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71d974b
start
ltqin May 7, 2022
b5b8562
read for gridwise gemm
ltqin May 7, 2022
c08dcaa
add MakeBGridDescriptor_K0_N0_N1_N2_N3_K1
ltqin May 11, 2022
7d42a6d
add thread copy desc and register buffer
ltqin May 11, 2022
673b30c
add K0PerBlock dim
ltqin May 14, 2022
d9240c6
add read global data
ltqin May 15, 2022
0adfd1a
finish gridwise gemm
ltqin May 15, 2022
8683437
finish blockwise gemm
ltqin May 16, 2022
21885e9
add print data
ltqin May 16, 2022
64c5889
add smallest config
ltqin May 16, 2022
4e81617
add compare code for gridwis gemm
ltqin May 16, 2022
2159921
fix NXdlPerWave
ltqin May 16, 2022
071ca12
fix k0perthread and gridewis gemm main loop
ltqin May 18, 2022
6b4c298
remove b matrix lds alloc
ltqin May 18, 2022
4f88629
fix name
ltqin May 18, 2022
8d4b51c
add test code
ltqin May 19, 2022
cf360b7
create b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3 from parameter
ltqin May 19, 2022
53963d6
add double register
ltqin May 20, 2022
a88005b
modify b_thread_desc_
ltqin May 20, 2022
215b177
add float
ltqin May 25, 2022
c5c32b4
fp16 tag
ltqin May 26, 2022
1d478b9
add tail for pipeline
ltqin May 27, 2022
5173bdd
finish main loop
ltqin May 28, 2022
09b9767
optimize main loop
ltqin May 28, 2022
fb6dafe
start clear gridwise gemm
ltqin May 28, 2022
6de61c0
clear code
ltqin May 28, 2022
28890a1
clear redundant code
ltqin May 29, 2022
8dd8936
change file name
ltqin May 29, 2022
7d85d04
change file name
ltqin May 30, 2022
f9c478e
Merge branch 'develop' into bmatrix_skip_lds
ltqin May 30, 2022
b571256
fix bug after merge develop
ltqin May 30, 2022
c3de33f
Merge branch 'develop' into bmatrix_skip_lds
ltqin May 31, 2022
993ec45
fix input parameters
ltqin Jun 1, 2022
428ae72
using MultiK0 control b load data loop
ltqin Jun 1, 2022
179c561
fix some config
ltqin Jun 7, 2022
346e837
fix verify data(mulitk0)
ltqin Jun 8, 2022
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1 change: 1 addition & 0 deletions example/01_gemm/CMakeLists.txt
Original file line number Diff line number Diff line change
Expand Up @@ -4,5 +4,6 @@ add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp)
add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
add_example_executable(example_gemm_xdl_skip_b_lds_fp16 gemm_xdl_skip_b_lds_fp16.cpp)
# FIXME: re-enable this exampe as test when SWDEV-335738 is fixed
add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
267 changes: 267 additions & 0 deletions example/01_gemm/gemm_xdl_skip_b_lds_fp16.cpp
Original file line number Diff line number Diff line change
@@ -0,0 +1,267 @@
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>

#include "check_err.hpp"
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "device_gemm_xdl_skip_b_lds.hpp"
#include "device_gemm_xdl.hpp"
#include "device_gemm_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp"
#include "reference_gemm.hpp"
#include "gemm_specialization.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 ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;

using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough;

static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
#define USING_SKIP_LDS 1

// clang-format off
#if USING_SKIP_LDS
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdlSkipBLds
//###########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BThreadTransfer| CThreadTransfer| CThreadTransfer|
//###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| SrcScalar| SrcDstVectorDim| DstScalar|
//###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| | PerVector|
//###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
#if 0
< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 32, 128, 4, 8, 32, 32, 1, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 8, 7, 1>;
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
#else
< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 16, 64, 4, 4, 16, 16, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>;
// < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 32, 4, 4, 32, 32, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>;
// < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 7, 1>;
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;
#endif

#else
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl
//###########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 16, 64, 4, 4, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1, 2>;
// < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 32, 4, 4, 32, 32, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1, 2>;
// < F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1, 2>;
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;

#endif
// clang-format on

using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, float, AElementOp, BElementOp, CElementOp>;

template <typename DataType>
std::ostream& show_2d_matrix(std::ostream& os, Tensor<DataType>& matrix)
{
os << "[" << std::endl;
for(size_t x = 0; x < matrix.mDesc.GetLengths()[0]; x++)
{
os << "[";
for(size_t y = 0; y < matrix.mDesc.GetLengths()[1]; y++)
{
os << std::setw(5) << static_cast<float>(matrix(x, y));
}
os << "]" << std::endl;
}
os << "]";
return os;
}
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
bool time_kernel = false;

// GEMM shape
#if 1
ck::index_t M = 16;
ck::index_t N = 64 * 720;
ck::index_t K = 4096;

ck::index_t StrideA = K;
ck::index_t StrideB = K;
ck::index_t StrideC = N;
#else
ck::index_t M = 16;
ck::index_t N = 16;
ck::index_t K = 8;

ck::index_t StrideA = 8;
ck::index_t StrideB = 8;
ck::index_t StrideC = 16;
#endif

if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);

M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);

StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}

auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};

Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));

std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;

switch(init_method)
{
case 0: break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
// a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
}

DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());

a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());

auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};

// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_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) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * 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;

c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());

if(do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();

auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);

ref_invoker.Run(ref_argument);

#if 0
{
show_2d_matrix(std::cout << "a : ", a_m_k) << std::endl;
show_2d_matrix(std::cout << "b: ", b_k_n) << std::endl;
show_2d_matrix(std::cout << "c_device: ", c_m_n_device_result) << std::endl;
show_2d_matrix(std::cout << "c_host :", c_m_n_host_result) << std::endl;
}
#endif
ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
}

return 0;
}
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