mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 01:10:17 +00:00
f95267f166
* add gridwise gemm v4r1 * rename * adding gemm+reduce * adding gemm+reduce * adding gemm+reduce * adding gemm+reduce * use sfc in shuffling * remove hardcode * remove hardcode * refactor * fix build * adding gemm+reduce * adding gemm+reduce * adding gemm+reduce * adding gemm+reduce * adding gemm+reduce * format * clean * adding gemm+reduce * adding profiler for gemm+reduce * adding gemm+reduce profiler * fix build * clean up * gemm+reduce * fix build * update DeviceGemm_Xdl_CShuffle; update enum to enum class * clean up * add test for gemm+reduce * clean up * refactor * fix build * fix build
336 lines
13 KiB
C++
336 lines
13 KiB
C++
#pragma once
|
|
#include "config.hpp"
|
|
#include "device.hpp"
|
|
#include "host_tensor.hpp"
|
|
#include "host_tensor_generator.hpp"
|
|
#include "host_conv.hpp"
|
|
#include "tensor_layout.hpp"
|
|
#include "device_tensor.hpp"
|
|
#include "element_wise_operation.hpp"
|
|
#include "element_wise_reduce_operation.hpp"
|
|
#include "device_gemm_reduce.hpp"
|
|
#include "reference_gemm.hpp"
|
|
|
|
namespace ck {
|
|
namespace tensor_operation {
|
|
namespace device {
|
|
namespace device_gemm_instance {
|
|
|
|
using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePtr<
|
|
ck::tensor_operation::element_wise::PassThrough,
|
|
ck::tensor_operation::element_wise::PassThrough,
|
|
ck::tensor_operation::element_wise::PassThrough,
|
|
ck::tensor_operation::element_wise::ReduceSum,
|
|
ck::tensor_operation::element_wise::ReduceSquareSum>;
|
|
|
|
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
|
|
std::vector<DeviceGemmReduceNoOpPtr>&);
|
|
|
|
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances(
|
|
std::vector<DeviceGemmReduceNoOpPtr>&);
|
|
|
|
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances(
|
|
std::vector<DeviceGemmReduceNoOpPtr>&);
|
|
|
|
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances(
|
|
std::vector<DeviceGemmReduceNoOpPtr>&);
|
|
|
|
} // namespace device_gemm_instance
|
|
} // namespace device
|
|
} // namespace tensor_operation
|
|
} // namespace ck
|
|
|
|
namespace ck {
|
|
namespace profiler {
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename CDataType,
|
|
typename DDataType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename CLayout>
|
|
bool profile_gemm_reduce_impl(int do_verification,
|
|
int init_method,
|
|
bool do_log,
|
|
int nrepeat,
|
|
int M,
|
|
int N,
|
|
int K,
|
|
int StrideA,
|
|
int StrideB,
|
|
int StrideC)
|
|
{
|
|
bool pass = true;
|
|
|
|
auto f_host_tensor_descriptor =
|
|
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
|
if(is_same<decltype(layout), 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<DDataType> d0_m_host_result(
|
|
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
|
Tensor<DDataType> d1_m_host_result(
|
|
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
|
|
|
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
|
Tensor<DDataType> d0_m_device_result(
|
|
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
|
Tensor<DDataType> d1_m_device_result(
|
|
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
|
|
|
|
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;
|
|
std::cout << "d0_m: " << d0_m_host_result.mDesc << std::endl;
|
|
std::cout << "d1_m: " << d1_m_host_result.mDesc << std::endl;
|
|
|
|
std::size_t num_thread = std::thread::hardware_concurrency();
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
std::srand(0);
|
|
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
|
|
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
|
|
break;
|
|
default:
|
|
std::srand(0);
|
|
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
|
|
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
|
|
}
|
|
|
|
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using D0ReduceOp = ck::tensor_operation::element_wise::ReduceSum;
|
|
using D1ReduceOp = ck::tensor_operation::element_wise::ReduceSquareSum;
|
|
|
|
const auto a_element_op = AElementOp{};
|
|
const auto b_element_op = BElementOp{};
|
|
const auto c_element_op = CElementOp{};
|
|
const auto d0_reduce_op = D0ReduceOp{};
|
|
const auto d1_reduce_op = D1ReduceOp{};
|
|
|
|
if(do_verification)
|
|
{
|
|
using ReferenceGemmInstance = ck::tensor_operation::host::
|
|
ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
|
|
|
|
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);
|
|
|
|
for(int m = 0; m < M; ++m)
|
|
{
|
|
float d0_acc = d0_reduce_op.GetReduceZeroValue();
|
|
float d1_acc = d1_reduce_op.GetReduceZeroValue();
|
|
|
|
for(int n = 0; n < N; ++n)
|
|
{
|
|
d0_reduce_op.Reduce(d0_acc, c_m_n_host_result(m, n));
|
|
d1_reduce_op.Reduce(d1_acc, c_m_n_host_result(m, n));
|
|
}
|
|
|
|
d0_m_host_result(m) = d0_acc;
|
|
d1_m_host_result(m) = d1_acc;
|
|
}
|
|
}
|
|
|
|
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
|
|
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
|
|
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
|
|
DeviceMem d0_device_buf(sizeof(DDataType) * d0_m_device_result.mDesc.GetElementSpace());
|
|
DeviceMem d1_device_buf(sizeof(DDataType) * d1_m_device_result.mDesc.GetElementSpace());
|
|
|
|
a_device_buf.ToDevice(a_m_k.mData.data());
|
|
b_device_buf.ToDevice(b_k_n.mData.data());
|
|
|
|
// add device GEMM instances
|
|
std::vector<ck::tensor_operation::device::device_gemm_instance::DeviceGemmReduceNoOpPtr>
|
|
gemm_ptrs;
|
|
|
|
if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value &&
|
|
is_same<CDataType, half_t>::value)
|
|
{
|
|
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
|
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
|
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
|
{
|
|
ck::tensor_operation::device::device_gemm_instance::
|
|
add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
|
|
gemm_ptrs);
|
|
}
|
|
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
|
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
|
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
|
{
|
|
ck::tensor_operation::device::device_gemm_instance::
|
|
add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances(
|
|
gemm_ptrs);
|
|
}
|
|
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
|
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
|
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
|
{
|
|
ck::tensor_operation::device::device_gemm_instance::
|
|
add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances(
|
|
gemm_ptrs);
|
|
}
|
|
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
|
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
|
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
|
{
|
|
ck::tensor_operation::device::device_gemm_instance::
|
|
add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances(
|
|
gemm_ptrs);
|
|
}
|
|
}
|
|
|
|
if(gemm_ptrs.size() <= 0)
|
|
{
|
|
throw std::runtime_error("wrong! no device GEMM instance found");
|
|
}
|
|
|
|
std::string best_gemm_name;
|
|
float best_ave_time = 0;
|
|
float best_tflops = 0;
|
|
float best_gb_per_sec = 0;
|
|
|
|
// profile device GEMM instances
|
|
for(auto& gemm_ptr : gemm_ptrs)
|
|
{
|
|
auto argument_ptr =
|
|
gemm_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
|
|
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
|
|
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
|
|
static_cast<DDataType*>(d0_device_buf.GetDeviceBuffer()),
|
|
static_cast<DDataType*>(d1_device_buf.GetDeviceBuffer()),
|
|
M,
|
|
N,
|
|
K,
|
|
StrideA,
|
|
StrideB,
|
|
StrideC,
|
|
a_element_op,
|
|
b_element_op,
|
|
c_element_op,
|
|
d0_reduce_op,
|
|
d1_reduce_op);
|
|
|
|
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
|
|
|
|
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
|
|
{
|
|
// warm up
|
|
invoker_ptr->Run(argument_ptr.get());
|
|
|
|
// timing
|
|
float total_time = 0;
|
|
|
|
for(int i = 0; i < nrepeat; ++i)
|
|
{
|
|
// init DO, D1 to 0
|
|
d0_device_buf.SetZero();
|
|
d1_device_buf.SetZero();
|
|
|
|
KernelTimer timer;
|
|
|
|
timer.Start();
|
|
|
|
invoker_ptr->Run(argument_ptr.get());
|
|
|
|
timer.End();
|
|
|
|
total_time += timer.GetElapsedTime();
|
|
}
|
|
|
|
float ave_time = total_time / nrepeat;
|
|
|
|
std::string gemm_name = gemm_ptr->GetTypeString();
|
|
|
|
std::size_t flop = std::size_t(2) * M * N * K;
|
|
|
|
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * M +
|
|
sizeof(CDataType) * M * N + sizeof(CDataType) * 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_name << std::endl;
|
|
|
|
if(tflops > best_tflops)
|
|
{
|
|
best_gemm_name = gemm_name;
|
|
best_tflops = tflops;
|
|
best_ave_time = ave_time;
|
|
best_gb_per_sec = gb_per_sec;
|
|
}
|
|
|
|
if(do_verification)
|
|
{
|
|
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
|
|
d0_device_buf.FromDevice(d0_m_device_result.mData.data());
|
|
d1_device_buf.FromDevice(d1_m_device_result.mData.data());
|
|
|
|
float c_error = check_error(c_m_n_host_result, c_m_n_device_result);
|
|
float d0_error = check_error(d0_m_host_result, d0_m_device_result);
|
|
float d1_error = check_error(d1_m_host_result, d1_m_device_result);
|
|
|
|
pass = pass && (c_error < 1E-6);
|
|
pass = pass && (d0_error < 1E-6);
|
|
pass = pass && (d1_error < 1E-6);
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "c_host: ", c_m_n_host_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "d0_host: ", d0_m_host_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "d0_device: ", d0_m_device_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "d1_host: ", d1_m_host_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "d1_device: ", d1_m_device_result.mData, ",")
|
|
<< std::endl;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
std::cout << "does not support this GEMM problem" << std::endl;
|
|
}
|
|
}
|
|
|
|
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
|
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
|
|
|
|
return pass;
|
|
}
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|