mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-19 20:40:07 +00:00
GEMM with Multiple Source, GEMM+Bias+Add+FastGeLU example and ckProfiler (#241)
* ad gelu and fast_gelu
* added GeLU and fast GeLU
* clean up
* add gemm+fastgelu example
* add gemm+gelu instances
* update profiler
* clean up
* clean up
* adding gemm+bias+activation
* clean
* adding bias
* clean
* adding gemm multiple d
* debugging
* add gemm bias add fastgelu
* rename, clean
* refactoring; add readme
* refactor
* refactor
* refactor
* refactor
* refactor
* refactor
* fix
* fix
* update example
* update example
* rename
* update example
* add ckProfiler
* clean
* clean
* clean
* clean
* add comment
* use type_convert
* clean
* clean element wise op
[ROCm/composable_kernel commit: 56adf7e9cc]
This commit is contained in:
Chao Liu
288
profiler/include/profile_gemm_add_add_fastgelu_impl.hpp
Normal file
288
profiler/include/profile_gemm_add_add_fastgelu_impl.hpp
Normal file
@@ -0,0 +1,288 @@
|
||||
#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 "reference_gemm.hpp"
|
||||
#include "device_gemm_multiple_d.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace device_gemm_instance {
|
||||
|
||||
using DeviceGemmAddAddFastGeluPtr = ck::tensor_operation::device::DeviceGemmMultipleDPtr<
|
||||
2,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::AddAddFastGelu>;
|
||||
|
||||
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<DeviceGemmAddAddFastGeluPtr>&);
|
||||
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<DeviceGemmAddAddFastGeluPtr>&);
|
||||
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(
|
||||
std::vector<DeviceGemmAddAddFastGeluPtr>&);
|
||||
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(
|
||||
std::vector<DeviceGemmAddAddFastGeluPtr>&);
|
||||
|
||||
} // namespace device_gemm_instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
|
||||
namespace ck {
|
||||
namespace profiler {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename AccDataType,
|
||||
typename D0DataType,
|
||||
typename D1DataType,
|
||||
typename EDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename D0Layout,
|
||||
typename D1Layout,
|
||||
typename ELayout>
|
||||
int profile_gemm_add_add_fastgelu_impl(int do_verification,
|
||||
int init_method,
|
||||
bool /*do_log*/,
|
||||
bool time_kernel,
|
||||
int M,
|
||||
int N,
|
||||
int K,
|
||||
int StrideA,
|
||||
int StrideB,
|
||||
int StrideD0,
|
||||
int StrideD1,
|
||||
int StrideE)
|
||||
{
|
||||
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<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD0, D0Layout{}));
|
||||
Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD1, D1Layout{}));
|
||||
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
|
||||
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
|
||||
|
||||
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
||||
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
||||
std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
|
||||
std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
|
||||
std::cout << "e_m_n: " << e_m_n_device_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});
|
||||
d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
|
||||
d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-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});
|
||||
d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
|
||||
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
|
||||
}
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using AddAddFastGelu = ck::tensor_operation::element_wise::AddAddFastGelu;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = AddAddFastGelu;
|
||||
|
||||
const auto a_element_op = AElementOp{};
|
||||
const auto b_element_op = BElementOp{};
|
||||
const auto cde_element_op = CDEElementOp{};
|
||||
|
||||
// add device GEMM instances
|
||||
std::vector<ck::tensor_operation::device::device_gemm_instance::DeviceGemmAddAddFastGeluPtr>
|
||||
device_op_ptrs;
|
||||
|
||||
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
|
||||
is_same_v<EDataType, half_t>)
|
||||
{
|
||||
if constexpr(is_same_v<ALayout, tensor_layout::gemm::RowMajor> &&
|
||||
is_same_v<BLayout, tensor_layout::gemm::RowMajor> &&
|
||||
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(
|
||||
device_op_ptrs);
|
||||
}
|
||||
else if constexpr(is_same_v<ALayout, tensor_layout::gemm::RowMajor> &&
|
||||
is_same_v<BLayout, tensor_layout::gemm::ColumnMajor> &&
|
||||
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(
|
||||
device_op_ptrs);
|
||||
}
|
||||
else if constexpr(is_same_v<ALayout, tensor_layout::gemm::ColumnMajor> &&
|
||||
is_same_v<BLayout, tensor_layout::gemm::RowMajor> &&
|
||||
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(
|
||||
device_op_ptrs);
|
||||
}
|
||||
else if constexpr(is_same_v<ALayout, tensor_layout::gemm::ColumnMajor> &&
|
||||
is_same_v<BLayout, tensor_layout::gemm::ColumnMajor> &&
|
||||
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
ck::tensor_operation::device::device_gemm_instance::
|
||||
add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(
|
||||
device_op_ptrs);
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "found " << device_op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
// run reference
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<AccDataType> c_m_n(HostTensorDescriptor(
|
||||
std::vector<std::size_t>{static_cast<std::size_t>(M), static_cast<std::size_t>(N)}));
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
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, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
|
||||
DeviceMem d0_m_n_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpace());
|
||||
DeviceMem d1_m_n_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpace());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpace());
|
||||
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
d0_m_n_device_buf.ToDevice(d0_m_n.mData.data());
|
||||
d1_m_n_device_buf.ToDevice(d1_m_n.mData.data());
|
||||
|
||||
std::string best_device_op_name;
|
||||
float best_ave_time = 0;
|
||||
float best_tflops = 0;
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
bool pass = true;
|
||||
|
||||
// profile device operation instances
|
||||
for(auto& device_op_ptr : device_op_ptrs)
|
||||
{
|
||||
auto argument_ptr = device_op_ptr->MakeArgumentPointer(
|
||||
a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 2>{d0_m_n_device_buf.GetDeviceBuffer(),
|
||||
d1_m_n_device_buf.GetDeviceBuffer()},
|
||||
static_cast<EDataType*>(e_device_buf.GetDeviceBuffer()),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
std::array<ck::index_t, 2>{StrideD0, StrideD1},
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
auto invoker_ptr = device_op_ptr->MakeInvokerPointer();
|
||||
|
||||
std::string device_op_name = device_op_ptr->GetTypeString();
|
||||
|
||||
if(device_op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
// re-init E to zero before profiling a kernel
|
||||
e_device_buf.SetZero();
|
||||
|
||||
float ave_time =
|
||||
invoker_ptr->Run(argument_ptr.get(), 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(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: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << device_op_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_device_op_name = device_op_name;
|
||||
best_tflops = tflops;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
|
||||
|
||||
pass = pass &&
|
||||
ck::utils::check_err(e_m_n_device_result.mData, e_m_n_host_result.mData);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << device_op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
||||
<< best_gb_per_sec << " GB/s, " << best_device_op_name << std::endl;
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user