1) Fixed Predictor lifecycle

2) Fixed Boosting trees initialization

#5482

src/boosting/gbdt.h

@@ -433,11 +433,18 @@ class GBDT : public GBDTBase {
       num_iteration_for_pred_ = num_iteration_for_pred_ - start_iteration;
     }
     start_iteration_for_pred_ = start_iteration;
-    if (is_pred_contrib) {
+
+    if (is_pred_contrib && !models_initialized_) {
+      std::lock_guard<std::mutex> lock(instance_mutex_);
+      if (models_initialized_)
+        return;
+
       #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
       for (int i = 0; i < static_cast<int>(models_.size()); ++i) {
         models_[i]->RecomputeMaxDepth();
       }
+
+      models_initialized_ = true;
     }
   }
 
@@ -548,6 +555,10 @@ class GBDT : public GBDTBase {
   int max_feature_idx_;
   /*! \brief Parser config file content */
   std::string parser_config_str_ = "";
+  /*! \brief Are the models initialized (passed RecomputeMaxDepth phase) */
+  bool models_initialized_ = false;
+  /*! \brief Mutex for exclusive models initialization */
+  std::mutex instance_mutex_;
 
 #ifdef USE_CUDA
   /*! \brief First order derivative of training data */

src/c_api.cpp

@@ -460,7 +460,7 @@ class Booster {
     *out_len = single_row_predictor->num_pred_in_one_row;
   }
 
-  Predictor CreatePredictor(int start_iteration, int num_iteration, int predict_type, int ncol, const Config& config) const {
+  std::shared_ptr<Predictor> CreatePredictor(int start_iteration, int num_iteration, int predict_type, int ncol, const Config& config) const {
     if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) {
       Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n" \
                  "You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", ncol, boosting_->MaxFeatureIdx() + 1);
@@ -478,7 +478,7 @@ class Booster {
       is_raw_score = false;
     }
 
-    return Predictor(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib,
+    return std::make_shared<Predictor>(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib,
                         config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin);
   }
 
@@ -496,7 +496,7 @@ class Booster {
       predict_contrib = true;
     }
     int64_t num_pred_in_one_row = boosting_->NumPredictOneRow(start_iteration, num_iteration, is_predict_leaf, predict_contrib);
-    auto pred_fun = predictor.GetPredictFunction();
+    auto pred_fun = predictor->GetPredictFunction();
     OMP_INIT_EX();
     #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
     for (int i = 0; i < nrow; ++i) {
@@ -517,7 +517,7 @@ class Booster {
                      int32_t** out_indices, void** out_data, int data_type,
                      bool* is_data_float32_ptr, int num_matrices) const {
     auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);
-    auto pred_sparse_fun = predictor.GetPredictSparseFunction();
+    auto pred_sparse_fun = predictor->GetPredictSparseFunction();
     std::vector<std::vector<std::unordered_map<int, double>>>& agg = *agg_ptr;
     OMP_INIT_EX();
     #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
@@ -652,7 +652,7 @@ class Booster {
     // Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices)
     int num_matrices = boosting_->NumModelPerIteration();
     auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);
-    auto pred_sparse_fun = predictor.GetPredictSparseFunction();
+    auto pred_sparse_fun = predictor->GetPredictSparseFunction();
     bool is_col_ptr_int32 = false;
     bool is_data_float32 = false;
     int num_output_cols = ncol + 1;