mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-11 17:00:18 +00:00
cec69bc3bc
* Add host API * manually rebase on develop * clean * manually rebase on develop * exclude tests from all target * address review comments * update client app name * fix missing lib name * clang-format update * refactor * refactor * refactor * refactor * refactor * fix test issue * refactor * refactor * refactor * upate cmake and readme Co-authored-by: Chao Liu <chao.liu2@amd.com>
318 lines
13 KiB
C++
318 lines
13 KiB
C++
#pragma once
|
|
#include <iomanip>
|
|
|
|
#include "check_err.hpp"
|
|
#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 "device_gemm.hpp"
|
|
#include "reference_gemm.hpp"
|
|
|
|
namespace ck {
|
|
namespace tensor_operation {
|
|
namespace device {
|
|
namespace device_grouped_gemm_instance {
|
|
|
|
using DeviceGroupedGemmNoOpPtr = ck::tensor_operation::device::DeviceGroupedGemmPtr<
|
|
ck::tensor_operation::element_wise::PassThrough,
|
|
ck::tensor_operation::element_wise::PassThrough,
|
|
ck::tensor_operation::element_wise::PassThrough>;
|
|
|
|
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
|
|
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
|
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
|
|
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
|
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
|
|
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
|
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
|
|
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
|
|
|
} // namespace device_grouped_gemm_instance
|
|
} // namespace device
|
|
} // namespace tensor_operation
|
|
} // namespace ck
|
|
|
|
namespace ck {
|
|
namespace profiler {
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename CDataType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename CLayout>
|
|
void profile_grouped_gemm_impl(int do_verification,
|
|
int init_method,
|
|
bool do_log,
|
|
bool time_kernel,
|
|
const std::vector<int>& Ms,
|
|
const std::vector<int>& Ns,
|
|
const std::vector<int>& Ks,
|
|
const std::vector<int>& StrideAs,
|
|
const std::vector<int>& StrideBs,
|
|
const std::vector<int>& StrideCs)
|
|
{
|
|
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}));
|
|
}
|
|
};
|
|
|
|
std::size_t group_count = Ms.size();
|
|
|
|
if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() &&
|
|
group_count == StrideBs.size() && group_count == StrideCs.size()))
|
|
{
|
|
throw std::runtime_error("wrong! inconsistent M/N/Ks, StrideA/B/Cs size\n");
|
|
}
|
|
|
|
std::vector<Tensor<ADataType>> a_m_k;
|
|
std::vector<Tensor<BDataType>> b_k_n;
|
|
std::vector<Tensor<CDataType>> c_m_n_device_results;
|
|
|
|
for(std::size_t i = 0; i < group_count; i++)
|
|
{
|
|
a_m_k.push_back(
|
|
Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{})));
|
|
b_k_n.push_back(
|
|
Tensor<BDataType>(f_host_tensor_descriptor(Ks[i], Ns[i], StrideBs[i], BLayout{})));
|
|
|
|
c_m_n_device_results.push_back(
|
|
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
|
|
|
|
std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i
|
|
<< "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i
|
|
<< "]:" << c_m_n_device_results[i].mDesc << std::endl;
|
|
|
|
std::size_t num_thread = 1;
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
a_m_k[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
|
|
b_k_n[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
|
|
break;
|
|
default:
|
|
a_m_k[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
|
|
b_k_n[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
|
|
}
|
|
|
|
c_m_n_device_results[i].GenerateTensorValue(GeneratorTensor_0<CDataType>{}, 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;
|
|
|
|
const auto a_element_op = AElementOp{};
|
|
const auto b_element_op = BElementOp{};
|
|
const auto c_element_op = CElementOp{};
|
|
|
|
// if(do_verification)
|
|
// {
|
|
|
|
// }
|
|
|
|
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
|
|
std::vector<DeviceMemPtr> a_device_buf, b_device_buf, c_device_buf;
|
|
|
|
a_device_buf.reserve(group_count);
|
|
b_device_buf.reserve(group_count);
|
|
c_device_buf.reserve(group_count);
|
|
|
|
std::vector<const void*> p_a, p_b;
|
|
std::vector<void*> p_c;
|
|
|
|
p_a.reserve(group_count);
|
|
p_b.reserve(group_count);
|
|
p_c.reserve(group_count);
|
|
|
|
std::vector<ck::tensor_operation::device::GemmShape> gemm_shapes;
|
|
|
|
gemm_shapes.reserve(group_count);
|
|
|
|
for(std::size_t i = 0; i < group_count; i++)
|
|
{
|
|
a_device_buf.emplace_back(
|
|
std::make_unique<DeviceMem>(sizeof(ADataType) * a_m_k[i].mDesc.GetElementSpace()));
|
|
b_device_buf.emplace_back(
|
|
std::make_unique<DeviceMem>(sizeof(BDataType) * b_k_n[i].mDesc.GetElementSpace()));
|
|
|
|
c_device_buf.emplace_back(std::make_unique<DeviceMem>(
|
|
sizeof(CDataType) * c_m_n_device_results[i].mDesc.GetElementSpace()));
|
|
|
|
a_device_buf[i]->ToDevice(a_m_k[i].mData.data());
|
|
b_device_buf[i]->ToDevice(b_k_n[i].mData.data());
|
|
c_device_buf[i]->ToDevice(c_m_n_device_results[i].mData.data());
|
|
|
|
gemm_shapes.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i]});
|
|
|
|
p_a.push_back(a_device_buf[i]->GetDeviceBuffer());
|
|
p_b.push_back(b_device_buf[i]->GetDeviceBuffer());
|
|
p_c.push_back(c_device_buf[i]->GetDeviceBuffer());
|
|
}
|
|
|
|
// add device GEMM instances
|
|
std::vector<
|
|
ck::tensor_operation::device::device_grouped_gemm_instance::DeviceGroupedGemmNoOpPtr>
|
|
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_grouped_gemm_instance::
|
|
add_device_grouped_gemm_xdl_f16_f16_f16_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_grouped_gemm_instance::
|
|
add_device_grouped_gemm_xdl_f16_f16_f16_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_grouped_gemm_instance::
|
|
add_device_grouped_gemm_xdl_f16_f16_f16_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_grouped_gemm_instance::
|
|
add_device_grouped_gemm_xdl_f16_f16_f16_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(p_a,
|
|
p_b,
|
|
p_c,
|
|
gemm_shapes,
|
|
ck::tensor_operation::element_wise::PassThrough{},
|
|
ck::tensor_operation::element_wise::PassThrough{},
|
|
ck::tensor_operation::element_wise::PassThrough{});
|
|
|
|
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
|
|
|
|
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
|
|
{
|
|
std::string gemm_name = gemm_ptr->GetTypeString();
|
|
|
|
float ave_time =
|
|
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
|
|
|
std::size_t flop = 0, num_btype = 0;
|
|
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
|
{
|
|
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
|
|
|
|
num_btype += sizeof(ADataType) * Ms[i] * Ks[i] + sizeof(BDataType) * Ks[i] * Ns[i] +
|
|
sizeof(CDataType) * Ms[i] * Ns[i];
|
|
}
|
|
|
|
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
|
|
|
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
|
std::cout << "Perf: " << std::setw(10) << 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)
|
|
{
|
|
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
|
{
|
|
|
|
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
|
|
|
|
Tensor<CDataType> c_m_n_host_result(
|
|
f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{}));
|
|
|
|
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[i],
|
|
b_k_n[i],
|
|
c_m_n_host_result,
|
|
a_element_op,
|
|
b_element_op,
|
|
c_element_op);
|
|
|
|
ref_invoker.Run(ref_argument);
|
|
ck::utils::check_err(c_m_n_device_results[i].mData, c_m_n_host_result.mData);
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "a : ", a_m_k[i].mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "b: ", b_k_n[i].mData, ",") << std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "c_device: ", c_m_n_device_results[i].mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "c_host : ", c_m_n_host_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;
|
|
} // namespace profiler
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|