revise and simplify template code

CMakeCache.txt

@@ -0,0 +1,38 @@
+# This is the CMakeCache file.
+# For build in directory: /media/ziyuexu/Research/Experiment/SecureFedXGBoost/XGBoost/xgboost_SecureBoostP2
+# It was generated by CMake: /usr/bin/cmake
+# You can edit this file to change values found and used by cmake.
+# If you do not want to change any of the values, simply exit the editor.
+# If you do want to change a value, simply edit, save, and exit the editor.
+# The syntax for the file is as follows:
+# KEY:TYPE=VALUE
+# KEY is the name of a variable in the cache.
+# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!.
+# VALUE is the current value for the KEY.
+
+########################
+# EXTERNAL cache entries
+########################
+
+
+########################
+# INTERNAL cache entries
+########################
+
+//This is the directory where this CMakeCache.txt was created
+CMAKE_CACHEFILE_DIR:INTERNAL=/media/ziyuexu/Research/Experiment/SecureFedXGBoost/XGBoost/xgboost_SecureBoostP2
+//Major version of cmake used to create the current loaded cache
+CMAKE_CACHE_MAJOR_VERSION:INTERNAL=3
+//Minor version of cmake used to create the current loaded cache
+CMAKE_CACHE_MINOR_VERSION:INTERNAL=22
+//Patch version of cmake used to create the current loaded cache
+CMAKE_CACHE_PATCH_VERSION:INTERNAL=1
+//Path to CMake executable.
+CMAKE_COMMAND:INTERNAL=/usr/bin/cmake
+//Path to cpack program executable.
+CMAKE_CPACK_COMMAND:INTERNAL=/usr/bin/cpack
+//Path to ctest program executable.
+CMAKE_CTEST_COMMAND:INTERNAL=/usr/bin/ctest
+//Path to CMake installation.
+CMAKE_ROOT:INTERNAL=/usr/share/cmake-3.22
+

CMakeFiles/clion-log.txt

@@ -0,0 +1,4 @@
+Cannot generate into /media/ziyuexu/Research/Experiment/SecureFedXGBoost/XGBoost/xgboost_SecureBoostP2
+It is already used for unknown project
+
+Please either delete it manually or select another generation directory

CMakeFiles/cmake.check_cache

@@ -0,0 +1 @@
+# This file is generated by cmake for dependency checking of the CMakeCache.txt file

src/collective/aggregator.h

@@ -69,7 +69,7 @@ void ApplyWithLabels(MetaInfo const& info, void* buffer, size_t size, Function&&
  * @param result The HostDeviceVector storing the results.
  * @param function The function used to calculate the results.
  */
-template <typename T, typename Function>
+template <bool is_gpair, typename T, typename Function>
 void ApplyWithLabels(MetaInfo const& info, HostDeviceVector<T>* result, Function&& function) {
   if (info.IsVerticalFederated()) {
     // We assume labels are only available on worker 0, so the calculation is done there and result
@@ -91,84 +91,46 @@ void ApplyWithLabels(MetaInfo const& info, HostDeviceVector<T>* result, Function
 
     std::size_t size{};
     if (collective::GetRank() == 0) {
-      size = result->Size();
+        size = result->Size();
     }
     collective::Broadcast(&size, sizeof(std::size_t), 0);
 
-    result->Resize(size);
-    collective::Broadcast(result->HostPointer(), size * sizeof(T), 0);
-  } else {
-    std::forward<Function>(function)();
-  }
-}
-
-// Same as above, but with encyption on the result
-template <typename T, typename Function>
-void ApplyWithLabelsEncrypted(MetaInfo const& info, HostDeviceVector<T>* result, Function&& function) {
-  if (info.IsVerticalFederated()) {
-    // We assume labels are only available on worker 0, so the calculation is done there and result
-    // broadcast to other workers.
-    std::string message;
-    if (collective::GetRank() == 0) {
-      try {
-        std::forward<Function>(function)();
-      } catch (dmlc::Error& e) {
-       message = e.what();
-      }
-    }
-
-    collective::Broadcast(&message, 0);
-    if (!message.empty()) {
-      LOG(FATAL) << &message[0];
-      return;
-    }
-
-    std::size_t size{};
-    if (collective::GetRank() == 0) {
-      size = result->Size();
-    }
-    collective::Broadcast(&size, sizeof(std::size_t), 0);
-
-    // save to vector and encrypt
-    if (collective::GetRank() == 0) {
-        // check the max and min value of the result vector
-        float max_g = std::numeric_limits<float>::min();
-        float min_g = std::numeric_limits<float>::max();
-        float max_h = std::numeric_limits<float>::min();
-        float min_h = std::numeric_limits<float>::max();
-        std::vector<double> result_vector_g, result_vector_h;
+    if (info.IsSecure() && is_gpair) {
+      // Under secure mode, gpairs will be processed to vector and encrypt
+      // information only available on rank 0
+      if (collective::GetRank() == 0) {
+        std::vector<double> vector_g, vector_h;
         for (int i = 0; i < size; i++) {
-            result_vector_g.push_back(result->HostVector()[i].GetGrad());
-            result_vector_h.push_back(result->HostVector()[i].GetHess());
-
-            if (result->HostVector()[i].GetGrad() > max_g) {
-                max_g = result->HostVector()[i].GetGrad();
-            }
-            if (result->HostVector()[i].GetGrad() < min_g) {
-                min_g = result->HostVector()[i].GetGrad();
-            }
-            if (result->HostVector()[i].GetHess() > max_h) {
-                max_h = result->HostVector()[i].GetHess();
-            }
-            if (result->HostVector()[i].GetHess() < min_h) {
-                min_h = result->HostVector()[i].GetHess();
-            }
+          auto gpair = result->HostVector()[i];
+          // cast from GradientPair to float pointer
+          auto gpair_ptr = reinterpret_cast<float*>(&gpair);
+          // save to vector
+          vector_g.push_back(gpair_ptr[0]);
+          vector_h.push_back(gpair_ptr[1]);
         }
-        // print 1 sample
-        //std::cout << " g[0]: " << result_vector_g[0] << " h[0]: " << result_vector_h[0] << std::endl;
-        // print max and min
-        //std::cout << "max_g: " << max_g << " min_g: " << min_g << " max_h: " << max_h << " min_h: " << min_h << std::endl;
+      // provide the vectors to the processor interface
+
+      }
+      // broadcast the encrypted data
+      result->Resize(size);
+      collective::Broadcast(result->HostPointer(), size * sizeof(T), 0);
+    } else {
+      // clear text mode
+      result->Resize(size);
+      collective::Broadcast(result->HostPointer(), size * sizeof(T), 0);
     }
 
-    result->Resize(size);
-    collective::Broadcast(result->HostPointer(), size * sizeof(T), 0);
 
+    /*
     // print 1 sample
-    std::cout << "Rank: " << collective::GetRank() << " after broadcast - g: " << result->HostVector()[0].GetGrad() << " h: " << result->HostVector()[0].GetHess() << std::endl;
-
-
+    if (is_gpair) {
+      std::cout << "Rank: " << collective::GetRank() << " after broadcast - g: "
+      << reinterpret_cast<float*>(&result->HostVector()[0])[0] << " h: "
+      << reinterpret_cast<float*>(&result->HostVector()[0])[1] << std::endl;
+    }
+    */
   } else {
-      std::forward<Function>(function)();
+    std::forward<Function>(function)();
   }
 }
 

src/learner.cc

@@ -846,7 +846,7 @@ class LearnerConfiguration : public Learner {
 
   void InitEstimation(MetaInfo const& info, linalg::Tensor<float, 1>* base_score) {
     base_score->Reshape(1);
-    collective::ApplyWithLabels(info, base_score->Data(),
+    collective::ApplyWithLabels<false>(info, base_score->Data(),
                                 [&] { UsePtr(obj_)->InitEstimation(info, base_score); });
   }
 };
@@ -1472,15 +1472,9 @@ class LearnerImpl : public LearnerIO {
   void GetGradient(HostDeviceVector<bst_float> const& preds, MetaInfo const& info,
                    std::int32_t iter, linalg::Matrix<GradientPair>* out_gpair) {
     out_gpair->Reshape(info.num_row_, this->learner_model_param_.OutputLength());
-    // calculate gradient and communicate with or without encryption
-    if (info.IsSecure()) {
-      collective::ApplyWithLabelsEncrypted(info, out_gpair->Data(),
+    // calculate gradient and communicate
+    collective::ApplyWithLabels<true>(info, out_gpair->Data(),
                                   [&] { obj_->GetGradient(preds, info, iter, out_gpair); });
-      } else {
-      collective::ApplyWithLabels(info, out_gpair->Data(),
-                                  [&] { obj_->GetGradient(preds, info, iter, out_gpair); });
-    }
-
   }
 
   /*! \brief random number transformation seed. */