[tests/profiling] Profile Fast predict methods

Build with -DBUILD_PROFILING_TESTS=ON and copy the default
model trained on the Higgs dataset from the benchmarks repo

 https://github.com/guolinke/boosting_tree_benchmarks.git

to LightGBM repo root and run the lightgbm_profile_* binaries.

The single instance used is the first row from that dataset.

CMakeLists.txt

@@ -15,6 +15,7 @@ OPTION(USE_R35 "Set to ON if your R version is not earlier than 3.5" OFF)
 OPTION(USE_TIMETAG "Set to ON to output time costs" OFF)
 OPTION(USE_DEBUG "Set to ON for Debug mode" OFF)
 OPTION(BUILD_FOR_R "Set to ON if building lib_lightgbm for use with the R package" OFF)
+OPTION(BUILD_PROFILING_TESTS "Set to ON to compile profiling executables for development and benchmarks." OFF)
 
 if(APPLE)
     OPTION(APPLE_OUTPUT_DYLIB "Output dylib shared library" OFF)
@@ -234,6 +235,12 @@ file(GLOB SOURCES
     src/treelearner/*.cpp
 )
 
+if(BUILD_PROFILING_TESTS)
+    # For profiling builds (use -DDEBUG=ON)
+    add_executable(lightgbm_profile_single_row_predict profiling/profile_single_row_predict.cpp ${SOURCES} src/c_api.cpp)
+    add_executable(lightgbm_profile_single_row_predict_fast profiling/profile_single_row_predict_fast.cpp ${SOURCES} src/c_api.cpp)
+endif(BUILD_PROFILING_TESTS)
+
 add_executable(lightgbm src/main.cpp ${SOURCES})
 list(APPEND SOURCES "src/c_api.cpp")
 

profiling/profile_single_row_predict.cpp

@@ -0,0 +1,27 @@
+#include <iostream>
+#include "LightGBM/c_api.h"
+
+using namespace std;
+
+int main() {
+  cout << "start\n";
+
+  BoosterHandle boosterHandle;
+  int num_iterations;
+  LGBM_BoosterCreateFromModelfile("./LightGBM_model.txt", &num_iterations, &boosterHandle);
+  cout << "Model iterations " << num_iterations<< "\n";
+
+  double values[] = {1.000000000000000000e+00,8.692932128906250000e-01,-6.350818276405334473e-01,2.256902605295181274e-01,3.274700641632080078e-01,-6.899932026863098145e-01,7.542022466659545898e-01,-2.485731393098831177e-01,-1.092063903808593750e+00,0.000000000000000000e+00,1.374992132186889648e+00,-6.536741852760314941e-01,9.303491115570068359e-01,1.107436060905456543e+00,1.138904333114624023e+00,-1.578198313713073730e+00,-1.046985387802124023e+00,0.000000000000000000e+00,6.579295396804809570e-01,-1.045456994324922562e-02,-4.576716944575309753e-02,3.101961374282836914e+00,1.353760004043579102e+00,9.795631170272827148e-01,9.780761599540710449e-01,9.200048446655273438e-01,7.216574549674987793e-01,9.887509346008300781e-01,8.766783475875854492e-01}; // score = 0.487278
+
+  int64_t dummy;
+
+  double score[1];
+  for (size_t i = 0; i < 3e5; ++i) {
+    LGBM_BoosterPredictForMatSingleRow(boosterHandle, values, C_API_DTYPE_FLOAT64, 28, 1, C_API_PREDICT_NORMAL, num_iterations, "", &dummy, score);
+  }
+  cout << "len=" << dummy << endl;
+
+  cout << "Score = " << score[0] << "\n";
+
+  cout << "end\n";
+}

profiling/profile_single_row_predict_fast.cpp

@@ -0,0 +1,32 @@
+#include <iostream>
+#include "LightGBM/c_api.h"
+
+using namespace std;
+
+int main() {
+  cout << "start\n";
+
+  BoosterHandle boosterHandle;
+  int num_iterations;
+  LGBM_BoosterCreateFromModelfile("./LightGBM_model.txt", &num_iterations, &boosterHandle);
+  cout << "Model iterations " << num_iterations<< "\n";
+
+  double values[] = {1.000000000000000000e+00,8.692932128906250000e-01,-6.350818276405334473e-01,2.256902605295181274e-01,3.274700641632080078e-01,-6.899932026863098145e-01,7.542022466659545898e-01,-2.485731393098831177e-01,-1.092063903808593750e+00,0.000000000000000000e+00,1.374992132186889648e+00,-6.536741852760314941e-01,9.303491115570068359e-01,1.107436060905456543e+00,1.138904333114624023e+00,-1.578198313713073730e+00,-1.046985387802124023e+00,0.000000000000000000e+00,6.579295396804809570e-01,-1.045456994324922562e-02,-4.576716944575309753e-02,3.101961374282836914e+00,1.353760004043579102e+00,9.795631170272827148e-01,9.780761599540710449e-01,9.200048446655273438e-01,7.216574549674987793e-01,9.887509346008300781e-01,8.766783475875854492e-01}; // score = 0.487278
+
+  FastConfigHandle fastConfigHandle;
+  LGBM_BoosterPredictForMatSingleRowFastInit(boosterHandle, C_API_DTYPE_FLOAT64, 28, "", &fastConfigHandle);
+
+  int64_t dummy_out_len;
+  double score[1];
+  for (size_t i = 0; i < 3e5; ++i) {
+    LGBM_BoosterPredictForMatSingleRowFast(fastConfigHandle, values, C_API_PREDICT_NORMAL, num_iterations, &dummy_out_len, score);
+  }
+
+  LGBM_FastConfigFree(fastConfigHandle);
+
+  cout << "len=" << dummy_out_len << endl;
+
+  cout << "Score = " << score[0] << "\n";
+
+  cout << "end\n";
+}