mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-05 14:11:29 +00:00
500fa99512
* 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
445 lines
27 KiB
C++
445 lines
27 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#include <iostream>
|
|
#include <numeric>
|
|
#include <initializer_list>
|
|
#include <cstdlib>
|
|
|
|
#include "ck/ck.hpp"
|
|
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
|
#include "ck/tensor_operation/gpu/device/device_contraction_multiple_d_xdl_cshuffle.hpp"
|
|
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
|
|
|
#include "ck/library/utility/check_err.hpp"
|
|
#include "ck/library/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 F32 = float;
|
|
|
|
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
|
|
|
using ADataType = F32;
|
|
using BDataType = F32;
|
|
using AccDataType = F32;
|
|
using CShuffleDataType = F32;
|
|
using DDataType = F32;
|
|
using DsDataType = ck::Tuple<DDataType>;
|
|
using EDataType = F32;
|
|
|
|
static constexpr ck::index_t NumDimM = 2;
|
|
static constexpr ck::index_t NumDimN = 2;
|
|
static constexpr ck::index_t NumDimK = 2;
|
|
|
|
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using CDEElementOp = ck::tensor_operation::element_wise::Bilinear;
|
|
|
|
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
|
|
|
// clang-format off
|
|
using DeviceOpInstanceKKNN = ck::tensor_operation::device::
|
|
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
|
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>;
|
|
|
|
using DeviceOpInstanceKNNN = ck::tensor_operation::device::
|
|
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
|
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 1, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4>;
|
|
|
|
using DeviceOpInstanceMKNN = ck::tensor_operation::device::
|
|
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
|
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 1, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>;
|
|
|
|
using DeviceOpInstanceMNNN = ck::tensor_operation::device::
|
|
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
|
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 1, 1, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4>;
|
|
// clang-format on
|
|
|
|
using DeviceOpInstance = DeviceOpInstanceKKNN;
|
|
|
|
// hardcoded for NumDimM == NumDimN == NumDimK == 2
|
|
template <ck::index_t NumDimM,
|
|
ck::index_t NumDimN,
|
|
ck::index_t NumDimK,
|
|
typename ADataType,
|
|
typename BDataType,
|
|
typename EDataType,
|
|
typename AccDataType,
|
|
typename AElementwiseOperation,
|
|
typename BElementwiseOperation,
|
|
typename CDEElementwiseOperation,
|
|
ck::enable_if_t<NumDimM == 2 && NumDimN == 2 && NumDimK == 2, bool> = false>
|
|
struct ReferenceContraction_M2_N2_K2 : public ck::tensor_operation::device::BaseOperator
|
|
{
|
|
// Argument
|
|
struct Argument : public ck::tensor_operation::device::BaseArgument
|
|
{
|
|
Argument(const Tensor<ADataType>& a_ms_ks,
|
|
const Tensor<BDataType>& b_ns_ks,
|
|
Tensor<EDataType>& e_ms_ns,
|
|
AElementwiseOperation a_element_op,
|
|
BElementwiseOperation b_element_op,
|
|
CDEElementwiseOperation cde_element_op)
|
|
: a_ms_ks_{a_ms_ks},
|
|
b_ns_ks_{b_ns_ks},
|
|
e_ms_ns_{e_ms_ns},
|
|
a_element_op_{a_element_op},
|
|
b_element_op_{b_element_op},
|
|
cde_element_op_{cde_element_op}
|
|
{
|
|
}
|
|
|
|
const Tensor<ADataType>& a_ms_ks_;
|
|
const Tensor<BDataType>& b_ns_ks_;
|
|
Tensor<EDataType>& e_ms_ns_;
|
|
|
|
AElementwiseOperation a_element_op_;
|
|
BElementwiseOperation b_element_op_;
|
|
CDEElementwiseOperation cde_element_op_;
|
|
};
|
|
|
|
// Invoker
|
|
struct Invoker : public ck::tensor_operation::device::BaseInvoker
|
|
{
|
|
using Argument = ReferenceContraction_M2_N2_K2::Argument;
|
|
|
|
float Run(const Argument& arg)
|
|
{
|
|
auto f_ms_ns = [&](auto m0, auto m1, auto n0, auto n1) {
|
|
const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[2];
|
|
const int K1 = arg.a_ms_ks_.mDesc.GetLengths()[3];
|
|
|
|
AccDataType v_acc = 0;
|
|
|
|
for(int k0 = 0; k0 < K0; ++k0)
|
|
{
|
|
for(int k1 = 0; k1 < K1; ++k1)
|
|
{
|
|
AccDataType v_a;
|
|
AccDataType v_b;
|
|
|
|
arg.a_element_op_(
|
|
v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, k0, k1)));
|
|
arg.b_element_op_(
|
|
v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, k0, k1)));
|
|
|
|
v_acc += v_a * v_b;
|
|
}
|
|
}
|
|
|
|
AccDataType v_c;
|
|
|
|
arg.cde_element_op_(v_c, v_acc);
|
|
|
|
arg.e_ms_ns_(m0, m1, n0, n1) = v_c;
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_ms_ns,
|
|
arg.e_ms_ns_.mDesc.GetLengths()[0],
|
|
arg.e_ms_ns_.mDesc.GetLengths()[1],
|
|
arg.e_ms_ns_.mDesc.GetLengths()[2],
|
|
arg.e_ms_ns_.mDesc.GetLengths()[3])(
|
|
std::thread::hardware_concurrency());
|
|
|
|
return 0;
|
|
}
|
|
|
|
float Run(const ck::tensor_operation::device::BaseArgument* p_arg,
|
|
const StreamConfig& /* stream_config */ = StreamConfig{}) override
|
|
{
|
|
return Run(*dynamic_cast<const Argument*>(p_arg));
|
|
}
|
|
};
|
|
|
|
static constexpr bool IsValidCompilationParameter()
|
|
{
|
|
// TODO: properly implement this check
|
|
return true;
|
|
}
|
|
|
|
bool IsSupportedArgument(const ck::tensor_operation::device::BaseArgument*) override
|
|
{
|
|
return true;
|
|
}
|
|
|
|
static auto MakeArgument(const Tensor<ADataType>& a_ms_ks,
|
|
const Tensor<BDataType>& b_ns_ks,
|
|
Tensor<EDataType>& e_ms_ns,
|
|
AElementwiseOperation a_element_op,
|
|
BElementwiseOperation b_element_op,
|
|
CDEElementwiseOperation cde_element_op)
|
|
{
|
|
return Argument{a_ms_ks, b_ns_ks, e_ms_ns, a_element_op, b_element_op, cde_element_op};
|
|
}
|
|
|
|
static auto MakeInvoker() { return Invoker{}; }
|
|
|
|
virtual std::unique_ptr<ck::tensor_operation::device::BaseInvoker> MakeInvokerPointer()
|
|
{
|
|
return std::make_unique<Invoker>(Invoker{});
|
|
}
|
|
|
|
std::string GetTypeString() const override
|
|
{
|
|
auto str = std::stringstream();
|
|
|
|
// clang-format off
|
|
str << "ReferenceContraction_M2_N2_K2"
|
|
<< std::endl;
|
|
// clang-format on
|
|
|
|
return str.str();
|
|
}
|
|
};
|
|
|
|
int main(int argc, char* argv[])
|
|
{
|
|
bool do_verification = true;
|
|
int init_method = 1;
|
|
bool time_kernel = false;
|
|
|
|
// A[M0, M1, K0, K1]
|
|
std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
|
|
std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
|
|
// B[N0, N1, K0, K1]
|
|
std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
|
|
std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
|
|
// D[M0, M1, N0, N1]
|
|
std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
|
|
std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
|
|
// E[M0, M1, N0, N1]
|
|
std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
|
|
std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
|
|
|
|
float alpha = 1.f;
|
|
float beta = 1.f;
|
|
|
|
if(argc == 1)
|
|
{
|
|
// use default case
|
|
}
|
|
else if(argc == 4)
|
|
{
|
|
do_verification = std::stoi(argv[1]);
|
|
init_method = std::stoi(argv[2]);
|
|
time_kernel = std::stoi(argv[3]);
|
|
}
|
|
else if(argc == 28)
|
|
{
|
|
do_verification = std::stoi(argv[1]);
|
|
init_method = std::stoi(argv[2]);
|
|
time_kernel = std::stoi(argv[3]);
|
|
|
|
const ck::index_t M0 = std::stoi(argv[4]);
|
|
const ck::index_t M1 = std::stoi(argv[5]);
|
|
|
|
const ck::index_t N0 = std::stoi(argv[6]);
|
|
const ck::index_t N1 = std::stoi(argv[7]);
|
|
|
|
const ck::index_t K0 = std::stoi(argv[8]);
|
|
const ck::index_t K1 = std::stoi(argv[9]);
|
|
|
|
a_ms_ks_lengths = {M0, M1, K0, K1};
|
|
a_ms_ks_strides = {
|
|
std::stoi(argv[10]), std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13])};
|
|
|
|
b_ns_ks_lengths = {N0, N1, K0, K1};
|
|
b_ns_ks_strides = {
|
|
std::stoi(argv[14]), std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17])};
|
|
|
|
d_ms_ns_lengths = {M0, M1, N0, N1};
|
|
d_ms_ns_strides = {
|
|
std::stoi(argv[18]), std::stoi(argv[19]), std::stoi(argv[20]), std::stoi(argv[21])};
|
|
|
|
e_ms_ns_lengths = {M0, M1, N0, N1};
|
|
e_ms_ns_strides = {
|
|
std::stoi(argv[22]), std::stoi(argv[23]), std::stoi(argv[24]), std::stoi(argv[25])};
|
|
|
|
alpha = std::stof(argv[26]);
|
|
beta = std::stof(argv[27]);
|
|
}
|
|
else
|
|
{
|
|
printf("arg1: verification (0=no, 1=yes)\n");
|
|
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
|
printf("arg3: time kernel (0=no, 1=yes)\n");
|
|
printf("arg4 to 7: M0, M1, N0, N1, K0, K1\n");
|
|
printf("arg10 to 13: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
|
|
printf("arg14 to 17: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
|
|
printf("arg18 to 21: Stride_D_M0, Stride_D_M1, Stride_D_N0, Stride_D_N1\n");
|
|
printf("arg22 to 25: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
|
|
printf("arg26 to 27: alpha, beta\n");
|
|
exit(0);
|
|
}
|
|
|
|
Tensor<ADataType> a_ms_ks(
|
|
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()),
|
|
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end()));
|
|
Tensor<BDataType> b_ns_ks(
|
|
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
|
|
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
|
|
Tensor<EDataType> d_ms_ns(
|
|
std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
|
|
std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
|
|
Tensor<EDataType> e_ms_ns_host_result(
|
|
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
|
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
|
Tensor<EDataType> e_ms_ns_device_result(
|
|
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
|
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
|
|
|
std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
|
|
std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
|
|
std::cout << "d_ms_ns: " << d_ms_ns.mDesc << std::endl;
|
|
std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
|
|
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
|
b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
|
d_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
|
break;
|
|
default:
|
|
a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
|
b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
|
d_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
|
break;
|
|
}
|
|
|
|
DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.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{alpha, beta};
|
|
|
|
// device operation
|
|
auto op = DeviceOpInstance{};
|
|
auto invoker = op.MakeInvoker();
|
|
auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
|
b_device_buf.GetDeviceBuffer(),
|
|
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
|
|
e_device_buf.GetDeviceBuffer(),
|
|
a_ms_ks_lengths,
|
|
a_ms_ks_strides,
|
|
b_ns_ks_lengths,
|
|
b_ns_ks_strides,
|
|
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
|
|
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
|
|
e_ms_ns_lengths,
|
|
e_ms_ns_strides,
|
|
a_element_op,
|
|
b_element_op,
|
|
cde_element_op);
|
|
|
|
if(!op.IsSupportedArgument(argument))
|
|
{
|
|
std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
|
|
|
|
return 0;
|
|
}
|
|
|
|
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
|
|
|
ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
|
|
e_ms_ns_lengths.begin() + NumDimM,
|
|
ck::index_t{1},
|
|
std::multiplies<ck::index_t>{});
|
|
|
|
ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
|
|
e_ms_ns_lengths.begin() + NumDimM + NumDimN,
|
|
ck::index_t{1},
|
|
std::multiplies<ck::index_t>{});
|
|
|
|
ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
|
|
a_ms_ks_lengths.begin() + NumDimM + NumDimK,
|
|
ck::index_t{1},
|
|
std::multiplies<ck::index_t>{});
|
|
|
|
std::size_t flop = std::size_t(2) * M * N * K;
|
|
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
|
sizeof(DDataType) * M * N + sizeof(EDataType) * M * N;
|
|
|
|
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
|
|
|
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
|
|
|
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
|
<< op.GetTypeString() << std::endl;
|
|
|
|
e_device_buf.FromDevice(e_ms_ns_device_result.mData.data());
|
|
|
|
if(do_verification)
|
|
{
|
|
Tensor<CShuffleDataType> c_ms_ns_host_result(
|
|
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
|
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
|
|
|
using ReferenceOpInstance = ReferenceContraction_M2_N2_K2<NumDimM,
|
|
NumDimN,
|
|
NumDimK,
|
|
ADataType,
|
|
BDataType,
|
|
CShuffleDataType,
|
|
AccDataType,
|
|
AElementOp,
|
|
BElementOp,
|
|
PassThrough>;
|
|
|
|
auto ref_gemm = ReferenceOpInstance{};
|
|
auto ref_invoker = ref_gemm.MakeInvoker();
|
|
|
|
auto ref_argument = ref_gemm.MakeArgument(
|
|
a_ms_ks, b_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
|
|
|
|
ref_invoker.Run(ref_argument);
|
|
|
|
for(size_t m0 = 0; m0 < e_ms_ns_host_result.mDesc.GetLengths()[0]; ++m0)
|
|
{
|
|
for(size_t m1 = 0; m1 < e_ms_ns_host_result.mDesc.GetLengths()[1]; ++m1)
|
|
{
|
|
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++n0)
|
|
{
|
|
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n1)
|
|
{
|
|
cde_element_op(e_ms_ns_host_result(m0, m1, n0, n1),
|
|
c_ms_ns_host_result(m0, m1, n0, n1),
|
|
d_ms_ns(m0, m1, n0, n1));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|