mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-15 18:42:06 +00:00
208 lines
9.3 KiB
C++
208 lines
9.3 KiB
C++
#include <iostream>
|
|
#include <numeric>
|
|
#include <initializer_list>
|
|
#include <cstdlib>
|
|
#include <stdlib.h>
|
|
#include <half.hpp>
|
|
#include "config.hpp"
|
|
#include "print.hpp"
|
|
#include "device.hpp"
|
|
#include "host_tensor.hpp"
|
|
#include "host_tensor_generator.hpp"
|
|
#include "host_gemm.hpp"
|
|
#include "device_tensor.hpp"
|
|
#include "device_base.hpp"
|
|
#include "device_gemm_xdl.hpp"
|
|
#include "element_wise_operation.hpp"
|
|
|
|
template <ck::index_t... Is>
|
|
using S = ck::Sequence<Is...>;
|
|
|
|
using ADataType = ck::half_t;
|
|
using BDataType = ck::half_t;
|
|
using CDataType = ck::half_t;
|
|
using AccDataType = float;
|
|
|
|
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;
|
|
|
|
// Compilation parameters for NT problem
|
|
// clang-format off
|
|
using DeviceGemmInstance =
|
|
//#########################################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| AElementwise| BElementwise| CElementwise| 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| | | | Operation| Operation| Operation| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
|
//#########################################| | | | | | | | | | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
|
ck::tensor_operation::device::DeviceGemmXdl< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>;
|
|
// clang-format on
|
|
|
|
template <typename AType,
|
|
typename BType,
|
|
typename CType,
|
|
typename AElementwiseOperation,
|
|
typename BElementwiseOperation,
|
|
typename CElementwiseOperation>
|
|
static void host_verify(const Tensor<AType>& a_m_k,
|
|
const Tensor<BType>& b_k_n,
|
|
Tensor<CType>& c_m_n,
|
|
const AElementwiseOperation& a_element_op,
|
|
const BElementwiseOperation& b_element_op,
|
|
const CElementwiseOperation& c_element_op)
|
|
{
|
|
auto f_mk_kn_mn = [&](auto m, auto n) {
|
|
const int K = a_m_k.mDesc.GetLengths()[1];
|
|
|
|
double v = 0;
|
|
|
|
for(int k = 0; k < K; ++k)
|
|
{
|
|
v += static_cast<const double>(a_element_op(a_m_k(m, k))) *
|
|
static_cast<const double>(b_element_op(b_k_n(k, n)));
|
|
}
|
|
|
|
c_m_n(m, n) = c_element_op(v);
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mk_kn_mn,
|
|
c_m_n.mDesc.GetLengths()[0],
|
|
c_m_n.mDesc.GetLengths()[1])(std::thread::hardware_concurrency());
|
|
}
|
|
|
|
int main(int argc, char* argv[])
|
|
{
|
|
bool do_verification = 0;
|
|
int init_method = 0;
|
|
int nrepeat = 5;
|
|
|
|
// GEMM shape
|
|
ck::index_t M = 3840;
|
|
ck::index_t N = 4096;
|
|
ck::index_t K = 4096;
|
|
|
|
ck::index_t StrideA = 4096;
|
|
ck::index_t StrideB = 4096;
|
|
ck::index_t StrideC = 4096;
|
|
|
|
if(argc == 4)
|
|
{
|
|
do_verification = std::stoi(argv[1]);
|
|
init_method = std::stoi(argv[2]);
|
|
nrepeat = std::stoi(argv[3]);
|
|
}
|
|
else if(argc == 10)
|
|
{
|
|
do_verification = std::stoi(argv[1]);
|
|
init_method = std::stoi(argv[2]);
|
|
nrepeat = std::stoi(argv[3]);
|
|
|
|
M = std::stoi(argv[4]);
|
|
N = std::stoi(argv[5]);
|
|
K = std::stoi(argv[6]);
|
|
|
|
StrideA = std::stoi(argv[7]);
|
|
StrideB = std::stoi(argv[8]);
|
|
StrideC = std::stoi(argv[9]);
|
|
}
|
|
else
|
|
{
|
|
printf("arg1: verification (0=no, 1=yes)\n");
|
|
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
|
printf("arg3: run kernel # of times (>1)\n");
|
|
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
|
|
exit(0);
|
|
}
|
|
|
|
auto f_host_tensor_descriptor =
|
|
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
|
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
|
|
{
|
|
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
|
std::vector<std::size_t>({stride, 1}));
|
|
}
|
|
else
|
|
{
|
|
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
|
std::vector<std::size_t>({1, stride}));
|
|
}
|
|
};
|
|
|
|
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
|
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
|
Tensor<BDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
|
Tensor<BDataType> 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;
|
|
default:
|
|
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
|
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
|
}
|
|
|
|
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());
|
|
c_m_n_device_buf.ToDevice(c_m_n_device_result.mData.data());
|
|
|
|
// 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,
|
|
AElementOp{},
|
|
BElementOp{},
|
|
CElementOp{});
|
|
|
|
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, nrepeat);
|
|
|
|
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"
|
|
<< std::endl;
|
|
|
|
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
|
|
|
|
if(do_verification)
|
|
{
|
|
host_verify(a_m_k, b_k_n, c_m_n_host_result, AElementOp{}, BElementOp{}, CElementOp{});
|
|
|
|
check_error(c_m_n_host_result, c_m_n_device_result);
|
|
}
|
|
}
|