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Commit f9c478e2 authored by ltqin's avatar ltqin
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Merge branch 'develop' into bmatrix_skip_lds

parents 7d85d04a 91d8b7d6
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Showing with 482 additions and 731 deletions
profiler/include/profile_conv_fwd_bias_relu_atomic_add_impl.hpp
View file @ f9c478e2
...@@ -119,7 +119,7 @@ template <int NDimSpatial, ...@@ -119,7 +119,7 @@ template <int NDimSpatial,
void profile_conv_fwd_bias_relu_atomic_add_impl(int do_verification, void profile_conv_fwd_bias_relu_atomic_add_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
...@@ -275,7 +275,8 @@ void profile_conv_fwd_bias_relu_atomic_add_impl(int do_verification, ...@@ -275,7 +275,8 @@ void profile_conv_fwd_bias_relu_atomic_add_impl(int do_verification,
{ {
std::string conv_name = op_ptr->GetTypeString(); std::string conv_name = op_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X; std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
......
profiler/include/profile_conv_fwd_bias_relu_impl.hpp
View file @ f9c478e2
...@@ -41,7 +41,7 @@ template <int NDimSpatial, ...@@ -41,7 +41,7 @@ template <int NDimSpatial,
void profile_conv_fwd_bias_relu_impl(int do_verification, void profile_conv_fwd_bias_relu_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
...@@ -207,7 +207,8 @@ void profile_conv_fwd_bias_relu_impl(int do_verification, ...@@ -207,7 +207,8 @@ void profile_conv_fwd_bias_relu_impl(int do_verification,
{ {
std::string conv_name = op_ptr->GetTypeString(); std::string conv_name = op_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X; std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
......
profiler/include/profile_convnd_bwd_data_impl.hpp
View file @ f9c478e2
#pragma once #pragma once
#include "config.hpp" #include "config.hpp"
#include "device.hpp" #include "device.hpp"
#include "conv_fwd_util.hpp" #include "conv_util.hpp"
#include "host_tensor.hpp" #include "host_tensor.hpp"
#include "host_tensor_generator.hpp" #include "host_tensor_generator.hpp"
#include "tensor_layout.hpp" #include "tensor_layout.hpp"
...@@ -222,7 +222,7 @@ static bool check_out(const Tensor<T>& ref, const Tensor<T>& result) ...@@ -222,7 +222,7 @@ static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
{ {
float max_diff = 1e-6; float max_diff = 1e-6;
for(int i = 0; i < ref.mData.size(); ++i) for(std::size_t i = 0; i < ref.mData.size(); ++i)
{ {
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i])); float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
if(max_diff < diff) if(max_diff < diff)
...@@ -236,16 +236,16 @@ template <typename DataType> ...@@ -236,16 +236,16 @@ template <typename DataType>
void show_data_nhwc_layout(Tensor<DataType>& nhwc) void show_data_nhwc_layout(Tensor<DataType>& nhwc)
{ {
std::cout << "["; std::cout << "[";
for(int n = 0; n < nhwc.mDesc.GetLengths()[0]; n++) for(int n = 0; n < ck::type_convert<int>(nhwc.mDesc.GetLengths()[0]); n++)
{ {
std::cout << "["; std::cout << "[";
for(int hi = 0; hi < nhwc.mDesc.GetLengths()[2]; hi++) for(int hi = 0; hi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[2]); hi++)
{ {
std::cout << "["; std::cout << "[";
for(int wi = 0; wi < nhwc.mDesc.GetLengths()[3]; wi++) for(int wi = 0; wi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[3]); wi++)
{ {
std::cout << "["; std::cout << "[";
for(int c = 0; c < nhwc.mDesc.GetLengths()[1]; c++) for(int c = 0; c < ck::type_convert<int>(nhwc.mDesc.GetLengths()[1]); c++)
{ {
std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << " "; std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << " ";
} }
...@@ -269,7 +269,7 @@ template <int NDimSpatial, ...@@ -269,7 +269,7 @@ template <int NDimSpatial,
bool profile_convnd_bwd_data_impl(int do_verification, bool profile_convnd_bwd_data_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
...@@ -410,7 +410,8 @@ bool profile_convnd_bwd_data_impl(int do_verification, ...@@ -410,7 +410,8 @@ bool profile_convnd_bwd_data_impl(int do_verification,
{ {
std::string conv_name = conv_ptr->GetTypeString(); std::string conv_name = conv_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t flop =
ck::utils::conv::get_flops(N, C, K, filter_spatial_lengths, output_spatial_lengths); ck::utils::conv::get_flops(N, C, K, filter_spatial_lengths, output_spatial_lengths);
......
profiler/include/profile_gemm_bias_2d_impl.hpp
View file @ f9c478e2
...@@ -65,7 +65,7 @@ template <typename ADataType, ...@@ -65,7 +65,7 @@ template <typename ADataType,
void profile_gemm_bias_2d_impl(int do_verification, void profile_gemm_bias_2d_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
int M, int M,
int N, int N,
int K, int K,
...@@ -259,7 +259,8 @@ void profile_gemm_bias_2d_impl(int do_verification, ...@@ -259,7 +259,8 @@ void profile_gemm_bias_2d_impl(int do_verification,
{ {
std::string gemm_name = gemm_ptr->GetTypeString(); std::string gemm_name = gemm_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); 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 flop = std::size_t(2) * M * N * K;
......
profiler/include/profile_gemm_bias_relu_add_impl.hpp
View file @ f9c478e2
...@@ -48,7 +48,7 @@ template <typename ADataType, ...@@ -48,7 +48,7 @@ template <typename ADataType,
void profile_gemm_bias_relu_add_impl(int do_verification, void profile_gemm_bias_relu_add_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
int M, int M,
int N, int N,
int K, int K,
...@@ -232,7 +232,8 @@ void profile_gemm_bias_relu_add_impl(int do_verification, ...@@ -232,7 +232,8 @@ void profile_gemm_bias_relu_add_impl(int do_verification,
{ {
std::string gemm_name = gemm_ptr->GetTypeString(); std::string gemm_name = gemm_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); 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 flop = std::size_t(2) * M * N * K;
......
profiler/include/profile_gemm_bias_relu_impl.hpp
View file @ f9c478e2
...@@ -48,7 +48,7 @@ template <typename ADataType, ...@@ -48,7 +48,7 @@ template <typename ADataType,
void profile_gemm_bias_relu_impl(int do_verification, void profile_gemm_bias_relu_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
int M, int M,
int N, int N,
int K, int K,
...@@ -212,7 +212,8 @@ void profile_gemm_bias_relu_impl(int do_verification, ...@@ -212,7 +212,8 @@ void profile_gemm_bias_relu_impl(int do_verification,
{ {
std::string gemm_name = gemm_ptr->GetTypeString(); std::string gemm_name = gemm_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); 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 flop = std::size_t(2) * M * N * K;
......
profiler/include/profile_gemm_impl.hpp
View file @ f9c478e2
#pragma once #pragma once
#include <iomanip> #include <iomanip>
#include <iostream>
#include <typeinfo>
#include "check_err.hpp" #include "check_err.hpp"
#include "config.hpp" #include "config.hpp"
...@@ -42,14 +44,10 @@ void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(std::vector<De ...@@ -42,14 +44,10 @@ void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(std::vector<De
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&); void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&); void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_int8_int8_int8_mk_kn_mn_instances( void add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
std::vector<DeviceGemmNoOpPtr>&); void add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_int8_int8_int8_mk_nk_mn_instances( void add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
std::vector<DeviceGemmNoOpPtr>&); void add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_int8_int8_int8_km_kn_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_int8_int8_int8_km_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances( void add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&); std::vector<DeviceGemmNoOpPtr>&);
...@@ -74,6 +72,21 @@ void add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(std::vector<Devic ...@@ -74,6 +72,21 @@ void add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(std::vector<Devic
void add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&); void add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&); void add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_i8_i8_i8_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_i8_i8_i8_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_i8_i8_i8_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_i8_i8_i8_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance } // namespace device_gemm_instance
} // namespace device } // namespace device
} // namespace tensor_operation } // namespace tensor_operation
...@@ -85,13 +98,14 @@ namespace profiler { ...@@ -85,13 +98,14 @@ namespace profiler {
template <typename ADataType, template <typename ADataType,
typename BDataType, typename BDataType,
typename CDataType, typename CDataType,
typename AccDataType,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
typename CLayout> typename CLayout>
void profile_gemm_impl(int do_verification, void profile_gemm_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
int M, int M,
int N, int N,
int K, int K,
...@@ -125,7 +139,11 @@ void profile_gemm_impl(int do_verification, ...@@ -125,7 +139,11 @@ void profile_gemm_impl(int do_verification,
std::size_t num_thread = 1; std::size_t num_thread = 1;
switch(init_method) switch(init_method)
{ {
case 0: break; // case 0: break;
case 0:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{}, num_thread);
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{}, num_thread);
break;
case 1: case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread); a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread); b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
...@@ -174,6 +192,9 @@ void profile_gemm_impl(int do_verification, ...@@ -174,6 +192,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(gemm_ptrs);
} }
...@@ -192,6 +213,9 @@ void profile_gemm_impl(int do_verification, ...@@ -192,6 +213,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(gemm_ptrs);
} }
...@@ -210,6 +234,9 @@ void profile_gemm_impl(int do_verification, ...@@ -210,6 +234,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(gemm_ptrs);
} }
...@@ -228,6 +255,9 @@ void profile_gemm_impl(int do_verification, ...@@ -228,6 +255,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(gemm_ptrs);
} }
...@@ -250,6 +280,9 @@ void profile_gemm_impl(int do_verification, ...@@ -250,6 +280,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
} }
...@@ -268,6 +301,9 @@ void profile_gemm_impl(int do_verification, ...@@ -268,6 +301,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
...@@ -289,6 +325,9 @@ void profile_gemm_impl(int do_verification, ...@@ -289,6 +325,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
} }
...@@ -307,6 +346,9 @@ void profile_gemm_impl(int do_verification, ...@@ -307,6 +346,9 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
} }
...@@ -353,28 +395,40 @@ void profile_gemm_impl(int do_verification, ...@@ -353,28 +395,40 @@ void profile_gemm_impl(int do_verification,
is_same<CLayout, tensor_layout::gemm::RowMajor>::value) is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{ {
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_int8_int8_int8_mk_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_i8_i8_i8_mk_kn_mn_instances(gemm_ptrs);
} }
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value && else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value && is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<CLayout, tensor_layout::gemm::RowMajor>::value) is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{ {
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_int8_int8_int8_mk_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_i8_i8_i8_mk_nk_mn_instances(gemm_ptrs);
} }
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value && else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<BLayout, tensor_layout::gemm::RowMajor>::value && is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
is_same<CLayout, tensor_layout::gemm::RowMajor>::value) is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{ {
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_int8_int8_int8_km_kn_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_i8_i8_i8_km_kn_mn_instances(gemm_ptrs);
} }
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value && else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value && is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<CLayout, tensor_layout::gemm::RowMajor>::value) is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{ {
ck::tensor_operation::device::device_gemm_instance:: ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_int8_int8_int8_km_nk_mn_instances(gemm_ptrs); add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instances(gemm_ptrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_i8_i8_i8_km_nk_mn_instances(gemm_ptrs);
} }
} }
...@@ -416,12 +470,13 @@ void profile_gemm_impl(int do_verification, ...@@ -416,12 +470,13 @@ void profile_gemm_impl(int do_verification,
std::string gemm_name = gemm_ptr->GetTypeString(); std::string gemm_name = gemm_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); 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 flop = std::size_t(2) * M * N * K;
std::size_t num_btype = std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * M + sizeof(CDataType) * M * N; sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time; float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
...@@ -457,8 +512,14 @@ void profile_gemm_impl(int do_verification, ...@@ -457,8 +512,14 @@ void profile_gemm_impl(int do_verification,
bf16_to_f32_(b_k_n, b_f32_k_n); bf16_to_f32_(b_k_n, b_f32_k_n);
bf16_to_f32_(c_m_n_device_result, c_m_n_device_f32_result); bf16_to_f32_(c_m_n_device_result, c_m_n_device_f32_result);
using ReferenceGemmInstance = ck::tensor_operation::host:: using ReferenceGemmInstance =
ReferenceGemm<float, float, float, AElementOp, BElementOp, CElementOp>; ck::tensor_operation::host::ReferenceGemm<float,
float,
float,
float,
AElementOp,
BElementOp,
CElementOp>;
auto ref_gemm = ReferenceGemmInstance{}; auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker(); auto ref_invoker = ref_gemm.MakeInvoker();
...@@ -490,6 +551,7 @@ void profile_gemm_impl(int do_verification, ...@@ -490,6 +551,7 @@ void profile_gemm_impl(int do_verification,
ck::tensor_operation::host::ReferenceGemm<ADataType, ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType, BDataType,
CDataType, CDataType,
AccDataType,
AElementOp, AElementOp,
BElementOp, BElementOp,
CElementOp>; CElementOp>;
...@@ -522,12 +584,50 @@ void profile_gemm_impl(int do_verification, ...@@ -522,12 +584,50 @@ void profile_gemm_impl(int do_verification,
} }
else else
{ {
std::cout << "does not support this GEMM problem" << std::endl; std::cout << gemm_ptr->GetTypeString() << " does not support this GEMM problem"
<< std::endl;
} }
} }
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, " if constexpr(is_same<CDataType, float>::value)
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl; {
std::cout << "Best Perf for datatype = f32";
}
else if constexpr(is_same<CDataType, half_t>::value)
{
std::cout << "Best Perf for datatype = f16";
}
else if constexpr(is_same<CDataType, bhalf_t>::value)
{
std::cout << "Best Perf for datatype = bf16";
}
else if constexpr(is_same<CDataType, int8_t>::value)
{
std::cout << "Best Perf for datatype = int8";
}
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
{
std::cout << " ALayout = RowMajor";
}
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
{
std::cout << " ALayout = ColumnMajor";
}
if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
{
std::cout << " BLayout = RowMajor";
}
else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
{
std::cout << " BLayout = ColumnMajor";
}
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " : " << best_ave_time
<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
<< best_gemm_name << std::endl;
} }
} // namespace profiler } // namespace profiler
......
profiler/include/profile_gemm_reduce_impl.hpp
View file @ f9c478e2
...@@ -16,11 +16,21 @@ namespace tensor_operation { ...@@ -16,11 +16,21 @@ namespace tensor_operation {
namespace device { namespace device {
namespace device_gemm_instance { namespace device_gemm_instance {
using F32 = float;
using F16 = ck::half_t;
using DPtrsGlobal = ck::Tuple<F32*, F32*>;
using Identity = ck::tensor_operation::element_wise::UnaryIdentic<F32, F32, false>;
using Square = ck::tensor_operation::element_wise::UnarySquare<F32, F32, false>;
using DInElementOps = ck::Tuple<Identity, Square>;
using DOutElementOps = ck::Tuple<Identity, Identity>;
using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePtr< using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePtr<
DPtrsGlobal,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::UnarySquare<float, float, false>>; DInElementOps,
DOutElementOps>;
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances( void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
std::vector<DeviceGemmReduceNoOpPtr>&); std::vector<DeviceGemmReduceNoOpPtr>&);
...@@ -52,7 +62,7 @@ template <typename ADataType, ...@@ -52,7 +62,7 @@ template <typename ADataType,
bool profile_gemm_reduce_impl(int do_verification, bool profile_gemm_reduce_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
int M, int M,
int N, int N,
int K, int K,
...@@ -112,24 +122,35 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -112,24 +122,35 @@ bool profile_gemm_reduce_impl(int do_verification,
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread); b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
} }
using AElementOp = ck::tensor_operation::element_wise::PassThrough; using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough; using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough; using CElementOp = ck::tensor_operation::element_wise::PassThrough;
using D0ReduceOp = ck::reduce::Add<float>; using D0ReduceOp = ck::reduce::Add<float>;
using D1ReduceOp = ck::reduce::Add<float>; using D1ReduceOp = ck::reduce::Add<float>;
using D1ElementOp = ck::tensor_operation::element_wise::UnarySquare<float, float, false>; using UnaryIdenticElementOp =
ck::tensor_operation::element_wise::UnaryIdentic<float, float, false>;
const auto a_element_op = AElementOp{}; using UnarySquareElementOp =
const auto b_element_op = BElementOp{}; ck::tensor_operation::element_wise::UnarySquare<float, float, false>;
const auto c_element_op = CElementOp{}; using DxsInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
const auto d0_reduce_op = D0ReduceOp{}; using DxsOutElementOps = ck::Tuple<UnaryIdenticElementOp, UnaryIdenticElementOp>;
const auto d1_reduce_op = D1ReduceOp{};
const auto d1_element_op = D1ElementOp{}; const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
const auto dxs_in_element_op = DxsInElementOps{};
const auto dxs_out_element_op = DxsOutElementOps{};
const auto d0_reduce_op = D0ReduceOp{};
const auto d1_reduce_op = D1ReduceOp{};
if(do_verification) if(do_verification)
{ {
using ReferenceGemmInstance = ck::tensor_operation::host:: using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>; BDataType,
CDataType,
DDataType,
AElementOp,
BElementOp,
CElementOp>;
auto ref_gemm = ReferenceGemmInstance{}; auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker(); auto ref_invoker = ref_gemm.MakeInvoker();
...@@ -149,7 +170,7 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -149,7 +170,7 @@ bool profile_gemm_reduce_impl(int do_verification,
float d0_val = ck::type_convert<float>(c_m_n_host_result(m, n)); float d0_val = ck::type_convert<float>(c_m_n_host_result(m, n));
float d1_val; float d1_val;
d1_element_op(d1_val, d0_val); UnarySquareElementOp{}(d1_val, d0_val);
d0_reduce_op(d0_acc, d0_val); d0_reduce_op(d0_acc, d0_val);
d1_reduce_op(d1_acc, d1_val); d1_reduce_op(d1_acc, d1_val);
} }
...@@ -165,6 +186,9 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -165,6 +186,9 @@ bool profile_gemm_reduce_impl(int do_verification,
DeviceMem d0_device_buf(sizeof(DDataType) * d0_m_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()); DeviceMem d1_device_buf(sizeof(DDataType) * d1_m_device_result.mDesc.GetElementSpace());
auto dxs_global = ck::make_tuple(static_cast<DDataType*>(d0_device_buf.GetDeviceBuffer()),
static_cast<DDataType*>(d1_device_buf.GetDeviceBuffer()));
a_device_buf.ToDevice(a_m_k.mData.data()); a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data()); b_device_buf.ToDevice(b_k_n.mData.data());
...@@ -226,8 +250,7 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -226,8 +250,7 @@ bool profile_gemm_reduce_impl(int do_verification,
gemm_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()), gemm_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()), static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()), static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
static_cast<DDataType*>(d0_device_buf.GetDeviceBuffer()), dxs_global,
static_cast<DDataType*>(d1_device_buf.GetDeviceBuffer()),
M, M,
N, N,
K, K,
...@@ -237,42 +260,25 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -237,42 +260,25 @@ bool profile_gemm_reduce_impl(int do_verification,
a_element_op, a_element_op,
b_element_op, b_element_op,
c_element_op, c_element_op,
d1_element_op); dxs_in_element_op,
dxs_out_element_op);
auto invoker_ptr = gemm_ptr->MakeInvokerPointer(); auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
if(gemm_ptr->IsSupportedArgument(argument_ptr.get())) if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{ {
// warm up // init DO, D1 to 0
invoker_ptr->Run(argument_ptr.get()); d0_device_buf.SetZero();
d1_device_buf.SetZero();
// 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; float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::string gemm_name = gemm_ptr->GetTypeString(); std::string gemm_name = gemm_ptr->GetTypeString();
std::size_t flop = std::size_t(2) * M * N * K; std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * M + std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(CDataType) * M * N + sizeof(CDataType) * N; sizeof(CDataType) * M * N + sizeof(CDataType) * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time; float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
......
profiler/include/profile_grouped_gemm_impl.hpp
View file @ f9c478e2
...@@ -43,19 +43,20 @@ namespace profiler { ...@@ -43,19 +43,20 @@ namespace profiler {
template <typename ADataType, template <typename ADataType,
typename BDataType, typename BDataType,
typename CDataType, typename CDataType,
typename AccDataType,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
typename CLayout> typename CLayout>
void profile_grouped_gemm_impl(int do_verification, void profile_grouped_gemm_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
std::vector<int> Ms, const std::vector<int>& Ms,
std::vector<int> Ns, const std::vector<int>& Ns,
std::vector<int> Ks, const std::vector<int>& Ks,
std::vector<int> StrideAs, const std::vector<int>& StrideAs,
std::vector<int> StrideBs, const std::vector<int>& StrideBs,
std::vector<int> StrideCs) const std::vector<int>& StrideCs)
{ {
auto f_host_tensor_descriptor = auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
...@@ -71,7 +72,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -71,7 +72,7 @@ void profile_grouped_gemm_impl(int do_verification,
} }
}; };
int group_count = Ms.size(); std::size_t group_count = Ms.size();
if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() && if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() &&
group_count == StrideBs.size() && group_count == StrideCs.size())) group_count == StrideBs.size() && group_count == StrideCs.size()))
...@@ -83,7 +84,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -83,7 +84,7 @@ void profile_grouped_gemm_impl(int do_verification,
std::vector<Tensor<BDataType>> b_k_n; std::vector<Tensor<BDataType>> b_k_n;
std::vector<Tensor<CDataType>> c_m_n_device_results; std::vector<Tensor<CDataType>> c_m_n_device_results;
for(int i = 0; i < Ms.size(); i++) for(std::size_t i = 0; i < group_count; i++)
{ {
a_m_k.push_back( a_m_k.push_back(
Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{}))); Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{})));
...@@ -144,7 +145,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -144,7 +145,7 @@ void profile_grouped_gemm_impl(int do_verification,
gemm_shapes.reserve(group_count); gemm_shapes.reserve(group_count);
for(int i = 0; i < group_count; i++) for(std::size_t i = 0; i < group_count; i++)
{ {
a_device_buf.emplace_back( a_device_buf.emplace_back(
std::make_unique<DeviceMem>(sizeof(ADataType) * a_m_k[i].mDesc.GetElementSpace())); std::make_unique<DeviceMem>(sizeof(ADataType) * a_m_k[i].mDesc.GetElementSpace()));
...@@ -231,10 +232,11 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -231,10 +232,11 @@ void profile_grouped_gemm_impl(int do_verification,
{ {
std::string gemm_name = gemm_ptr->GetTypeString(); std::string gemm_name = gemm_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = 0, num_btype = 0; std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < gemm_shapes.size(); i++) for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{ {
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i]; flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
...@@ -258,7 +260,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -258,7 +260,7 @@ void profile_grouped_gemm_impl(int do_verification,
if(do_verification) if(do_verification)
{ {
for(int i = 0; i < gemm_shapes.size(); i++) for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{ {
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data()); c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
...@@ -270,6 +272,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -270,6 +272,7 @@ void profile_grouped_gemm_impl(int do_verification,
ck::tensor_operation::host::ReferenceGemm<ADataType, ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType, BDataType,
CDataType, CDataType,
AccDataType,
AElementOp, AElementOp,
BElementOp, BElementOp,
CElementOp>; CElementOp>;
......
profiler/include/profile_reduce_impl.hpp
View file @ f9c478e2
...@@ -5,74 +5,77 @@ ...@@ -5,74 +5,77 @@
#include "device_reduce_instance.hpp" #include "device_reduce_instance.hpp"
#include "reduction_enums.hpp" #include "reduction_enums.hpp"
#include "host_reduction.hpp" #include "host_reduction.hpp"
#include "host_common_util.hpp"
#include "host_tensor_generator.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace device_reduce_instance { namespace device_reduce_instance {
template <int Rank, int NumReduceDim, int ReduceOpId, int NanOpt, int IndicesOpt> template <int Rank, int NumReduceDim, int ReduceOpId, bool PropagateNan, bool UseIndex>
struct ReduceDescription struct ReduceDescription
{ {
static constexpr int Rank_ = Rank; static constexpr int Rank_ = Rank;
static constexpr int NumReduceDim_ = NumReduceDim; static constexpr int NumReduceDim_ = NumReduceDim;
static constexpr int ReduceOpId_ = ReduceOpId; static constexpr int ReduceOpId_ = ReduceOpId;
static constexpr int NanOpt_ = NanOpt; static constexpr int PropagateNan_ = PropagateNan;
static constexpr int IndicesOpt_ = IndicesOpt; static constexpr int UseIndex_ = UseIndex;
}; };
using reduce_description_instances = std::tuple<ReduceDescription<4, 3, 0, 0, 0>, // for ADD using reduce_description_instances =
ReduceDescription<4, 4, 0, 0, 0>, std::tuple<ReduceDescription<4, 3, 0, false, false>, // for ADD
ReduceDescription<4, 1, 0, 0, 0>, ReduceDescription<4, 4, 0, false, false>,
ReduceDescription<2, 1, 0, 0, 0>, ReduceDescription<4, 1, 0, false, false>,
ReduceDescription<2, 1, 0, false, false>,
ReduceDescription<4, 3, 5, 0, 0>, // for AVG
ReduceDescription<4, 4, 5, 0, 0>, ReduceDescription<4, 3, 5, false, false>, // for AVG
ReduceDescription<4, 1, 5, 0, 0>, ReduceDescription<4, 4, 5, false, false>,
ReduceDescription<2, 1, 5, 0, 0>, ReduceDescription<4, 1, 5, false, false>,
ReduceDescription<2, 1, 5, false, false>,
ReduceDescription<4, 3, 7, 0, 0>, // for NORM2
ReduceDescription<4, 4, 7, 0, 0>, ReduceDescription<4, 3, 7, false, false>, // for NORM2
ReduceDescription<4, 1, 7, 0, 0>, ReduceDescription<4, 4, 7, false, false>,
ReduceDescription<2, 1, 7, 0, 0>, ReduceDescription<4, 1, 7, false, false>,
ReduceDescription<2, 1, 7, false, false>,
ReduceDescription<4, 3, 2, 0, 0>, // for MIN
ReduceDescription<4, 4, 2, 0, 0>, ReduceDescription<4, 3, 2, false, false>, // for MIN
ReduceDescription<4, 1, 2, 0, 0>, ReduceDescription<4, 4, 2, false, false>,
ReduceDescription<2, 1, 2, 0, 0>, ReduceDescription<4, 1, 2, false, false>,
ReduceDescription<4, 3, 3, 0, 0>, // for MAX ReduceDescription<2, 1, 2, false, false>,
ReduceDescription<4, 4, 3, 0, 0>, ReduceDescription<4, 3, 3, false, false>, // for MAX
ReduceDescription<4, 1, 3, 0, 0>, ReduceDescription<4, 4, 3, false, false>,
ReduceDescription<2, 1, 3, 0, 0>, ReduceDescription<4, 1, 3, false, false>,
ReduceDescription<4, 3, 4, 0, 0>, // for AMAX ReduceDescription<2, 1, 3, false, false>,
ReduceDescription<4, 4, 4, 0, 0>, ReduceDescription<4, 3, 4, false, false>, // for AMAX
ReduceDescription<4, 1, 4, 0, 0>, ReduceDescription<4, 4, 4, false, false>,
ReduceDescription<2, 1, 4, 0, 0>, ReduceDescription<4, 1, 4, false, false>,
ReduceDescription<2, 1, 4, false, false>,
ReduceDescription<4, 3, 2, 0, 1>, // for MIN
ReduceDescription<4, 4, 2, 0, 1>, ReduceDescription<4, 3, 2, false, true>, // for MIN
ReduceDescription<4, 1, 2, 0, 1>, ReduceDescription<4, 4, 2, false, true>,
ReduceDescription<2, 1, 2, 0, 1>, ReduceDescription<4, 1, 2, false, true>,
ReduceDescription<4, 3, 3, 0, 1>, // for MAX ReduceDescription<2, 1, 2, false, true>,
ReduceDescription<4, 4, 3, 0, 1>, ReduceDescription<4, 3, 3, false, true>, // for MAX
ReduceDescription<4, 1, 3, 0, 1>, ReduceDescription<4, 4, 3, false, true>,
ReduceDescription<2, 1, 3, 0, 1>, ReduceDescription<4, 1, 3, false, true>,
ReduceDescription<4, 3, 4, 0, 1>, // for AMAX ReduceDescription<2, 1, 3, false, true>,
ReduceDescription<4, 4, 4, 0, 1>, ReduceDescription<4, 3, 4, false, true>, // for AMAX
ReduceDescription<4, 1, 4, 0, 1>, ReduceDescription<4, 4, 4, false, true>,
ReduceDescription<2, 1, 4, 0, 1>>; ReduceDescription<4, 1, 4, false, true>,
ReduceDescription<2, 1, 4, false, true>>;
template <typename DescriptionType> template <typename DescriptionType>
bool description_match(const DescriptionType& description, bool description_match(const DescriptionType& description,
int Rank, int Rank,
const std::vector<int>& reduceDims, const std::vector<int>& reduceDims,
ReduceTensorOp ReduceOpId, ReduceTensorOp ReduceOpId,
NanPropagation NanOpt, bool PropagateNan,
ReduceTensorIndices IndicesOpt) bool UseIndex)
{ {
if(description.Rank_ != Rank || description.ReduceOpId_ != static_cast<int>(ReduceOpId) || if(description.Rank_ != Rank || description.ReduceOpId_ != static_cast<int>(ReduceOpId) ||
description.NanOpt_ != static_cast<int>(NanOpt) || description.PropagateNan_ != static_cast<int>(PropagateNan) ||
description.IndicesOpt_ != static_cast<int>(IndicesOpt)) description.UseIndex_ != static_cast<int>(UseIndex))
return (false); return (false);
if(DescriptionType::NumReduceDim_ != reduceDims.size()) if(DescriptionType::NumReduceDim_ != reduceDims.size())
...@@ -116,48 +119,18 @@ static inline std::vector<int> get_invariant_dims(const std::vector<int>& reduce ...@@ -116,48 +119,18 @@ static inline std::vector<int> get_invariant_dims(const std::vector<int>& reduce
return invariantDims; return invariantDims;
}; };
template <typename T>
static void dumpBufferToFile(const char* fileName, T* data, size_t dataNumItems)
{
std::ofstream outFile(fileName, std::ios::binary);
if(outFile)
{
outFile.write(reinterpret_cast<char*>(data), dataNumItems * sizeof(T));
outFile.close();
std::cout << "Write output to file " << fileName << std::endl;
}
else
{
std::cout << "Could not open file " << fileName << " for writing" << std::endl;
}
};
// map the data type used by the GPU kernels to the corresponding type used by the host codes
template <typename InType>
struct type_mapping
{
using OutType = InType;
};
template <>
struct type_mapping<ck::half_t>
{
using OutType = half_float::half;
};
template <typename InDataType, template <typename InDataType,
typename AccDataType, typename AccDataType,
typename OutDataType, typename OutDataType,
int Rank, int Rank,
int NumReduceDim, int NumReduceDim,
ReduceTensorOp ReduceOpId, ReduceTensorOp ReduceOpId,
NanPropagation NanOpt, bool PropagateNan,
ReduceTensorIndices IndicesOpt> bool UseIndex>
void profile_reduce_impl_impl(bool do_verification, bool profile_reduce_impl_impl(bool do_verification,
int init_method, int init_method,
bool do_log,
bool do_dumpout, bool do_dumpout,
int nrepeat, bool time_kernel,
const std::vector<size_t>& inLengths, const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims, const std::vector<int>& reduceDims,
float alpha, float alpha,
...@@ -166,15 +139,13 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -166,15 +139,13 @@ void profile_reduce_impl_impl(bool do_verification,
using namespace ck::tensor_operation::device; using namespace ck::tensor_operation::device;
using namespace ck::tensor_operation::device::device_reduce_instance; using namespace ck::tensor_operation::device::device_reduce_instance;
using namespace ck::host_reduce; using namespace ck::host_reduce;
using ck::host_common::dumpBufferToFile;
constexpr bool op_support_indices = constexpr bool op_support_indices =
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX || (ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX); ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = constexpr bool OutputIndex = (op_support_indices && UseIndex);
(op_support_indices && (IndicesOpt != ReduceTensorIndices::NO_INDICES));
constexpr bool PropagateNan = (NanOpt == NanPropagation::PROPAGATE_NAN);
constexpr bool out_support_atomic_add = std::is_same<OutDataType, float>::value; constexpr bool out_support_atomic_add = std::is_same<OutDataType, float>::value;
constexpr bool op_support_atomic_add = constexpr bool op_support_atomic_add =
...@@ -195,8 +166,7 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -195,8 +166,7 @@ void profile_reduce_impl_impl(bool do_verification,
(op_support_indices && !std::is_same<AccDataType, float>::value); (op_support_indices && !std::is_same<AccDataType, float>::value);
// 1) The indices can only be used when the reduction operation is indexable // 1) The indices can only be used when the reduction operation is indexable
constexpr bool invalid_reduce_3 = constexpr bool invalid_reduce_3 = (!op_support_indices && UseIndex);
(!op_support_indices && IndicesOpt != ReduceTensorIndices::NO_INDICES);
// 1) If InDataType is int8_t, must use int8_t as AccDataType for indexable reduction operations // 1) If InDataType is int8_t, must use int8_t as AccDataType for indexable reduction operations
// 2) If InDataType is int8_t, must use int32_t as AccDataType for non-indexable reduction // 2) If InDataType is int8_t, must use int32_t as AccDataType for non-indexable reduction
...@@ -219,6 +189,8 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -219,6 +189,8 @@ void profile_reduce_impl_impl(bool do_verification,
constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2 || invalid_reduce_3 || constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2 || invalid_reduce_3 ||
invalid_reduce_4 || invalid_reduce_5 || invalid_reduce_6); invalid_reduce_4 || invalid_reduce_5 || invalid_reduce_6);
bool pass = true;
if constexpr(!invalid_reduce) if constexpr(!invalid_reduce)
{ {
Tensor<InDataType> in(inLengths); Tensor<InDataType> in(inLengths);
...@@ -282,7 +254,7 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -282,7 +254,7 @@ void profile_reduce_impl_impl(bool do_verification,
if(beta != 0.0f) if(beta != 0.0f)
out_dev.ToDevice(out.mData.data()); out_dev.ToDevice(out.mData.data());
size_t indicesSizeInBytes = NeedIndices ? out.mDesc.GetElementSize() * sizeof(int) : 0; size_t indicesSizeInBytes = OutputIndex ? out.mDesc.GetElementSize() * sizeof(int) : 0;
DeviceMem out_indices_dev(indicesSizeInBytes); DeviceMem out_indices_dev(indicesSizeInBytes);
...@@ -295,29 +267,11 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -295,29 +267,11 @@ void profile_reduce_impl_impl(bool do_verification,
using AccElementwiseOperation_0 = using AccElementwiseOperation_0 =
typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>:: typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::
AccElementwiseOperation; AccElementwiseOperation;
using InElementwiseOperation_1 =
typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::
InElementwiseOperation;
using AccElementwiseOperation_1 =
typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::
AccElementwiseOperation;
using InElementwiseOperation_2 =
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
InElementwiseOperation;
using AccElementwiseOperation_2 =
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
AccElementwiseOperation;
using DeviceReduceInstPtr0 = using DeviceReduceInstPtr0 =
DeviceReducePtr<InElementwiseOperation_0, AccElementwiseOperation_0>; DeviceReducePtr<InElementwiseOperation_0, AccElementwiseOperation_0>;
using DeviceReduceInstPtr1 =
DeviceReducePtr<InElementwiseOperation_1, AccElementwiseOperation_1>;
using DeviceReduceInstPtr2 =
DeviceReducePtr<InElementwiseOperation_2, AccElementwiseOperation_2>;
std::vector<DeviceReduceInstPtr0> reduce0_ptrs; std::vector<DeviceReduceInstPtr0> reduce0_ptrs;
std::vector<DeviceReduceInstPtr1> reduce1_ptrs;
std::vector<DeviceReduceInstPtr2> reduce2_ptrs;
add_device_reduce_instance_threadwise<InDataType, add_device_reduce_instance_threadwise<InDataType,
AccDataType, AccDataType,
...@@ -325,8 +279,8 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -325,8 +279,8 @@ void profile_reduce_impl_impl(bool do_verification,
Rank, Rank,
NumReduceDim, NumReduceDim,
ReduceOpId, ReduceOpId,
NanOpt, PropagateNan,
IndicesOpt>(reduce0_ptrs); UseIndex>(reduce0_ptrs);
add_device_reduce_instance_blockwise<InDataType, add_device_reduce_instance_blockwise<InDataType,
AccDataType, AccDataType,
...@@ -334,8 +288,8 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -334,8 +288,8 @@ void profile_reduce_impl_impl(bool do_verification,
Rank, Rank,
NumReduceDim, NumReduceDim,
ReduceOpId, ReduceOpId,
NanOpt, PropagateNan,
IndicesOpt>(reduce0_ptrs); UseIndex>(reduce0_ptrs);
if constexpr(use_atomic_add) if constexpr(use_atomic_add)
{ {
...@@ -345,35 +299,11 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -345,35 +299,11 @@ void profile_reduce_impl_impl(bool do_verification,
Rank, Rank,
NumReduceDim, NumReduceDim,
ReduceOpId, ReduceOpId,
NanOpt, PropagateNan,
IndicesOpt>(reduce0_ptrs); UseIndex>(reduce0_ptrs);
} }
else
{
add_device_reduce_instance_multiblock_partial_reduce<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOpId,
NanOpt,
IndicesOpt>(reduce1_ptrs);
};
// used for secondary reduction if(reduce0_ptrs.empty())
if constexpr(!use_atomic_add)
{
add_device_reduce_instance_blockwise_second_call<AccDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOpId,
NanOpt,
IndicesOpt>(reduce2_ptrs);
};
if(reduce0_ptrs.empty() && reduce1_ptrs.empty())
{ {
throw std::runtime_error("Wrong! No device REDUCE instance found"); throw std::runtime_error("Wrong! No device REDUCE instance found");
}; };
...@@ -387,23 +317,25 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -387,23 +317,25 @@ void profile_reduce_impl_impl(bool do_verification,
Rank, Rank,
NumReduceDim, NumReduceDim,
PropagateNan, PropagateNan,
NeedIndices> OutputIndex>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims); hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run( hostReduce.Run(
alpha, in.mData.data(), beta, out_ref.mData.data(), out_indices_ref.mData.data()); alpha, in.mData.data(), beta, out_ref.mData.data(), out_indices_ref.mData.data());
}; };
const auto i_inLengths = to_int_vector(inLengths); std::vector<ck::index_t> i_inLengths;
const auto i_inStrides = to_int_vector(inStrides); std::vector<ck::index_t> i_inStrides;
const auto i_outLengths = to_int_vector(outLengths); std::vector<ck::index_t> i_outLengths;
const auto i_outStrides = to_int_vector(outStrides); std::vector<ck::index_t> i_outStrides;
i_inLengths.assign(inLengths.begin(), inLengths.end());
i_inStrides.assign(inStrides.begin(), inStrides.end());
i_outLengths.assign(outLengths.begin(), outLengths.end());
i_outStrides.assign(outStrides.begin(), outStrides.end());
for(auto& reduce_ptr : reduce0_ptrs) for(auto& reduce_ptr : reduce0_ptrs)
{ {
auto wsSizeInBytes = reduce_ptr->GetWorkspaceSizeInBytes(i_inLengths, reduceDims);
DeviceMem ws_dev(wsSizeInBytes);
InElementwiseOperation_0 in_elementwise_op_0(static_cast<int32_t>(reduce_total_length)); InElementwiseOperation_0 in_elementwise_op_0(static_cast<int32_t>(reduce_total_length));
AccElementwiseOperation_0 acc_elementwise_op_0( AccElementwiseOperation_0 acc_elementwise_op_0(
...@@ -417,9 +349,9 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -417,9 +349,9 @@ void profile_reduce_impl_impl(bool do_verification,
alpha, alpha,
beta, beta,
in_dev.GetDeviceBuffer(), in_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(), out_dev.GetDeviceBuffer(),
out_indices_dev.GetDeviceBuffer(), out_indices_dev.GetDeviceBuffer(),
ws_dev.GetDeviceBuffer(),
in_elementwise_op_0, in_elementwise_op_0,
acc_elementwise_op_0); acc_elementwise_op_0);
...@@ -430,7 +362,8 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -430,7 +362,8 @@ void profile_reduce_impl_impl(bool do_verification,
auto invoker_ptr = reduce_ptr->MakeInvokerPointer(); auto invoker_ptr = reduce_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), nrepeat); float avg_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t num_bytes = std::size_t num_bytes =
invariant_total_length * reduce_total_length * sizeof(InDataType) + invariant_total_length * reduce_total_length * sizeof(InDataType) +
...@@ -438,8 +371,9 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -438,8 +371,9 @@ void profile_reduce_impl_impl(bool do_verification,
float gb_per_sec = num_bytes / 1.E6 / avg_time; float gb_per_sec = num_bytes / 1.E6 / avg_time;
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, " << reduce_name if(time_kernel)
<< std::endl; std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< reduce_name << std::endl;
if(gb_per_sec > best_gb_per_sec) if(gb_per_sec > best_gb_per_sec)
{ {
...@@ -449,22 +383,24 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -449,22 +383,24 @@ void profile_reduce_impl_impl(bool do_verification,
if(do_verification) if(do_verification)
{ {
bool single_pass;
out_dev.FromDevice(out.mData.data()); out_dev.FromDevice(out.mData.data());
ck::utils::check_err(out.mData, out_ref.mData); single_pass = ck::utils::check_err(out.mData, out_ref.mData);
if(NeedIndices) if(OutputIndex)
{ {
out_indices_dev.FromDevice(out_indices.mData.data()); out_indices_dev.FromDevice(out_indices.mData.data());
ck::utils::check_err(out_indices.mData, out_indices_ref.mData); single_pass = single_pass &&
; ck::utils::check_err(out_indices.mData, out_indices_ref.mData);
}; };
if(do_log) if(!single_pass)
{ {
LogRangeAsType<float>(std::cout << "out_host : ", out_ref.mData, ",") std::cout << "Fail Info: " << reduce_ptr->GetTypeString() << std::endl;
<< std::endl; }
LogRangeAsType<float>(std::cout << "out_device: ", out.mData, ",") << std::endl;
}; pass = pass && single_pass;
}; };
if(do_dumpout) if(do_dumpout)
...@@ -473,7 +409,7 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -473,7 +409,7 @@ void profile_reduce_impl_impl(bool do_verification,
dumpBufferToFile("dump_out.bin", out.mData.data(), out.mDesc.GetElementSize()); dumpBufferToFile("dump_out.bin", out.mData.data(), out.mDesc.GetElementSize());
dumpBufferToFile( dumpBufferToFile(
"dump_out_host.bin", out_ref.mData.data(), out_ref.mDesc.GetElementSize()); "dump_out_host.bin", out_ref.mData.data(), out_ref.mDesc.GetElementSize());
if(NeedIndices) if(OutputIndex)
{ {
dumpBufferToFile("dump_indices.bin", dumpBufferToFile("dump_indices.bin",
out_indices.mData.data(), out_indices.mData.data(),
...@@ -485,156 +421,34 @@ void profile_reduce_impl_impl(bool do_verification, ...@@ -485,156 +421,34 @@ void profile_reduce_impl_impl(bool do_verification,
}; };
}; };
for(auto& reduce_ptr : reduce1_ptrs) if(time_kernel)
{ std::cout << "Best Perf: " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s"
auto wsSizeInBytes = reduce_ptr->GetWorkspaceSizeInBytes(i_inLengths, reduceDims); << std::endl;
DeviceMem ws_dev(wsSizeInBytes);
InElementwiseOperation_1 in_elementwise_op_1(static_cast<int32_t>(reduce_total_length));
AccElementwiseOperation_1 acc_elementwise_op_1(
static_cast<int32_t>(reduce_total_length));
auto argument_ptr = reduce_ptr->MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
in_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer(),
out_indices_dev.GetDeviceBuffer(),
ws_dev.GetDeviceBuffer(),
in_elementwise_op_1,
acc_elementwise_op_1);
if(!reduce_ptr->IsSupportedArgument(argument_ptr.get()))
continue;
std::string reduce_name = reduce_ptr->GetTypeString();
auto invoker_ptr = reduce_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
std::size_t num_bytes =
invariant_total_length * reduce_total_length * sizeof(InDataType) +
invariant_total_length * sizeof(OutDataType);
std::vector<int> inLengths2 = reduce_ptr->GetWorkspace2dLengths(argument_ptr.get());
std::vector<int> inStrides2{inLengths2[1], 1};
for(auto& reduce2_ptr : reduce2_ptrs)
{
InElementwiseOperation_2 in_elementwise_op_2(
static_cast<int32_t>(reduce_total_length));
AccElementwiseOperation_2 acc_elementwise_op_2(
static_cast<int32_t>(reduce_total_length));
auto argument2_ptr =
reduce2_ptr->MakeArgumentPointer(inLengths2,
inStrides2,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
ws_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer(),
out_indices_dev.GetDeviceBuffer(),
ws_dev.GetDeviceBuffer(),
in_elementwise_op_2,
acc_elementwise_op_2);
if(!reduce2_ptr->IsSupportedArgument(argument2_ptr.get()))
continue;
std::string reduce2_name = reduce2_ptr->GetTypeString();
auto invoker2_ptr = reduce2_ptr->MakeInvokerPointer();
float avg_time_2 = invoker2_ptr->Run(argument2_ptr.get(), nrepeat);
std::size_t num_bytes_2 =
static_cast<size_t>(inLengths2[0]) * inLengths2[1] * sizeof(AccDataType);
float gb_per_sec = (num_bytes + num_bytes_2) / 1.E6 / (avg_time + avg_time_2);
std::cout << "Perf: " << (avg_time + avg_time_2) << " ms, " << gb_per_sec
<< " GB/s, " << reduce_name << " => " << reduce2_name << std::endl;
if(gb_per_sec > best_gb_per_sec)
{
best_avg_time = avg_time + avg_time_2;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
out_dev.FromDevice(out.mData.data());
ck::utils::check_err(out.mData, out_ref.mData);
if(NeedIndices)
{
out_indices_dev.FromDevice(out_indices.mData.data());
ck::utils::check_err(out_indices.mData, out_indices_ref.mData);
;
};
if(do_log)
{
LogRangeAsType<float>(std::cout << "out_host : ", out_ref.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "out_device: ", out.mData, ",")
<< std::endl;
}
}
if(do_dumpout)
{
dumpBufferToFile("dump_in.bin", in.mData.data(), in.mDesc.GetElementSize());
dumpBufferToFile("dump_out.bin", out.mData.data(), out.mDesc.GetElementSize());
dumpBufferToFile(
"dump_out_host.bin", out_ref.mData.data(), out_ref.mDesc.GetElementSize());
if(NeedIndices)
{
dumpBufferToFile("dump_indices.bin",
out_indices.mData.data(),
out_indices.mDesc.GetElementSize());
dumpBufferToFile("dump_indices_host.bin",
out_indices_ref.mData.data(),
out_indices_ref.mDesc.GetElementSize());
};
};
};
};
std::cout << "Best Perf: " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s"
<< std::endl;
} }
else else
{ {
std::cout << "The requested reduction operation is not supported, please check !!!" std::cout << "The requested reduction operation is not supported, please check !!!"
<< std::endl; << std::endl;
}; };
return pass;
}; };
template <typename InDataType, typename AccDataType, typename OutDataType> template <typename InDataType, typename AccDataType, typename OutDataType>
void profile_reduce_impl(bool do_verification, bool profile_reduce_impl(bool do_verification,
int init_method, int init_method,
bool do_log,
bool do_dumpout, bool do_dumpout,
int nrepeat, bool time_kernel,
const std::vector<size_t>& inLengths, const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims, const std::vector<int>& reduceDims,
ReduceTensorOp ReduceOpId, ReduceTensorOp ReduceOpId,
NanPropagation NanOpt, bool PropagateNan,
ReduceTensorIndices IndicesOpt, bool UseIndex,
float alpha, float alpha,
float beta) float beta)
{ {
bool matched = false; bool matched = false;
bool pass = true;
using tuple_of_description_instances = using tuple_of_description_instances =
tensor_operation::device::device_reduce_instance::reduce_description_instances; tensor_operation::device::device_reduce_instance::reduce_description_instances;
...@@ -648,29 +462,30 @@ void profile_reduce_impl(bool do_verification, ...@@ -648,29 +462,30 @@ void profile_reduce_impl(bool do_verification,
using descType = remove_cvref_t<decltype(std::get<i>(tuple_object))>; using descType = remove_cvref_t<decltype(std::get<i>(tuple_object))>;
if(!description_match( if(!description_match(
descType{}, inLengths.size(), reduceDims, ReduceOpId, NanOpt, IndicesOpt)) descType{}, inLengths.size(), reduceDims, ReduceOpId, PropagateNan, UseIndex))
return; return;
profile_reduce_impl_impl<InDataType, pass = pass &&
AccDataType, profile_reduce_impl_impl<InDataType,
OutDataType, AccDataType,
descType::Rank_, OutDataType,
descType::NumReduceDim_, descType::Rank_,
static_cast<ReduceTensorOp>(descType::ReduceOpId_), descType::NumReduceDim_,
static_cast<NanPropagation>(descType::NanOpt_), static_cast<ReduceTensorOp>(descType::ReduceOpId_),
static_cast<ReduceTensorIndices>(descType::IndicesOpt_)>( static_cast<bool>(descType::PropagateNan_),
do_verification, static_cast<bool>(descType::UseIndex_)>(do_verification,
init_method, init_method,
do_log, do_dumpout,
do_dumpout, time_kernel,
nrepeat, inLengths,
inLengths, reduceDims,
reduceDims, alpha,
alpha, beta);
beta);
matched = true; matched = true;
}); });
return pass;
}; };
} // namespace profiler } // namespace profiler
......
profiler/src/profile_batched_gemm.cpp
View file @ f9c478e2
...@@ -48,8 +48,8 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -48,8 +48,8 @@ int profile_batched_gemm(int argc, char* argv[])
printf(" 3: A[g, k, m] * B[g, n, k] = C[g, m, n])\n"); printf(" 3: A[g, k, m] * B[g, n, k] = C[g, m, n])\n");
printf("arg4: verification (0: no; 1: yes)\n"); printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n"); printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n"); printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n"); printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 14: M, N, K, StrideA, StrideB, StrideC, BatchCount\n"); printf("arg8 to 14: M, N, K, StrideA, StrideB, StrideC, BatchCount\n");
exit(1); exit(1);
} }
...@@ -59,7 +59,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -59,7 +59,7 @@ int profile_batched_gemm(int argc, char* argv[])
const bool do_verification = std::stoi(argv[4]); const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]); const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]); const bool do_log = std::stoi(argv[6]);
const int nrepeat = std::stoi(argv[7]); const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]); const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]); const int N = std::stoi(argv[9]);
...@@ -82,7 +82,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -82,7 +82,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -102,7 +102,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -102,7 +102,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -122,7 +122,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -122,7 +122,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -142,7 +142,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -142,7 +142,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -162,7 +162,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -162,7 +162,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -182,7 +182,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -182,7 +182,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -202,7 +202,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -202,7 +202,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -222,7 +222,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -222,7 +222,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -242,7 +242,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -242,7 +242,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -262,7 +262,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -262,7 +262,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -282,7 +282,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -282,7 +282,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -302,7 +302,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -302,7 +302,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -322,7 +322,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -322,7 +322,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -342,7 +342,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -342,7 +342,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -362,7 +362,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -362,7 +362,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -382,7 +382,7 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -382,7 +382,7 @@ int profile_batched_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -396,5 +396,5 @@ int profile_batched_gemm(int argc, char* argv[]) ...@@ -396,5 +396,5 @@ int profile_batched_gemm(int argc, char* argv[])
throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented"); throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
} }
return 1; return 0;
} }
profiler/src/profile_batched_gemm_reduce.cpp
View file @ f9c478e2
...@@ -33,8 +33,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[]) ...@@ -33,8 +33,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
printf(" 3: A[k, m] * B[n, k] = C[m, n])\n"); printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
printf("arg4: verification (0: no; 1: yes)\n"); printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n"); printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n"); printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n"); printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 14: M, N, K, StrideA, StrideB, StrideC, BatchCount\n"); printf("arg8 to 14: M, N, K, StrideA, StrideB, StrideC, BatchCount\n");
printf("arg15: split k into mulitiple batch\n"); printf("arg15: split k into mulitiple batch\n");
exit(1); exit(1);
...@@ -45,7 +45,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[]) ...@@ -45,7 +45,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
const bool do_verification = std::stoi(argv[4]); const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]); const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]); const bool do_log = std::stoi(argv[6]);
const int nrepeat = std::stoi(argv[7]); const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]); const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]); const int N = std::stoi(argv[9]);
...@@ -69,7 +69,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[]) ...@@ -69,7 +69,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -91,7 +91,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[]) ...@@ -91,7 +91,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -113,7 +113,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[]) ...@@ -113,7 +113,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -135,7 +135,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[]) ...@@ -135,7 +135,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -149,5 +149,5 @@ int profile_batched_gemm_reduce(int argc, char* argv[]) ...@@ -149,5 +149,5 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
throw std::runtime_error("wrong! this data_type & layout is not implemented"); throw std::runtime_error("wrong! this data_type & layout is not implemented");
} }
return 1; return 0;
} }
profiler/src/profile_conv_bwd_data.cpp deleted 100644 → 0
View file @ 7d85d04a
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "profile_conv_bwd_data_impl.hpp"
enum struct ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
};
enum struct ConvInputLayout
{
NCHW, // 0
NHWC, // 1
};
enum struct ConvWeightLayout
{
KCYX, // 0
KYXC, // 1
};
enum struct ConvOutputLayout
{
NKHW, // 0
NHWK, // 1
};
int profile_conv_bwd_data(int argc, char* argv[])
{
if(argc != 25)
{
printf("arg1: tensor operation (conv_bwd: BackwardConvolution)\n");
printf("arg2: data type (0: fp32; 1: fp16)\n");
printf("arg3: input tensor layout (0: NCHW; 1: NHWC)\n");
printf("arg4: weight tensor layout (0: KCYX; 1: KYXC)\n");
printf("arg5: output tensor layout (0: NKHW; 1: NHWK)\n");
printf("arg6: verification (0: no; 1: yes)\n");
printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg9: run kernel # of times (>1)\n");
printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(1);
}
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const auto in_layout = static_cast<ConvInputLayout>(std::stoi(argv[3]));
const auto wei_layout = static_cast<ConvWeightLayout>(std::stoi(argv[4]));
const auto out_layout = static_cast<ConvOutputLayout>(std::stoi(argv[5]));
const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]);
const int nrepeat = std::stoi(argv[9]);
const ck::index_t N = std::stoi(argv[10]);
const ck::index_t K = std::stoi(argv[11]);
const ck::index_t C = std::stoi(argv[12]);
const ck::index_t Y = std::stoi(argv[13]);
const ck::index_t X = std::stoi(argv[14]);
const ck::index_t Hi = std::stoi(argv[15]);
const ck::index_t Wi = std::stoi(argv[16]);
const ck::index_t conv_stride_h = std::stoi(argv[17]);
const ck::index_t conv_stride_w = std::stoi(argv[18]);
const ck::index_t conv_dilation_h = std::stoi(argv[19]);
const ck::index_t conv_dilation_w = std::stoi(argv[20]);
const ck::index_t in_left_pad_h = std::stoi(argv[21]);
const ck::index_t in_left_pad_w = std::stoi(argv[22]);
const ck::index_t in_right_pad_h = std::stoi(argv[23]);
const ck::index_t in_right_pad_w = std::stoi(argv[24]);
const ck::index_t YEff = (Y - 1) * conv_dilation_h + 1;
const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
if(data_type == ConvDataType::F32_F32_F32 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_bwd_data_impl<2,
float,
float,
float,
float,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else if(data_type == ConvDataType::F16_F16_F16 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_bwd_data_impl<2,
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else if(data_type == ConvDataType::BF16_BF16_BF16 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_bwd_data_impl<2,
uint16_t,
uint16_t,
uint16_t,
float,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else if(data_type == ConvDataType::INT8_INT8_INT8 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_bwd_data_impl<2,
int8_t,
int8_t,
int8_t,
int32_t,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else
{
throw std::runtime_error("wrong! this Conv data_type & layout is not implemented");
}
return 1;
}
profiler/src/profile_conv_bwd_weight.cpp
View file @ f9c478e2
...@@ -58,7 +58,7 @@ int profile_conv_bwd_weight(int argc, char* argv[]) ...@@ -58,7 +58,7 @@ int profile_conv_bwd_weight(int argc, char* argv[])
const bool do_verification = std::stoi(argv[6]); const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]); const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]); const bool do_log = std::stoi(argv[8]);
const int nrepeat = std::stoi(argv[9]); const bool time_kernel = std::stoi(argv[9]);
const ck::index_t N = std::stoi(argv[10]); const ck::index_t N = std::stoi(argv[10]);
const ck::index_t K = std::stoi(argv[11]); const ck::index_t K = std::stoi(argv[11]);
...@@ -98,7 +98,7 @@ int profile_conv_bwd_weight(int argc, char* argv[]) ...@@ -98,7 +98,7 @@ int profile_conv_bwd_weight(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
N, N,
K, K,
C, C,
...@@ -124,7 +124,7 @@ int profile_conv_bwd_weight(int argc, char* argv[]) ...@@ -124,7 +124,7 @@ int profile_conv_bwd_weight(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
N, N,
K, K,
C, C,
...@@ -142,5 +142,5 @@ int profile_conv_bwd_weight(int argc, char* argv[]) ...@@ -142,5 +142,5 @@ int profile_conv_bwd_weight(int argc, char* argv[])
throw std::runtime_error("wrong! this Conv data_type & layout is not implemented"); throw std::runtime_error("wrong! this Conv data_type & layout is not implemented");
} }
return 1; return 0;
} }
profiler/src/profile_conv_fwd_bias_relu.cpp
View file @ f9c478e2
...@@ -42,7 +42,7 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[]) ...@@ -42,7 +42,7 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[])
printf("arg6: verification (0: no; 1: yes)\n"); printf("arg6: verification (0: no; 1: yes)\n");
printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n"); printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n"); printf("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg9: run kernel # of times (>1)\n"); printf("arg9: time kernel (0=n0, 1=yes)\n");
printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, " printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n"); "RightPx\n");
exit(1); exit(1);
...@@ -55,7 +55,7 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[]) ...@@ -55,7 +55,7 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[])
const bool do_verification = std::stoi(argv[6]); const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]); const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]); const bool do_log = std::stoi(argv[8]);
const int nrepeat = std::stoi(argv[9]); const bool time_kernel = std::stoi(argv[9]);
const ck::index_t N = std::stoi(argv[10]); const ck::index_t N = std::stoi(argv[10]);
const ck::index_t K = std::stoi(argv[11]); const ck::index_t K = std::stoi(argv[11]);
...@@ -93,7 +93,7 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[]) ...@@ -93,7 +93,7 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
N, N,
K, K,
C, C,
...@@ -110,5 +110,5 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[]) ...@@ -110,5 +110,5 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[])
throw std::runtime_error("wrong! data_type & layout for this operator is not implemented"); throw std::runtime_error("wrong! data_type & layout for this operator is not implemented");
} }
return 1; return 0;
} }
profiler/src/profile_conv_fwd_bias_relu_add.cpp
View file @ f9c478e2
...@@ -43,7 +43,7 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[]) ...@@ -43,7 +43,7 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[])
printf("arg6: verification (0: no; 1: yes)\n"); printf("arg6: verification (0: no; 1: yes)\n");
printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n"); printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n"); printf("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg9: run kernel # of times (>1)\n"); printf("arg9: time kernel (0=n0, 1=yes)\n");
printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, " printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n"); "RightPx\n");
exit(1); exit(1);
...@@ -56,7 +56,7 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[]) ...@@ -56,7 +56,7 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[])
const bool do_verification = std::stoi(argv[6]); const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]); const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]); const bool do_log = std::stoi(argv[8]);
const int nrepeat = std::stoi(argv[9]); const bool time_kernel = std::stoi(argv[9]);
const ck::index_t N = std::stoi(argv[10]); const ck::index_t N = std::stoi(argv[10]);
const ck::index_t K = std::stoi(argv[11]); const ck::index_t K = std::stoi(argv[11]);
...@@ -94,7 +94,7 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[]) ...@@ -94,7 +94,7 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
N, N,
K, K,
C, C,
...@@ -111,5 +111,5 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[]) ...@@ -111,5 +111,5 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[])
throw std::runtime_error("wrong! data_type & layout for this operator is not implemented"); throw std::runtime_error("wrong! data_type & layout for this operator is not implemented");
} }
return 1; return 0;
} }
profiler/src/profile_conv_fwd_bias_relu_atomic_add.cpp
View file @ f9c478e2
...@@ -43,7 +43,7 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[]) ...@@ -43,7 +43,7 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[])
printf("arg6: verification (0: no; 1: yes)\n"); printf("arg6: verification (0: no; 1: yes)\n");
printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n"); printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n"); printf("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg9: run kernel # of times (>1)\n"); printf("arg9: time kernel (0=n0, 1=yes)\n");
printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, " printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n"); "RightPx\n");
exit(1); exit(1);
...@@ -56,7 +56,7 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[]) ...@@ -56,7 +56,7 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[])
const bool do_verification = std::stoi(argv[6]); const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]); const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]); const bool do_log = std::stoi(argv[8]);
const int nrepeat = std::stoi(argv[9]); const bool time_kernel = std::stoi(argv[9]);
const ck::index_t N = std::stoi(argv[10]); const ck::index_t N = std::stoi(argv[10]);
const ck::index_t K = std::stoi(argv[11]); const ck::index_t K = std::stoi(argv[11]);
...@@ -95,7 +95,7 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[]) ...@@ -95,7 +95,7 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
N, N,
K, K,
C, C,
...@@ -112,5 +112,5 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[]) ...@@ -112,5 +112,5 @@ int profile_conv_fwd_bias_relu_atomic_add(int argc, char* argv[])
throw std::runtime_error("wrong! data_type & layout for this operator is not implemented"); throw std::runtime_error("wrong! data_type & layout for this operator is not implemented");
} }
return 1; return 0;
} }
profiler/src/profile_convnd_bwd_data.cpp
View file @ f9c478e2
...@@ -39,40 +39,40 @@ ck::utils::conv::ConvParams parse_conv_params(int num_dim_spatial, char* argv[], ...@@ -39,40 +39,40 @@ ck::utils::conv::ConvParams parse_conv_params(int num_dim_spatial, char* argv[],
// (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right) // (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
ck::utils::conv::ConvParams params; ck::utils::conv::ConvParams params;
params.num_dim_spatial = num_dim_spatial; params.num_dim_spatial_ = num_dim_spatial;
params.N = std::stoi(argv[arg_idx++]); params.N_ = std::stoi(argv[arg_idx++]);
params.K = std::stoi(argv[arg_idx++]); params.K_ = std::stoi(argv[arg_idx++]);
params.C = std::stoi(argv[arg_idx++]); params.C_ = std::stoi(argv[arg_idx++]);
params.filter_spatial_lengths.resize(num_dim_spatial); params.filter_spatial_lengths_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i) for(int i = 0; i < num_dim_spatial; ++i)
{ {
params.filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]); params.filter_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
} }
params.input_spatial_lengths.resize(num_dim_spatial); params.input_spatial_lengths_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i) for(int i = 0; i < num_dim_spatial; ++i)
{ {
params.input_spatial_lengths[i] = std::stoi(argv[arg_idx++]); params.input_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
} }
params.conv_filter_strides.resize(num_dim_spatial); params.conv_filter_strides_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i) for(int i = 0; i < num_dim_spatial; ++i)
{ {
params.conv_filter_strides[i] = std::stoi(argv[arg_idx++]); params.conv_filter_strides_[i] = std::stoi(argv[arg_idx++]);
} }
params.conv_filter_dilations.resize(num_dim_spatial); params.conv_filter_dilations_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i) for(int i = 0; i < num_dim_spatial; ++i)
{ {
params.conv_filter_dilations[i] = std::stoi(argv[arg_idx++]); params.conv_filter_dilations_[i] = std::stoi(argv[arg_idx++]);
} }
params.input_left_pads.resize(num_dim_spatial); params.input_left_pads_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i) for(int i = 0; i < num_dim_spatial; ++i)
{ {
params.input_left_pads[i] = std::stoi(argv[arg_idx++]); params.input_left_pads_[i] = std::stoi(argv[arg_idx++]);
} }
params.input_right_pads.resize(num_dim_spatial); params.input_right_pads_.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i) for(int i = 0; i < num_dim_spatial; ++i)
{ {
params.input_right_pads[i] = std::stoi(argv[arg_idx++]); params.input_right_pads_[i] = std::stoi(argv[arg_idx++]);
} }
return params; return params;
...@@ -95,7 +95,7 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial) ...@@ -95,7 +95,7 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
printf("arg6: verification (0: no; 1: yes)\n"); printf("arg6: verification (0: no; 1: yes)\n");
printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n"); printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n"); printf("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg9: run kernel # of times (>1)\n"); printf("arg9: time kernel (0=n0, 1=yes)\n");
printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, " printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n"); "RightPx\n");
return 1; return 1;
...@@ -108,7 +108,7 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial) ...@@ -108,7 +108,7 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
const bool do_verification = std::stoi(argv[6]); const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]); const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]); const bool do_log = std::stoi(argv[8]);
const int nrepeat = std::stoi(argv[9]); const bool time_kernel = std::stoi(argv[9]);
ck::utils::conv::ConvParams params = parse_conv_params(num_dim_spatial, argv, preParams); ck::utils::conv::ConvParams params = parse_conv_params(num_dim_spatial, argv, preParams);
...@@ -132,17 +132,17 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial) ...@@ -132,17 +132,17 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
params.N, params.N_,
params.K, params.K_,
params.C, params.C_,
params.input_spatial_lengths, params.input_spatial_lengths_,
params.filter_spatial_lengths, params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(), params.GetOutputSpatialLengths(),
params.conv_filter_strides, params.conv_filter_strides_,
params.conv_filter_dilations, params.conv_filter_dilations_,
params.input_left_pads, params.input_left_pads_,
params.input_right_pads); params.input_right_pads_);
break; break;
case 2: case 2:
...@@ -157,17 +157,17 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial) ...@@ -157,17 +157,17 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
params.N, params.N_,
params.K, params.K_,
params.C, params.C_,
params.input_spatial_lengths, params.input_spatial_lengths_,
params.filter_spatial_lengths, params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(), params.GetOutputSpatialLengths(),
params.conv_filter_strides, params.conv_filter_strides_,
params.conv_filter_dilations, params.conv_filter_dilations_,
params.input_left_pads, params.input_left_pads_,
params.input_right_pads); params.input_right_pads_);
break; break;
case 3: case 3:
...@@ -182,17 +182,17 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial) ...@@ -182,17 +182,17 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
params.N, params.N_,
params.K, params.K_,
params.C, params.C_,
params.input_spatial_lengths, params.input_spatial_lengths_,
params.filter_spatial_lengths, params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(), params.GetOutputSpatialLengths(),
params.conv_filter_strides, params.conv_filter_strides_,
params.conv_filter_dilations, params.conv_filter_dilations_,
params.input_left_pads, params.input_left_pads_,
params.input_right_pads); params.input_right_pads_);
break; break;
default: break; default: break;
......
profiler/src/profile_convnd_fwd.cpp
View file @ f9c478e2
...@@ -5,7 +5,7 @@ ...@@ -5,7 +5,7 @@
#include <vector> #include <vector>
#include <half.hpp> #include <half.hpp>
#include "conv_fwd_util.hpp" #include "conv_util.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "fill.hpp" #include "fill.hpp"
#include "profile_convnd_fwd.hpp" #include "profile_convnd_fwd.hpp"
...@@ -119,7 +119,7 @@ template <int NDim, ...@@ -119,7 +119,7 @@ template <int NDim,
void profile_convnd_instances_impl(const ck::utils::conv::ConvParams& params, void profile_convnd_instances_impl(const ck::utils::conv::ConvParams& params,
bool do_verification, bool do_verification,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
int init_method, int init_method,
ConvLayouts) ConvLayouts)
{ {
...@@ -185,7 +185,7 @@ void profile_convnd_instances_impl(const ck::utils::conv::ConvParams& params, ...@@ -185,7 +185,7 @@ void profile_convnd_instances_impl(const ck::utils::conv::ConvParams& params,
reference_conv_fwd_fun); reference_conv_fwd_fun);
auto best_conf = run_engine.Profile( auto best_conf = run_engine.Profile(
conv::ConvolutionFwdInstances<InDataType, WeiDataType, OutDataType>::template Get<NDim>(), conv::ConvolutionFwdInstances<InDataType, WeiDataType, OutDataType>::template Get<NDim>(),
nrepeat, time_kernel,
do_verification, do_verification,
do_log); do_log);
...@@ -201,7 +201,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -201,7 +201,7 @@ void profile_convnd_instances(ConvDataType data_type,
const ck::utils::conv::ConvParams& params, const ck::utils::conv::ConvParams& params,
bool do_verification, bool do_verification,
bool do_log, bool do_log,
int nrepeat, bool time_kernel,
int init_method) int init_method)
{ {
switch(data_layout) switch(data_layout)
...@@ -214,7 +214,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -214,7 +214,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{}); ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break; break;
...@@ -223,7 +223,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -223,7 +223,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{}); ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break; break;
...@@ -232,7 +232,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -232,7 +232,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{}); ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break; break;
...@@ -241,7 +241,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -241,7 +241,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{}); ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break; break;
...@@ -256,7 +256,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -256,7 +256,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{}); ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break; break;
...@@ -265,7 +265,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -265,7 +265,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{}); ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break; break;
...@@ -274,7 +274,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -274,7 +274,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{}); ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break; break;
...@@ -283,7 +283,7 @@ void profile_convnd_instances(ConvDataType data_type, ...@@ -283,7 +283,7 @@ void profile_convnd_instances(ConvDataType data_type,
params, params,
do_verification, do_verification,
do_log, do_log,
nrepeat, time_kernel,
init_method, init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{}); ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break; break;
...@@ -304,7 +304,7 @@ int ck::profiler::profile_convnd_fwd(int argc, char* argv[]) ...@@ -304,7 +304,7 @@ int ck::profiler::profile_convnd_fwd(int argc, char* argv[])
bool do_verification{true}; bool do_verification{true};
int init_method{2}; int init_method{2};
bool do_log{false}; bool do_log{false};
int nrepeat{100}; bool time_kernel{false};
int num_dim_spatial{2}; int num_dim_spatial{2};
ConvParams params; ConvParams params;
...@@ -318,7 +318,7 @@ int ck::profiler::profile_convnd_fwd(int argc, char* argv[]) ...@@ -318,7 +318,7 @@ int ck::profiler::profile_convnd_fwd(int argc, char* argv[])
do_verification = std::stoi(argv[4]); do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]); init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]); do_log = std::stoi(argv[6]);
nrepeat = std::stoi(argv[7]); time_kernel = std::stoi(argv[7]);
num_dim_spatial = std::stoi(argv[8]); num_dim_spatial = std::stoi(argv[8]);
} }
if(argc >= 10) if(argc >= 10)
...@@ -332,20 +332,20 @@ int ck::profiler::profile_convnd_fwd(int argc, char* argv[]) ...@@ -332,20 +332,20 @@ int ck::profiler::profile_convnd_fwd(int argc, char* argv[])
{ {
case 1: case 1:
profile_convnd_instances<1>( profile_convnd_instances<1>(
data_type, data_layout, params, do_verification, do_log, nrepeat, init_method); data_type, data_layout, params, do_verification, do_log, time_kernel, init_method);
break; break;
case 2: case 2:
profile_convnd_instances<2>( profile_convnd_instances<2>(
data_type, data_layout, params, do_verification, do_log, nrepeat, init_method); data_type, data_layout, params, do_verification, do_log, time_kernel, init_method);
break; break;
case 3: case 3:
profile_convnd_instances<3>( profile_convnd_instances<3>(
data_type, data_layout, params, do_verification, do_log, nrepeat, init_method); data_type, data_layout, params, do_verification, do_log, time_kernel, init_method);
break; break;
default: default:
throw std::runtime_error("profile_conv_fwd: unsupported num_dim_spatial value: " + throw std::runtime_error("profile_conv_fwd: unsupported num_dim_spatial value: " +
std::to_string(num_dim_spatial)); std::to_string(num_dim_spatial));
} }
return 1; return 0;
} }
profiler/src/profile_gemm.cpp
View file @ f9c478e2
...@@ -38,8 +38,8 @@ int profile_gemm(int argc, char* argv[]) ...@@ -38,8 +38,8 @@ int profile_gemm(int argc, char* argv[])
printf(" 3: A[k, m] * B[n, k] = C[m, n])\n"); printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
printf("arg4: verification (0: no; 1: yes)\n"); printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n"); printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n"); printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n"); printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideC\n"); printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideC\n");
printf("arg14: split k into mulitiple batch\n"); printf("arg14: split k into mulitiple batch\n");
exit(1); exit(1);
...@@ -50,7 +50,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -50,7 +50,7 @@ int profile_gemm(int argc, char* argv[])
const bool do_verification = std::stoi(argv[4]); const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]); const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]); const bool do_log = std::stoi(argv[6]);
const int nrepeat = std::stoi(argv[7]); const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]); const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]); const int N = std::stoi(argv[9]);
...@@ -68,13 +68,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -68,13 +68,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::half_t, ck::profiler::profile_gemm_impl<ck::half_t,
ck::half_t, ck::half_t,
ck::half_t, ck::half_t,
float,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -88,13 +89,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -88,13 +89,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::half_t, ck::profiler::profile_gemm_impl<ck::half_t,
ck::half_t, ck::half_t,
ck::half_t, ck::half_t,
float,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -108,13 +110,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -108,13 +110,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::half_t, ck::profiler::profile_gemm_impl<ck::half_t,
ck::half_t, ck::half_t,
ck::half_t, ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -128,13 +131,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -128,13 +131,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::half_t, ck::profiler::profile_gemm_impl<ck::half_t,
ck::half_t, ck::half_t,
ck::half_t, ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -146,6 +150,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -146,6 +150,7 @@ int profile_gemm(int argc, char* argv[])
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
ck::profiler::profile_gemm_impl<float, ck::profiler::profile_gemm_impl<float,
float,
float, float,
float, float,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
...@@ -154,7 +159,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -154,7 +159,7 @@ int profile_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -166,6 +171,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -166,6 +171,7 @@ int profile_gemm(int argc, char* argv[])
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_NK_MN) else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_NK_MN)
{ {
ck::profiler::profile_gemm_impl<float, ck::profiler::profile_gemm_impl<float,
float,
float, float,
float, float,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
...@@ -174,7 +180,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -174,7 +180,7 @@ int profile_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -186,6 +192,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -186,6 +192,7 @@ int profile_gemm(int argc, char* argv[])
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_KN_MN) else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_KN_MN)
{ {
ck::profiler::profile_gemm_impl<float, ck::profiler::profile_gemm_impl<float,
float,
float, float,
float, float,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
...@@ -194,7 +201,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -194,7 +201,7 @@ int profile_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -206,6 +213,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -206,6 +213,7 @@ int profile_gemm(int argc, char* argv[])
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_NK_MN) else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_NK_MN)
{ {
ck::profiler::profile_gemm_impl<float, ck::profiler::profile_gemm_impl<float,
float,
float, float,
float, float,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
...@@ -214,7 +222,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -214,7 +222,7 @@ int profile_gemm(int argc, char* argv[])
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -228,13 +236,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -228,13 +236,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<int8_t, ck::profiler::profile_gemm_impl<int8_t,
int8_t, int8_t,
int8_t, int8_t,
int32_t,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -248,13 +257,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -248,13 +257,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<int8_t, ck::profiler::profile_gemm_impl<int8_t,
int8_t, int8_t,
int8_t, int8_t,
int32_t,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -268,13 +278,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -268,13 +278,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<int8_t, ck::profiler::profile_gemm_impl<int8_t,
int8_t, int8_t,
int8_t, int8_t,
int32_t,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -288,13 +299,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -288,13 +299,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<int8_t, ck::profiler::profile_gemm_impl<int8_t,
int8_t, int8_t,
int8_t, int8_t,
int32_t,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -308,13 +320,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -308,13 +320,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::bhalf_t, ck::profiler::profile_gemm_impl<ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
float,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -328,13 +341,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -328,13 +341,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::bhalf_t, ck::profiler::profile_gemm_impl<ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
float,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -348,13 +362,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -348,13 +362,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::bhalf_t, ck::profiler::profile_gemm_impl<ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
float,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -368,13 +383,14 @@ int profile_gemm(int argc, char* argv[]) ...@@ -368,13 +383,14 @@ int profile_gemm(int argc, char* argv[])
ck::profiler::profile_gemm_impl<ck::bhalf_t, ck::profiler::profile_gemm_impl<ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
ck::bhalf_t, ck::bhalf_t,
float,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor>( ck::tensor_layout::gemm::RowMajor>(
do_verification, do_verification,
init_method, init_method,
do_log, do_log,
nrepeat, time_kernel,
M, M,
N, N,
K, K,
...@@ -388,5 +404,5 @@ int profile_gemm(int argc, char* argv[]) ...@@ -388,5 +404,5 @@ int profile_gemm(int argc, char* argv[])
throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented"); throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
} }
return 1; return 0;
} }
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