fix refactoring bug in voting parallel (#3089)

src/treelearner/feature_histogram.hpp

@@ -1147,32 +1147,15 @@ class HistogramPool {
     }
   }
 
-  void DynamicChangeSize(const Dataset* train_data, bool is_hist_colwise,
-                         const Config* config, int cache_size, int total_size) {
-    if (feature_metas_.empty()) {
-      SetFeatureInfo<true, true>(train_data, config, &feature_metas_);
-      uint64_t bin_cnt_over_features = 0;
-      for (int i = 0; i < train_data->num_features(); ++i) {
-        bin_cnt_over_features +=
-            static_cast<uint64_t>(feature_metas_[i].num_bin);
-      }
-      Log::Info("Total Bins %d", bin_cnt_over_features);
-    }
-    int old_cache_size = static_cast<int>(pool_.size());
-    Reset(cache_size, total_size);
-
-    if (cache_size > old_cache_size) {
-      pool_.resize(cache_size);
-      data_.resize(cache_size);
-    }
+  static int GetNumTotalHistogramBins(const Dataset* train_data,
+    bool is_hist_colwise, std::vector<int>* offsets) {
     int num_total_bin = static_cast<int>(train_data->NumTotalBin());
-
-    std::vector<int> offsets;
+    offsets->clear();
     if (is_hist_colwise) {
       int offset = 0;
       for (int j = 0; j < train_data->num_features(); ++j) {
         offset += train_data->SubFeatureBinOffset(j);
-        offsets.push_back(offset);
+        offsets->push_back(offset);
         auto num_bin = train_data->FeatureNumBin(j);
         if (train_data->FeatureBinMapper(j)->GetMostFreqBin() == 0) {
           num_bin -= 1;
@@ -1182,13 +1165,37 @@ class HistogramPool {
     } else {
       num_total_bin = 1;
       for (int j = 0; j < train_data->num_features(); ++j) {
-        offsets.push_back(num_total_bin);
+        offsets->push_back(num_total_bin);
         num_total_bin += train_data->FeatureBinMapper(j)->num_bin();
         if (train_data->FeatureBinMapper(j)->GetMostFreqBin() == 0) {
           num_total_bin -= 1;
         }
       }
     }
+    return num_total_bin;
+  }
+
+  void DynamicChangeSize(const Dataset* train_data, bool is_hist_colwise,
+                         const Config* config, int cache_size, int total_size) {
+    if (feature_metas_.empty()) {
+      SetFeatureInfo<true, true>(train_data, config, &feature_metas_);
+      uint64_t bin_cnt_over_features = 0;
+      for (int i = 0; i < train_data->num_features(); ++i) {
+        bin_cnt_over_features +=
+            static_cast<uint64_t>(feature_metas_[i].num_bin);
+      }
+      Log::Info("Total Bins %d", bin_cnt_over_features);
+    }
+    int old_cache_size = static_cast<int>(pool_.size());
+    Reset(cache_size, total_size);
+
+    if (cache_size > old_cache_size) {
+      pool_.resize(cache_size);
+      data_.resize(cache_size);
+    }
+    std::vector<int> offsets;
+    int num_total_bin =
+        this->GetNumTotalHistogramBins(train_data, is_hist_colwise, &offsets);
     OMP_INIT_EX();
 #pragma omp parallel for schedule(static)
     for (int i = old_cache_size; i < cache_size; ++i) {

src/treelearner/voting_parallel_tree_learner.cpp

@@ -63,20 +63,15 @@ void VotingParallelTreeLearner<TREELEARNER_T>::Init(const Dataset* train_data, b
   // initialize histograms for global
   smaller_leaf_histogram_array_global_.reset(new FeatureHistogram[this->num_features_]);
   larger_leaf_histogram_array_global_.reset(new FeatureHistogram[this->num_features_]);
-  auto num_total_bin = train_data->NumTotalBin();
-  smaller_leaf_histogram_data_.resize(num_total_bin);
-  larger_leaf_histogram_data_.resize(num_total_bin);
+  std::vector<int> offsets;
+  int num_total_bin = HistogramPool::GetNumTotalHistogramBins(
+      train_data, this->share_state_->is_colwise, &offsets);
+  smaller_leaf_histogram_data_.resize(num_total_bin * 2);
+  larger_leaf_histogram_data_.resize(num_total_bin * 2);
   HistogramPool::SetFeatureInfo<true, true>(train_data, this->config_, &feature_metas_);
-  uint64_t offset = 0;
   for (int j = 0; j < train_data->num_features(); ++j) {
-    offset += static_cast<uint64_t>(train_data->SubFeatureBinOffset(j));
-    smaller_leaf_histogram_array_global_[j].Init(smaller_leaf_histogram_data_.data() + offset, &feature_metas_[j]);
-    larger_leaf_histogram_array_global_[j].Init(larger_leaf_histogram_data_.data() + offset, &feature_metas_[j]);
-    auto num_bin = train_data->FeatureNumBin(j);
-    if (train_data->FeatureBinMapper(j)->GetMostFreqBin() == 0) {
-      num_bin -= 1;
-    }
-    offset += static_cast<uint64_t>(num_bin);
+    smaller_leaf_histogram_array_global_[j].Init(smaller_leaf_histogram_data_.data() + offsets[j] * 2, &feature_metas_[j]);
+    larger_leaf_histogram_array_global_[j].Init(larger_leaf_histogram_data_.data() + offsets[j] * 2, &feature_metas_[j]);
   }
 }