descs[i].N_, gemm_descs[i].stride_B_, BLayout{})));
|
||||
d_tensors.push_back(Tensor<DDataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_Ds_[0], ELayout{})));
|
||||
e_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
|
||||
e_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
|
||||
|
||||
std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
|
||||
<< " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << e_device_tensors[i].mDesc
|
||||
<< std::endl;
|
||||
|
||||
flop += std::size_t(2) * gemm_descs[i].M_ * gemm_descs[i].K_ * gemm_descs[i].N_;
|
||||
num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
|
||||
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
|
||||
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSize();
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
case 2:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
default:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
|
||||
d_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
|
||||
}
|
||||
}
|
||||
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
a_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
d_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(DDataType) * d_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
e_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
|
||||
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
|
||||
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
|
||||
d_tensors_device[i]->ToDevice(d_tensors[i].mData.data());
|
||||
|
||||
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
|
||||
p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
|
||||
p_ds.push_back({d_tensors_device[i]->GetDeviceBuffer()});
|
||||
p_c.push_back(e_tensors_device[i]->GetDeviceBuffer());
|
||||
}
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
|
||||
auto gemm = DeviceGemmInstance{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
|
||||
// do GEMM
|
||||
auto argument = gemm.MakeArgument(
|
||||
p_a, p_b, p_ds, p_c, gemm_descs, a_element_op, b_element_op, cde_element_op);
|
||||
|
||||
DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument));
|
||||
|
||||
gemm.SetWorkSpacePointer(&argument, gemm_desc_workspace.GetDeviceBuffer());
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
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, "
|
||||
<< gemm.GetTypeString() << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
if(do_verification)
|
||||
{
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
EDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data());
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
|
||||
b_tensors[i],
|
||||
e_host_tensors[i],
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int m = 0; m < gemm_descs[i].M_; ++m)
|
||||
{
|
||||
for(int n = 0; n < gemm_descs[i].N_; ++n)
|
||||
{
|
||||
cde_element_op(
|
||||
e_host_tensors[i](m, n), e_host_tensors[i](m, n), d_tensors[i](m, n));
|
||||
}
|
||||
}
|
||||
|
||||
pass &= ck::utils::check_err(e_device_tensors[i].mData, e_host_tensors[i].mData);
|
||||
}
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
1
example/28_grouped_gemm_bias_e_permute/CMakeLists.txt
Normal file
1
example/28_grouped_gemm_bias_e_permute/CMakeLists.txt
Normal file
@@ -0,0 +1 @@
|
||||
add_example_executable(example_grouped_gemm_bias_e_permute_xdl_fp16 grouped_gemm_bias_e_permute_xdl_fp16.cpp)
|
||||
@@ -0,0 +1,483 @@
|
||||
// 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_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_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/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F16;
|
||||
using DDataType = F16;
|
||||
using DsDataType = ck::Tuple<DDataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
static constexpr ck::index_t NumDimM = 3;
|
||||
static constexpr ck::index_t NumDimN = 2;
|
||||
static constexpr ck::index_t NumDimK = 1;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
|
||||
static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Packed;
|
||||
|
||||
// clang-format off
|
||||
using DeviceOpInstanceKKNN = ck::tensor_operation::device::
|
||||
//############################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| Gemm| A| B| DE| 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| Spacialization| Spacialization| 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|
|
||||
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F16, F16, F32, F16, DsDataType, F16, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 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, 4>, 8>;
|
||||
// clang-format on
|
||||
|
||||
// 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 == 3 && NumDimN == 2 && NumDimK == 1, bool> = false>
|
||||
struct ReferenceContraction_M3_N2_K1 : 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_M3_N2_K1::Argument;
|
||||
|
||||
float Run(const Argument& arg)
|
||||
{
|
||||
auto f_ms_ns = [&](auto m0, auto m1, auto m2, auto n0, auto n1) {
|
||||
const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[3];
|
||||
|
||||
AccDataType v_acc = 0;
|
||||
|
||||
for(int k0 = 0; k0 < K0; ++k0)
|
||||
{
|
||||
AccDataType v_a;
|
||||
AccDataType v_b;
|
||||
|
||||
arg.a_element_op_(
|
||||
v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, m2, k0)));
|
||||
arg.b_element_op_(
|
||||
v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, k0)));
|
||||
|
||||
v_acc += v_a * v_b;
|
||||
}
|
||||
|
||||
AccDataType v_c;
|
||||
|
||||
arg.cde_element_op_(v_c, v_acc);
|
||||
|
||||
arg.e_ms_ns_(m0, m1, m2, 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],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[4])(
|
||||
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_M3_N2_K1"
|
||||
<< 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;
|
||||
|
||||
if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
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");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
std::size_t group_count = rand() % 16 + 1;
|
||||
|
||||
// GEMM shape
|
||||
std::vector<ck::tensor_operation::device::ContractionDesc<1>> contraction_descs;
|
||||
std::vector<const void*> p_a, p_b;
|
||||
std::vector<std::array<const void*, 1>> p_ds;
|
||||
std::vector<void*> p_c;
|
||||
|
||||
contraction_descs.reserve(group_count);
|
||||
|
||||
for(std::size_t i = 0; i < group_count; i++)
|
||||
{
|
||||
int M0 = 4 * (rand() % 4 + 1);
|
||||
int M1 = 4 * (rand() % 4 + 1);
|
||||
int M2 = 256;
|
||||
|
||||
int N0 = 4 * (rand() % 4 + 1);
|
||||
int N1 = 128;
|
||||
|
||||
int K0 = 64 * (rand() % 4 + 1);
|
||||
|
||||
// A[M0, M1, M2, K0]
|
||||
std::vector<ck::index_t> a_ms_ks_lengths{M0, M1, M2, K0};
|
||||
std::vector<ck::index_t> a_ms_ks_strides{M1 * M2 * K0, M2 * K0, K0, 1};
|
||||
// B[N0, N1, K0]
|
||||
std::vector<ck::index_t> b_ns_ks_lengths{N0, N1, K0};
|
||||
std::vector<ck::index_t> b_ns_ks_strides{N1 * K0, K0, 1};
|
||||
#if 0
|
||||
// D[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> d_ms_ns_strides{0, 0, 0, N1, 1};
|
||||
// E[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> e_ms_ns_strides{N0 * M1 * N1 * M2, N1 * M2, 1, M1 * N1 * M2, M2};
|
||||
#else
|
||||
// D[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> d_ms_ns_strides{0, 0, 0, N1, 1};
|
||||
// E[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> e_ms_ns_strides{M1 * M2 * N0 * N1, M2 * N0 * N1, N0 * N1, N1, 1};
|
||||
#endif
|
||||
|
||||
contraction_descs.push_back(
|
||||
ck::tensor_operation::device::ContractionDesc<1>{a_ms_ks_lengths,
|
||||
a_ms_ks_strides,
|
||||
b_ns_ks_lengths,
|
||||
b_ns_ks_strides,
|
||||
{d_ms_ns_lengths},
|
||||
{d_ms_ns_strides},
|
||||
e_ms_ns_lengths,
|
||||
e_ms_ns_strides});
|
||||
}
|
||||
|
||||
std::vector<Tensor<ADataType>> a_tensors;
|
||||
std::vector<Tensor<BDataType>> b_tensors;
|
||||
std::vector<Tensor<DDataType>> d_tensors;
|
||||
std::vector<Tensor<EDataType>> e_device_tensors;
|
||||
|
||||
a_tensors.reserve(group_count);
|
||||
b_tensors.reserve(group_count);
|
||||
d_tensors.reserve(group_count);
|
||||
e_device_tensors.reserve(group_count);
|
||||
|
||||
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
|
||||
|
||||
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, d_tensors_device,
|
||||
e_tensors_device;
|
||||
|
||||
a_tensors_device.reserve(group_count);
|
||||
b_tensors_device.reserve(group_count);
|
||||
d_tensors_device.reserve(group_count);
|
||||
e_tensors_device.reserve(group_count);
|
||||
|
||||
std::size_t flop = 0, num_btype = 0;
|
||||
|
||||
for(std::size_t i = 0; i < contraction_descs.size(); i++)
|
||||
{
|
||||
const auto a_ms_ks_lengths = contraction_descs[i].a_ms_ks_lengths;
|
||||
const auto a_ms_ks_strides = contraction_descs[i].a_ms_ks_strides;
|
||||
|
||||
const auto b_ns_ks_lengths = contraction_descs[i].b_ns_ks_lengths;
|
||||
const auto b_ns_ks_strides = contraction_descs[i].b_ns_ks_strides;
|
||||
|
||||
const auto d_ms_ns_lengths = contraction_descs[i].ds_ms_ns_lengths[0];
|
||||
const auto d_ms_ns_strides = contraction_descs[i].ds_ms_ns_strides[0];
|
||||
|
||||
const auto e_ms_ns_lengths = contraction_descs[i].e_ms_ns_lengths;
|
||||
const auto e_ms_ns_strides = contraction_descs[i].e_ms_ns_strides;
|
||||
|
||||
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<DDataType> 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_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()));
|
||||
|
||||
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>{});
|
||||
|
||||
a_tensors.push_back(a_ms_ks);
|
||||
b_tensors.push_back(b_ns_ks);
|
||||
d_tensors.push_back(d_ms_ns);
|
||||
|
||||
// e_host_tensors.push_back(e_ms_ns_host_result);
|
||||
e_device_tensors.push_back(e_ms_ns_device_result);
|
||||
|
||||
flop += std::size_t(2) * M_ * K_ * N_;
|
||||
|
||||
num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
|
||||
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
|
||||
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSize();
|
||||
|
||||
std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
|
||||
<< " b_n_k: " << b_tensors[i].mDesc << " c_m_n: " << e_device_tensors[i].mDesc
|
||||
<< std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_tensors[i].GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
|
||||
break;
|
||||
case 2:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_tensors[i].GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
|
||||
break;
|
||||
default:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_1<ADataType>{});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_1<BDataType>{});
|
||||
d_tensors[i].GenerateTensorValue(GeneratorTensor_1<DDataType>{});
|
||||
}
|
||||
}
|
||||
|
||||
for(std::size_t i = 0; i < contraction_descs.size(); i++)
|
||||
{
|
||||
a_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
d_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(DDataType) * d_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
e_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
|
||||
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
|
||||
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
|
||||
d_tensors_device[i]->ToDevice(d_tensors[i].mData.data());
|
||||
|
||||
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
|
||||
p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
|
||||
p_ds.push_back({d_tensors_device[i]->GetDeviceBuffer()});
|
||||
p_c.push_back(e_tensors_device[i]->GetDeviceBuffer());
|
||||
}
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
|
||||
auto gemm = DeviceOpInstanceKKNN{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
|
||||
// do GEMM
|
||||
auto argument = gemm.MakeArgument(
|
||||
p_a, p_b, p_ds, p_c, contraction_descs, a_element_op, b_element_op, cde_element_op);
|
||||
|
||||
DeviceMem contraction_desc_workspace(gemm.GetWorkSpaceSize(&argument));
|
||||
|
||||
gemm.SetWorkSpacePointer(&argument, contraction_desc_workspace.GetDeviceBuffer());
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
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, "
|
||||
<< gemm.GetTypeString() << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
for(std::size_t i = 0; i < group_count; i++)
|
||||
{
|
||||
const auto e_ms_ns_lengths = contraction_descs[i].e_ms_ns_lengths;
|
||||
const auto e_ms_ns_strides = contraction_descs[i].e_ms_ns_strides;
|
||||
|
||||
Tensor<EDataType> 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()));
|
||||
|
||||
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()));
|
||||
|
||||
e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data());
|
||||
|
||||
using ReferenceOpInstance = ReferenceContraction_M3_N2_K1<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_tensors[i],
|
||||
b_tensors[i],
|
||||
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 m2 = 0; m2 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++m2)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n0)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[4]; ++n1)
|
||||
{
|
||||
cde_element_op(e_ms_ns_host_result(m0, m1, m2, n0, n1),
|
||||
c_ms_ns_host_result(m0, m1, m2, n0, n1),
|
||||
d_tensors[i](m0, m1, m2, n0, n1));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pass &= ck::utils::check_err(e_device_tensors[i].mData, e_ms_ns_host_result.mData);
|
||||
}
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
1
example/29_batched_gemm_bias_e_permute/CMakeLists.txt
Normal file
1
example/29_batched_gemm_bias_e_permute/CMakeLists.txt
Normal file
@@ -0,0 +1 @@
|
||||
add_example_executable(example_batched_gemm_bias_e_permute_xdl_fp16 batched_gemm_bias_e_permute_xdl_fp16.cpp)
|
||||
@@ -0,0 +1,418 @@
|
||||
// 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_batched_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/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Add = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F16;
|
||||
using DDataType = F16;
|
||||
using DsDataType = ck::Tuple<DDataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
static constexpr ck::index_t NumDimG = 2;
|
||||
static constexpr ck::index_t NumDimM = 2;
|
||||
static constexpr ck::index_t NumDimN = 2;
|
||||
static constexpr ck::index_t NumDimK = 1;
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CDEElementOp = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
|
||||
static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceOpInstanceKKNN = ck::tensor_operation::device::
|
||||
//############################################| NumDimG| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| Gemm| A| B| DE| 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| Spacialization| Spacialization| 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|
|
||||
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedContractionMultipleD_Xdl_CShuffle< NumDimG, NumDimM, NumDimN, NumDimK, F16, F16, F32, F16, DsDataType, F16, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 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, 4>, 8>;
|
||||
// clang-format on
|
||||
|
||||
using DeviceOpInstance = DeviceOpInstanceKKNN;
|
||||
|
||||
// hardcoded for NumDimM == NumDimN == NumDimK == 2
|
||||
template <ck::index_t NumDimG,
|
||||
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<NumDimG == 2 && NumDimM == 2 && NumDimN == 2 && NumDimK == 1, bool> =
|
||||
false>
|
||||
struct ReferenceContraction_G2_M2_N2_K1 : public ck::tensor_operation::device::BaseOperator
|
||||
{
|
||||
// Argument
|
||||
struct Argument : public ck::tensor_operation::device::BaseArgument
|
||||
{
|
||||
Argument(const Tensor<ADataType>& a_gs_ms_ks,
|
||||
const Tensor<BDataType>& b_gs_ns_ks,
|
||||
Tensor<EDataType>& e_gs_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
: a_gs_ms_ks_{a_gs_ms_ks},
|
||||
b_gs_ns_ks_{b_gs_ns_ks},
|
||||
e_gs_ms_ns_{e_gs_ms_ns},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op}
|
||||
{
|
||||
}
|
||||
|
||||
const Tensor<ADataType>& a_gs_ms_ks_;
|
||||
const Tensor<BDataType>& b_gs_ns_ks_;
|
||||
Tensor<EDataType>& e_gs_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_G2_M2_N2_K1::Argument;
|
||||
|
||||
float Run(const Argument& arg)
|
||||
{
|
||||
auto f_ms_ns = [&](auto g0, auto g1, auto m0, auto m1, auto n0, auto n1) {
|
||||
const int K0 = arg.a_gs_ms_ks_.mDesc.GetLengths()[4];
|
||||
|
||||
AccDataType v_acc = 0;
|
||||
|
||||
for(int k0 = 0; k0 < K0; ++k0)
|
||||
{
|
||||
AccDataType v_a;
|
||||
AccDataType v_b;
|
||||
|
||||
arg.a_element_op_(
|
||||
v_a,
|
||||
ck::type_convert<const AccDataType>(arg.a_gs_ms_ks_(g0, g1, m0, m1, k0)));
|
||||
arg.b_element_op_(
|
||||
v_b,
|
||||
ck::type_convert<const AccDataType>(arg.b_gs_ns_ks_(g0, g1, n0, n1, k0)));
|
||||
|
||||
v_acc += v_a * v_b;
|
||||
}
|
||||
|
||||
AccDataType v_c;
|
||||
|
||||
arg.cde_element_op_(v_c, v_acc);
|
||||
|
||||
arg.e_gs_ms_ns_(g0, g1, m0, m1, n0, n1) = v_c;
|
||||
};
|
||||
|
||||
make_ParallelTensorFunctor(f_ms_ns,
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[0],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[1],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[2],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[3],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[4],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[5])(
|
||||
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_gs_ms_ks,
|
||||
const Tensor<BDataType>& b_gs_ns_ks,
|
||||
Tensor<EDataType>& e_gs_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
{
|
||||
return Argument{
|
||||
a_gs_ms_ks, b_gs_ns_ks, e_gs_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_G2_M2_N2_K1"
|
||||
<< 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;
|
||||
|
||||
ck::index_t G0 = 1;
|
||||
ck::index_t G1 = 2;
|
||||
|
||||
ck::index_t M0 = 4;
|
||||
ck::index_t M1 = 256;
|
||||
|
||||
ck::index_t N0 = 16;
|
||||
ck::index_t N1 = 128;
|
||||
|
||||
ck::index_t K0 = 64;
|
||||
|
||||
// A[G0, G1, M0, M1, K0]
|
||||
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M0, M1, K0};
|
||||
std::vector<ck::index_t> a_gs_ms_ks_strides{G1 * M0 * M1 * K0, M0 * M1 * K0, M1 * K0, K0, 1};
|
||||
// B[G0, G1, N0, N1, K0]
|
||||
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, G1, N0, N1, K0};
|
||||
std::vector<ck::index_t> b_gs_ns_ks_strides{G1 * N0 * N1 * K0, N0 * N1 * K0, N1 * K0, K0, 1};
|
||||
|
||||
// D[G0, G1, M0, N0, M1, N1]
|
||||
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
|
||||
std::vector<ck::index_t> d_gs_ms_ns_strides{G1 * N0 * N1, N0 * N1, 0, 0, N1, 1};
|
||||
// E[G0, G1, M0, N0, M1, N1]
|
||||
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
|
||||
std::vector<ck::index_t> e_gs_ms_ns_strides{
|
||||
G1 * M0 * N0 * M1 * N1, M0 * N0 * M1 * N1, N0 * M1 * N1, N1, M1 * N1, 1};
|
||||
|
||||
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
|
||||
{
|
||||
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");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
Tensor<ADataType> a_gs_ms_ks(
|
||||
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()),
|
||||
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end()));
|
||||
Tensor<BDataType> b_gs_ns_ks(
|
||||
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()),
|
||||
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
|
||||
Tensor<DDataType> d_gs_ms_ns(
|
||||
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_gs_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_gs_ms_ns_device_result(
|
||||
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
|
||||
|
||||
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
|
||||
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
|
||||
std::cout << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
|
||||
std::cout << "e_gs_ms_ns: " << e_gs_ms_ns_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_gs_ns_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) *
|
||||
e_gs_ms_ns_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
|
||||
b_device_buf.ToDevice(b_gs_ns_ks.mData.data());
|
||||
d_device_buf.ToDevice(d_gs_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{};
|
||||
|
||||
// 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_gs_ms_ks_lengths,
|
||||
a_gs_ms_ks_strides,
|
||||
b_gs_ns_ks_lengths,
|
||||
b_gs_ns_ks_strides,
|
||||
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
|
||||
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
|
||||
e_gs_ms_ns_lengths,
|
||||
e_gs_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 G = std::accumulate(e_gs_ms_ns_lengths.begin(),
|
||||
e_gs_ms_ns_lengths.begin() + NumDimG,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t M = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG,
|
||||
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t N = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
|
||||
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM + NumDimN,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t K = std::accumulate(a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM,
|
||||
a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM + NumDimK,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
std::size_t flop = std::size_t(2) * G * M * N * K;
|
||||
std::size_t num_btype = sizeof(ADataType) * G * M * K + sizeof(BDataType) * G * K * N +
|
||||
sizeof(DDataType) * G * M * N + sizeof(EDataType) * G * 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_gs_ms_ns_device_result.mData.data());
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
|
||||
|
||||
using ReferenceOpInstance = ReferenceContraction_G2_M2_N2_K1<NumDimG,
|
||||
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_gs_ms_ks, b_gs_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(size_t g0 = 0; g0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[0]; ++g0)
|
||||
{
|
||||
for(size_t g1 = 0; g1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[1]; ++g1)
|
||||
{
|
||||
for(size_t m0 = 0; m0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[2]; ++m0)
|
||||
{
|
||||
for(size_t m1 = 0; m1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[3]; ++m1)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[4]; ++n0)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[5];
|
||||
++n1)
|
||||
{
|
||||
cde_element_op(e_gs_ms_ns_host_result(g0, g1, m0, m1, n0, n1),
|
||||
c_ms_ns_host_result(g0, g1, m0, m1, n0, n1),
|
||||
d_gs_ms_ns(g0, g1, m0, m1, n0, n1));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData)
|
||||
? 0
|
||||
: 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -1,3 +0,0 @@
|
||||
add_example_executable(example_batched_gemm_xdl_fp16 batched_gemm_xdl_fp16.cpp)
|
||||
add_example_executable(example_batched_gemm_bias_xdl_fp16 batched_gemm_bias_xdl_fp16.cpp)
|
||||
|
||||
@@ -1,248 +0,0 @@
|
||||
#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_batched_gemm_multi_d_xdl.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.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_batched_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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Add = ck::tensor_operation::element_wise::Add;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F16;
|
||||
using DDataType = F16;
|
||||
using DsDataType = ck::Tuple<DDataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using DLayout = Row;
|
||||
using DsLayout = ck::Tuple<DLayout>;
|
||||
using ELayout = Row;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = Add;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
// static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
|
||||
// static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
||||
|
||||
// clang-format off
|
||||
using DeviceGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmMultiD_Xdl
|
||||
//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//######| | | | | 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|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
|
||||
// clang-format on
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
const int M = 256 * (rand() % 16 + 1);
|
||||
const int N = 128 * (rand() % 16 + 1);
|
||||
const int K = 64 * (rand() % 16 + 1);
|
||||
|
||||
const int stride_A = K;
|
||||
const int stride_B = K;
|
||||
const int stride_D = 0;
|
||||
const int stride_E = N;
|
||||
|
||||
const int batch_stride_A = M * K;
|
||||
const int batch_stride_B = K * N;
|
||||
const int batch_stride_D = N;
|
||||
const int batch_stride_E = M * N;
|
||||
|
||||
const int batch_count = 16;
|
||||
|
||||
if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
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");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
// GEMM shape
|
||||
auto f_host_tensor_descriptor = [](std::size_t batch_count_,
|
||||
std::size_t row,
|
||||
std::size_t col,
|
||||
std::size_t stride,
|
||||
std::size_t batch_stride,
|
||||
auto layout) {
|
||||
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}),
|
||||
std::vector<std::size_t>({batch_stride, stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}),
|
||||
std::vector<std::size_t>({batch_stride, 1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_g_m_k(
|
||||
f_host_tensor_descriptor(batch_count, M, K, stride_A, batch_stride_A, ALayout{}));
|
||||
Tensor<BDataType> b_g_k_n(
|
||||
f_host_tensor_descriptor(batch_count, K, N, stride_B, batch_stride_B, BLayout{}));
|
||||
|
||||
Tensor<DDataType> d_g_m_n(
|
||||
f_host_tensor_descriptor(batch_count, M, N, stride_D, batch_stride_D, DLayout{}));
|
||||
|
||||
Tensor<EDataType> e_g_m_n_device_result(
|
||||
f_host_tensor_descriptor(batch_count, M, N, stride_E, batch_stride_E, ELayout{}));
|
||||
|
||||
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
|
||||
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
|
||||
std::cout << "d_g_m_n: " << d_g_m_n.mDesc << std::endl;
|
||||
std::cout << "e_g_m_n: " << e_g_m_n_device_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_g_m_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_g_m_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d_device_buf(sizeof(DDataType) * d_g_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(EDataType) * e_g_m_n_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a_g_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_g_k_n.mData.data());
|
||||
d_device_buf.ToDevice(d_g_m_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
|
||||
auto gemm = DeviceGemmInstance{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
|
||||
// do GEMM
|
||||
auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{d_device_buf.GetDeviceBuffer()},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
batch_count,
|
||||
stride_A,
|
||||
stride_B,
|
||||
{stride_D},
|
||||
stride_E,
|
||||
batch_stride_A,
|
||||
batch_stride_B,
|
||||
{batch_stride_D},
|
||||
batch_stride_E,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = std::size_t(2) * batch_count * M * N * K;
|
||||
std::size_t num_btype = sizeof(ADataType) * batch_count * M * K +
|
||||
sizeof(BDataType) * batch_count * K * N +
|
||||
sizeof(EDataType) * batch_count * 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, "
|
||||
<< gemm.GetTypeString() << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(e_g_m_n_device_result.mData.data());
|
||||
|
||||
using ReferenceBatchedGemmInstance =
|
||||
ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
|
||||
BDataType,
|
||||
EDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
|
||||
auto ref_invoker = ref_batched_gemm.MakeInvoker();
|
||||
|
||||
Tensor<EDataType> e_g_m_n_host_result(
|
||||
f_host_tensor_descriptor(batch_count, M, N, stride_E, batch_stride_E, ELayout{}));
|
||||
|
||||
auto ref_argument = ref_batched_gemm.MakeArgument(
|
||||
a_g_m_k, b_g_k_n, e_g_m_n_host_result, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int g = 0; g < batch_count; g++)
|
||||
{
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
cde_element_op(e_g_m_n_host_result(g, m, n),
|
||||
e_g_m_n_host_result(g, m, n),
|
||||
d_g_m_n(g, m, n));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pass = ck::utils::check_err(
|
||||
e_g_m_n_host_result.mData, e_g_m_n_device_result.mData, "Error: Incorrect results c");
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
@@ -1,217 +0,0 @@
|
||||
#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_batched_gemm_multi_d_xdl.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.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_batched_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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F16;
|
||||
using DsDataType = ck::Tuple<>;
|
||||
using EDataType = F16;
|
||||
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using DsLayout = ck::Tuple<>;
|
||||
using ELayout = Row;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
// static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
|
||||
// static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
||||
|
||||
// clang-format off
|
||||
using DeviceGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmMultiD_Xdl
|
||||
//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//######| | | | | 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|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
|
||||
// clang-format on
|
||||
|
||||
using ReferenceBatchedGemmInstance = ck::tensor_operation::host::
|
||||
ReferenceBatchedGemm<ADataType, BDataType, EDataType, AElementOp, BElementOp, CDEElementOp>;
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
const int M = 256 * (rand() % 16 + 1);
|
||||
const int N = 128 * (rand() % 16 + 1);
|
||||
const int K = 64 * (rand() % 16 + 1);
|
||||
|
||||
const int stride_A = K;
|
||||
const int stride_B = K;
|
||||
const int stride_C = N;
|
||||
|
||||
const int batch_stride_A = M * K;
|
||||
const int batch_stride_B = K * N;
|
||||
const int batch_stride_C = M * N;
|
||||
|
||||
const int batch_count = 16;
|
||||
|
||||
if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
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");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
// GEMM shape
|
||||
auto f_host_tensor_descriptor = [](std::size_t batch_count_,
|
||||
std::size_t row,
|
||||
std::size_t col,
|
||||
std::size_t stride,
|
||||
std::size_t batch_stride,
|
||||
auto layout) {
|
||||
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}),
|
||||
std::vector<std::size_t>({batch_stride, stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}),
|
||||
std::vector<std::size_t>({batch_stride, 1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_g_m_k(
|
||||
f_host_tensor_descriptor(batch_count, M, K, stride_A, batch_stride_A, ALayout{}));
|
||||
Tensor<BDataType> b_g_k_n(
|
||||
f_host_tensor_descriptor(batch_count, K, N, stride_B, batch_stride_B, BLayout{}));
|
||||
|
||||
Tensor<EDataType> e_g_m_n_device_result(
|
||||
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, ELayout{}));
|
||||
|
||||
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
|
||||
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
|
||||
std::cout << "e_g_m_n: " << e_g_m_n_device_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(EDataType) * e_g_m_n_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a_g_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_g_k_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
|
||||
auto gemm = DeviceGemmInstance{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
|
||||
// do GEMM
|
||||
auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
batch_count,
|
||||
stride_A,
|
||||
stride_B,
|
||||
{},
|
||||
stride_C,
|
||||
batch_stride_A,
|
||||
batch_stride_B,
|
||||
{},
|
||||
batch_stride_C,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = std::size_t(2) * batch_count * M * N * K;
|
||||
std::size_t num_btype = sizeof(ADataType) * batch_count * M * K +
|
||||
sizeof(BDataType) * batch_count * K * N +
|
||||
sizeof(EDataType) * batch_count * 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, "
|
||||
<< gemm.GetTypeString() << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(e_g_m_n_device_result.mData.data());
|
||||
|
||||
auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
|
||||
auto ref_invoker = ref_batched_gemm.MakeInvoker();
|
||||
|
||||
Tensor<EDataType> e_g_m_n_host_result(
|
||||
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, ELayout{}));
|
||||
|
||||
auto ref_argument = ref_batched_gemm.MakeArgument(
|
||||
a_g_m_k, b_g_k_n, e_g_m_n_host_result, a_element_op, b_element_op, cde_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
pass = ck::utils::check_err(
|
||||
e_g_m_n_host_result.mData, e_g_m_n_device_result.mData, "Error: Incorrect results c");
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
@@ -42,6 +42,6 @@ add_subdirectory(24_batched_gemm_e_permute)
|
||||
add_subdirectory(25_gemm_bias_e_permute)
|
||||
add_subdirectory(26_contraction)
|
||||
add_subdirectory(27_layernorm)
|
||||
add_subdirectory(28_grouped_gemm_bias)
|
||||
add_subdirectory(29_batched_gemm_multi_d)
|
||||
add_subdirectory(28_grouped_gemm_bias_e_permute)
|
||||
add_subdirectory(29_batched_gemm_bias_e_permute)
|
||||
add_subdirectory(30_grouped_convnd_fwd_bias_relu)
|
||||
|
||||
Reference in New Issue
Block a user