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Commit 3d005816 authored by Chao Liu's avatar Chao Liu
Browse files

update example

parent 9551101e
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Showing with 250 additions and 183 deletions
example/04_gemm_add_add_fastgelu/CMakeLists.txt 0 → 100644
View file @ 3d005816
add_example_executable(example_gemm_add_add_fastgelu_xdl_fp16 gemm_add_add_fastgelu_xdl_fp16.cpp)
example/04_gemm_bias_add_fastgelu/README.md → example/04_gemm_add_add_fastgelu/README.md
View file @ 3d005816
# Instructions for ```example_gemm_bias_add_fastgelu_xdl_fp16```
# Instructions for ```example_gemm_add_add_fastgelu_xdl_fp16```
## Run ```example_gemm_bias_add_fastgelu_xdl_fp16```
## Run ```example_gemm_add_add_fastgelu_xdl_fp16```
```bash
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: arg3: time kernel (0=no, 1=yes)
./bin/example_gemm_bias_add_fastgelu_xdl_fp16 1 1 1
#arg3: time kernel (0=no, 1=yes)
#arg4 to 11: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, StrideE"
./bin/example_gemm_add_add_fastgelu_xdl_fp16 1 1 1
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
......
example/04_gemm_bias_add_fastgelu/gemm_bias_add_fastgelu_xdl_fp16.cpp → example/04_gemm_add_add_fastgelu/gemm_add_add_fastgelu_xdl_fp16.cpp
View file @ 3d005816
......@@ -36,9 +36,11 @@ using D1DataType = F16;
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using ELayout = Row;
using ALayout = Row;
using BLayout = Col;
using D0Layout = Row;
using D1Layout = Row;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
......@@ -68,6 +70,7 @@ int main(int argc, char* argv[])
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideD0 = 0;
ck::index_t StrideD1 = 4096;
ck::index_t StrideE = 4096;
......@@ -81,7 +84,7 @@ int main(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 11)
else if(argc == 12)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
......@@ -93,15 +96,17 @@ int main(int argc, char* argv[])
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideD1 = std::stoi(argv[9]);
StrideE = std::stoi(argv[10]);
StrideD0 = std::stoi(argv[9]);
StrideD1 = std::stoi(argv[10]);
StrideE = std::stoi(argv[11]);
}
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=no, 1=yes)\n");
printf("arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD1, StrideE\n");
printf("arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, "
"StrideE\n");
exit(0);
}
......@@ -121,8 +126,8 @@ int main(int argc, char* argv[])
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<EDataType> d0_m_n(f_host_tensor_descriptor(M, N, 0, ELayout{}));
Tensor<EDataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
......@@ -138,14 +143,14 @@ int main(int argc, char* argv[])
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<EDataType>{-5, 5});
d1_m_n.GenerateTensorValue(GeneratorTensor_2<EDataType>{-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<EDataType>{0.0, 1.0});
d1_m_n.GenerateTensorValue(GeneratorTensor_3<EDataType>{0.0, 1.0});
d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
}
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
......@@ -177,7 +182,7 @@ int main(int argc, char* argv[])
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{0, StrideD1},
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
......@@ -204,9 +209,8 @@ int main(int argc, char* argv[])
if(do_verification)
{
e_m_n_device_buf.FromDevice(e_m_n_device_result.mData.data());
Tensor<AccDataType> c_m_n(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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,
......@@ -232,6 +236,8 @@ int main(int argc, char* argv[])
}
}
e_m_n_device_buf.FromDevice(e_m_n_device_result.mData.data());
return ck::utils::check_err(e_m_n_device_result.mData, e_m_n_host_result.mData) ? 0 : 1;
}
......
example/04_gemm_bias_add_fastgelu/CMakeLists.txt deleted 100644 → 0
View file @ 9551101e
add_example_executable(example_gemm_bias_add_fastgelu_xdl_fp16 gemm_bias_add_fastgelu_xdl_fp16.cpp)
example/CMakeLists.txt
View file @ 3d005816
......@@ -39,7 +39,7 @@ endfunction(add_example_executable_no_testing EXAMPLE_NAME)
add_subdirectory(01_gemm)
add_subdirectory(02_gemm_alpha_beta)
add_subdirectory(03_gemm_bias_relu)
add_subdirectory(04_gemm_bias_add_fastgelu)
add_subdirectory(04_gemm_add_add_fastgelu)
add_subdirectory(06_conv2d_fwd_bias_relu)
add_subdirectory(07_conv2d_fwd_bias_relu_add)
add_subdirectory(09_convnd_fwd)
......
profiler/CMakeLists.txt
View file @ 3d005816
......@@ -25,20 +25,20 @@ include_directories(BEFORE
set(PROFILER_SOURCE
src/profiler.cpp
src/profile_gemm.cpp
src/profile_gemm_bias_2d.cpp
src/profile_gemm_bias_relu.cpp
src/profile_gemm_bias_relu_add.cpp
src/profile_gemm_reduce.cpp
src/profile_batched_gemm.cpp
src/profile_conv_fwd_bias_relu.cpp
src/profile_conv_fwd_bias_relu_add.cpp
src/profile_conv_fwd_bias_relu_atomic_add.cpp
src/profile_convnd_fwd.cpp
src/profile_convnd_bwd_data.cpp
src/profile_reduce.cpp
src/profile_grouped_gemm.cpp
src/profile_conv_bwd_weight.cpp
src/profile_batched_gemm_reduce.cpp
# src/profile_gemm_bias_2d.cpp
# src/profile_gemm_bias_relu.cpp
# src/profile_gemm_bias_relu_add.cpp
# src/profile_gemm_reduce.cpp
# src/profile_batched_gemm.cpp
# src/profile_conv_fwd_bias_relu.cpp
# src/profile_conv_fwd_bias_relu_add.cpp
# src/profile_conv_fwd_bias_relu_atomic_add.cpp
# src/profile_convnd_fwd.cpp
# src/profile_convnd_bwd_data.cpp
# src/profile_reduce.cpp
# src/profile_grouped_gemm.cpp
# src/profile_conv_bwd_weight.cpp
# src/profile_batched_gemm_reduce.cpp
src/profile_gemm_add_add_fastgelu.cpp
)
......@@ -46,21 +46,21 @@ add_executable(ckProfiler ${PROFILER_SOURCE})
target_link_libraries(ckProfiler PRIVATE host_tensor)
target_link_libraries(ckProfiler PRIVATE conv_util)
target_link_libraries(ckProfiler PRIVATE device_gemm_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_bias2d_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_add_instance)
target_link_libraries(ckProfiler PRIVATE device_batched_gemm_instance)
target_link_libraries(ckProfiler PRIVATE device_conv1d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv3d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_atomic_add_instance)
target_link_libraries(ckProfiler PRIVATE device_convnd_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_grouped_gemm_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_batched_gemm_reduce_instance)
#target_link_libraries(ckProfiler PRIVATE device_gemm_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_instance)
#target_link_libraries(ckProfiler PRIVATE device_gemm_bias2d_instance)
#target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_instance)
#target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_add_instance)
#target_link_libraries(ckProfiler PRIVATE device_batched_gemm_instance)
#target_link_libraries(ckProfiler PRIVATE device_conv1d_fwd_instance)
#target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_instance)
#target_link_libraries(ckProfiler PRIVATE device_conv3d_fwd_instance)
#target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance)
#target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance)
#target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_atomic_add_instance)
#target_link_libraries(ckProfiler PRIVATE device_convnd_bwd_data_instance)
#target_link_libraries(ckProfiler PRIVATE device_reduce_instance)
#target_link_libraries(ckProfiler PRIVATE device_grouped_gemm_instance)
#target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_weight_instance)
#target_link_libraries(ckProfiler PRIVATE device_batched_gemm_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_add_add_fastgelu_instance)
profiler/include/profile_gemm_add_add_fastgelu_impl.hpp
View file @ 3d005816
......@@ -25,13 +25,13 @@ using DeviceGemmAddAddFastGeluPtr = ck::tensor_operation::device::DeviceGemmMult
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::FastGelu>;
void add_device_gemm_gelu_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(
std::vector<DeviceGemmAddAddFastGeluPtr>&);
void add_device_gemm_gelu_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(
std::vector<DeviceGemmAddAddFastGeluPtr>&);
void add_device_gemm_gelu_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(
std::vector<DeviceGemmAddAddFastGeluPtr>&);
void add_device_gemm_gelu_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(
void add_device_gemm_add_add_fastgelu_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(
std::vector<DeviceGemmAddAddFastGeluPtr>&);
} // namespace device_gemm_instance
......@@ -44,20 +44,26 @@ namespace profiler {
template <typename ADataType,
typename BDataType,
typename CDataType,
typename D0DataType,
typename D1DataType,
typename EDataType,
typename ALayout,
typename BLayout,
typename CLayout>
int profile_gemm_gelu_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
int M,
int N,
int K,
int StrideA,
int StrideB,
int StrideC)
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) {
......@@ -75,65 +81,75 @@ int profile_gemm_gelu_impl(int do_verification,
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_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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 << "c_m_n: " << c_m_n_device_result.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;
std::size_t num_thread = 1;
switch(init_method)
{
case 0: break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
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}, num_thread);
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
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 AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::FastGelu;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddAddFastGelu = ck::tensor_operation::element_wise::AddAddFastGelu;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
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<CDataType, 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<CLayout, tensor_layout::gemm::RowMajor>)
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
{
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_gelu_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<CLayout, tensor_layout::gemm::RowMajor>)
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
{
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_gelu_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<CLayout, tensor_layout::gemm::RowMajor>)
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
{
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_gelu_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<CLayout, tensor_layout::gemm::RowMajor>)
is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
{
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_gelu_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(device_op_ptrs);
......@@ -145,23 +161,44 @@ int profile_gemm_gelu_impl(int do_verification,
// run reference
if(do_verification)
{
using ReferenceOpInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
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_op = ReferenceOpInstance{};
auto ref_invoker = ref_op.MakeInvoker();
auto ref_argument = ref_op.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
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 c_device_buf(sizeof(CDataType) * c_m_n_device_result.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;
......@@ -174,18 +211,21 @@ int profile_gemm_gelu_impl(int do_verification,
for(auto& device_op_ptr : device_op_ptrs)
{
auto argument_ptr = device_op_ptr->MakeArgumentPointer(
static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
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,
StrideC,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
c_element_op);
cde_element_op);
auto invoker_ptr = device_op_ptr->MakeInvokerPointer();
......@@ -193,8 +233,8 @@ int profile_gemm_gelu_impl(int do_verification,
if(device_op_ptr->IsSupportedArgument(argument_ptr.get()))
{
// re-init C to zero before profiling a kernel
c_device_buf.SetZero();
// 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});
......@@ -202,7 +242,7 @@ int profile_gemm_gelu_impl(int do_verification,
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;
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
......@@ -221,20 +261,10 @@ int profile_gemm_gelu_impl(int do_verification,
if(do_verification)
{
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
pass = pass &&
ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
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;
}
ck::utils::check_err(e_m_n_device_result.mData, e_m_n_host_result.mData);
}
}
else
......
profiler/src/profile_gemm_add_add_fastgelu.cpp
View file @ 3d005816
......@@ -8,24 +8,24 @@
int profile_gemm_add_add_fastgelu(int argc, char* argv[])
{
enum struct GemmMatrixLayout
enum struct MatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
KM_KN_MN, // 2
KM_NK_MN, // 3
MK_KN_NM, // 4
MK_NK_NM, // 5
KM_KN_NM, // 6
KM_NK_NM, // 7
MK_KN_MN_MN_MN, // 0
MK_NK_MN_MN_MN, // 1
KM_KN_MN_MN_MN, // 2
KM_NK_MN_MN_MN, // 3
MK_KN_NM_MN_MN, // 4
MK_NK_NM_MN_MN, // 5
KM_KN_NM_MN_MN, // 6
KM_NK_NM_MN_MN, // 7
};
enum struct GemmDataType
enum struct MatrixDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
F32_F32_F32_F32_F32, // 0
F16_F16_F16_F16_F16_F16_F16, // 1
BF16_BF16_BF16_BF16_BF16, // 2
INT8_INT8_INT8_INT8_INT8, // 3
};
if(argc != 16)
......@@ -41,13 +41,13 @@ int profile_gemm_add_add_fastgelu(int argc, char* argv[])
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideC, StrideD0, StrideD1\n");
printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideE\n");
// clang-format on
exit(1);
}
const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
const auto data_type = static_cast<MatrixDataType>(std::stoi(argv[2]));
const auto layout = static_cast<MatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
......@@ -59,57 +59,85 @@ int profile_gemm_add_add_fastgelu(int argc, char* argv[])
const int StrideA = std::stoi(argv[11]);
const int StrideB = std::stoi(argv[12]);
const int StrideC = std::stoi(argv[13]);
const int StrideD0 = std::stoi(argv[14]);
const int StrideD1 = std::stoi(argv[15]);
const int StrideD0 = std::stoi(argv[13]);
const int StrideD1 = std::stoi(argv[14]);
const int StrideE = std::stoi(argv[15]);
using F16 = ck::half_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
auto profile =
[&](auto a_type, auto b_type, auto c_type, auto a_layout, auto b_layout, auto c_layout) {
using ADataType = decltype(a_type);
using BDataType = decltype(b_type);
using CDataType = decltype(c_type);
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using CLayout = decltype(c_layout);
auto profile = [&](auto a_type,
auto b_type,
auto d0_type,
auto d1_type,
auto e_type,
auto a_layout,
auto b_layout,
auto d0_layout,
auto d1_layout,
auto e_layout) {
using ADataType = decltype(a_type);
using BDataType = decltype(b_type);
using D0DataType = decltype(d0_type);
using D1DataType = decltype(d1_type);
using EDataType = decltype(e_type);
const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
const int DefaultStrideC = ck::is_same_v<CLayout, Row> ? N : M;
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using D0Layout = decltype(d0_layout);
using D1Layout = decltype(d1_layout);
using ELayout = decltype(e_layout);
return ck::profiler::
profile_gemm_gelu_impl<ADataType, BDataType, CDataType, ALayout, BLayout, CLayout>(
do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
(StrideA < 0) ? DefaultStrideA : StrideA,
(StrideB < 0) ? DefaultStrideB : StrideB,
(StrideC < 0) ? DefaultStrideC : StrideC);
};
const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
const int DefaultStrideD0 = ck::is_same_v<D0Layout, Row> ? N : M;
const int DefaultStrideD1 = ck::is_same_v<D1Layout, Row> ? N : M;
const int DefaultStrideE = ck::is_same_v<ELayout, Row> ? N : M;
if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
return ck::profiler::profile_gemm_add_add_gelu_impl<ADataType,
BDataType,
D0DataType,
D1DataType,
EDataType,
ALayout,
BLayout,
D0Layout,
D1Layout,
ELayout>(
do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
(StrideA < 0) ? DefaultStrideA : StrideA,
(StrideB < 0) ? DefaultStrideB : StrideB,
(StrideD0 < 0) ? DefaultStrideD0 : StrideD0,
(StrideD1 < 0) ? DefaultStrideD1 : StrideD1,
(StrideE < 0) ? DefaultStrideE : StrideE);
};
if(data_type == MatrixDataType::F16_F16_F16_F16_F16 && layout == MatrixLayout::MK_KN_MN_MN_MN)
{
return profile(F16{}, F16{}, F16{}, Row{}, Row{}, Row{});
return profile(F16{}, F16{}, F16{}, F16{}, F16{}, Row{}, Row{}, Row{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
else if(data_type == MatrixDataType::F16_F16_F16_F16_F16 &&
layout == MatrixLayout::MK_NK_MN_MN_MN)
{
return profile(F16{}, F16{}, F16{}, Row{}, Col{}, Row{});
return profile(F16{}, F16{}, F16{}, F16{}, F16{}, Row{}, Col{}, Row{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_KN_MN)
else if(data_type == MatrixDataType::F16_F16_F16_F16_F16 &&
layout == MatrixLayout::KM_KN_MN_MN_MN)
{
return profile(F16{}, F16{}, F16{}, Col{}, Row{}, Row{});
return profile(F16{}, F16{}, F16{}, F16{}, F16{}, Col{}, Row{}, Row{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_NK_MN)
else if(data_type == MatrixDataType::F16_F16_F16_F16_F16 &&
layout == MatrixLayout::KM_NK_MN_MN_MN)
{
return profile(F16{}, F16{}, F16{}, Col{}, Col{}, Row{});
return profile(F16{}, F16{}, F16{}, F16{}, F16{}, Col{}, Col{}, Row{}, Row{}, Row{});
}
else
{
......
profiler/src/profiler.cpp
View file @ 3d005816
......@@ -6,21 +6,21 @@
#include "profile_convnd_fwd.hpp"
int profile_gemm(int, char*[]);
int profile_gemm_bias_2d(int, char*[]);
int profile_gemm_bias_relu(int, char*[]);
int profile_gemm_bias_relu_add(int, char*[]);
int profile_gemm_reduce(int, char*[]);
int profile_batched_gemm(int, char*[]);
int profile_grouped_gemm(int, char*[]);
int profile_conv_fwd(int, char*[]);
int profile_conv_fwd_bias_relu(int, char*[]);
int profile_conv_fwd_bias_relu_add(int, char*[]);
int profile_conv_fwd_bias_relu_atomic_add(int, char*[]);
int profile_convnd_bwd_data(int, char*[], int);
int profile_reduce(int, char*[]);
int profile_conv_bwd_weight(int, char*[]);
int profile_batched_gemm_reduce(int, char*[]);
// int profile_gemm(int, char*[]);
// int profile_gemm_bias_2d(int, char*[]);
// int profile_gemm_bias_relu(int, char*[]);
// int profile_gemm_bias_relu_add(int, char*[]);
// int profile_gemm_reduce(int, char*[]);
// int profile_batched_gemm(int, char*[]);
// int profile_grouped_gemm(int, char*[]);
// int profile_conv_fwd(int, char*[]);
// int profile_conv_fwd_bias_relu(int, char*[]);
// int profile_conv_fwd_bias_relu_add(int, char*[]);
// int profile_conv_fwd_bias_relu_atomic_add(int, char*[]);
// int profile_convnd_bwd_data(int, char*[], int);
// int profile_reduce(int, char*[]);
// int profile_conv_bwd_weight(int, char*[]);
// int profile_batched_gemm_reduce(int, char*[]);
int profile_gemm_add_add_fastgelu(int, char*[]);
static void print_helper_message()
......@@ -58,6 +58,7 @@ int main(int argc, char* argv[])
{
return profile_gemm(argc, argv);
}
#if 0
else if(strcmp(argv[1], "gemm_bias_2d") == 0)
{
return profile_gemm_bias_2d(argc, argv);
......@@ -122,6 +123,7 @@ int main(int argc, char* argv[])
{
return profile_conv_bwd_weight(argc, argv);
}
#endif
else if(strcmp(argv[1], "gemm_add_add_fastgelu") == 0)
{
return profile_gemm_add_add_fastgelu(argc, argv);
......
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