Merge pull request #29 from guolinke/master

add lru pool to reduce memory usage when #feature is large(#4)

include/LightGBM/config.h

@@ -140,6 +140,8 @@ public:
   int num_leaves = 127;
   int feature_fraction_seed = 2;
   double feature_fraction = 1.0;
+  // max cache size(unit:MB) for historical histogram. < 0 means not limit
+  double histogram_pool_size = -1;
   void Set(const std::unordered_map<std::string, std::string>& params) override;
 };
 

include/LightGBM/utils/log.h

@@ -5,7 +5,6 @@
 #include <cstdlib>
 #include <cstdarg>
 #include <cstring>
-#include <fstream>
 
 namespace LightGBM {
 

include/LightGBM/utils/lru_pool.h

+#ifndef LIGHTGBM_UTILS_LRU_POOL_H_
+#define LIGHTGBM_UTILS_LRU_POOL_H_
+
+#include <LightGBM/utils/array_args.h>
+#include <LightGBM/utils/log.h>
+
+#include <cstring>
+
+namespace LightGBM {
+
+/*!
+* \brief A LRU cached object pool, used for store historical histograms
+*/
+template<typename T>
+class LRUPool {
+public:
+
+  /*!
+  * \brief Constructor
+  */
+  LRUPool() {
+  }
+
+  /*!
+  * \brief Destructor
+  */
+  ~LRUPool() {
+    FreeAll();
+  }
+  /*!
+  * \brief Reset pool size
+  * \param cache_size Max cache size
+  * \param total_size Total size will be used
+  */
+  void ResetSize(int cache_size, int total_size) {
+    // free old memory
+    FreeAll();
+    cache_size_ = cache_size;
+    // at least need 2 bucket to store smaller leaf and larger leaf
+    CHECK(cache_size_ >= 2);
+
+    total_size_ = total_size;
+
+    pool_ = new T[cache_size];
+    mapper_ = new int[total_size_];
+    inverse_mapper_ = new int[cache_size_];
+    last_used_time_ = new int[cache_size_];
+    ResetMap();
+  }
+
+  /*!
+  * \brief Return true if this pool is enough to store all data
+  */
+  bool IsEnough() {
+    return cache_size_ == total_size_;
+  }
+
+  /*!
+  * \brief Reset mapper
+  */
+  void ResetMap() {
+    cur_time_ = 0;
+    memset(mapper_, -1, sizeof(int)*total_size_);
+    memset(inverse_mapper_, -1, sizeof(int)*cache_size_);
+    memset(last_used_time_, 0, sizeof(int)*cache_size_);
+  }
+
+  /*!
+  * \brief Set data for the pool for specific index
+  * \param idx which index want to set to
+  * \param data
+  */
+  void Set(int idx, const T& data) {
+    pool_[idx] = data;
+  }
+
+  /*!
+  * \brief Get data for the specific index
+  * \param idx which index want to get 
+  * \param out output data will store into this
+  * \return True if this index is in the pool, False if this index is not in the pool
+  */
+  bool Get(int idx, T* out) {
+    if (mapper_[idx] >= 0) {
+      int slot = mapper_[idx];
+      *out = pool_[slot];
+      last_used_time_[slot] = ++cur_time_;
+      return true;
+    } else {
+      // choose the least used slot 
+      int slot = static_cast<int>(ArrayArgs<int>::ArgMin(last_used_time_, cache_size_));
+      *out = pool_[slot];
+      last_used_time_[slot] = ++cur_time_;
+
+      // reset previous mapper
+      if (inverse_mapper_[slot] >= 0) mapper_[inverse_mapper_[slot]] = -1;
+
+      // update current mapper
+      mapper_[idx] = slot;
+      inverse_mapper_[slot] = idx;
+      return false;
+    }
+  }
+
+  /*!
+  * \brief Move data from one index to another index
+  * \param src_idx 
+  * \param dst_idx 
+  */
+  void Move(int src_idx, int dst_idx) {
+    if (mapper_[src_idx] < 0) {
+      return;
+    }
+    // get slot of src idx
+    int slot = mapper_[src_idx];
+    // reset src_idx
+    mapper_[src_idx] = -1;
+
+    // move to dst idx
+    mapper_[dst_idx] = slot;
+    last_used_time_[slot] = ++cur_time_;
+    inverse_mapper_[slot] = dst_idx;
+  }
+private:
+  void FreeAll(){
+    if (pool_ != nullptr) {
+      delete[] pool_;
+    }
+    if (mapper_ != nullptr) {
+      delete[] mapper_;
+    }
+    if (inverse_mapper_ != nullptr) {
+      delete[] inverse_mapper_;
+    }
+    if (last_used_time_ != nullptr) {
+      delete[] last_used_time_;
+    }
+  }
+  T* pool_ = nullptr;
+  int cache_size_;
+  int total_size_;
+  int* mapper_ = nullptr;
+  int* inverse_mapper_ = nullptr;
+  int* last_used_time_ = nullptr;
+  int cur_time_ = 0;
+};
+
+}
+
+#endif  // LIGHTGBM_UTILS_LRU_POOL_H_

src/io/config.cpp

@@ -110,28 +110,31 @@ void OverallConfig::GetTaskType(const std::unordered_map<std::string, std::strin
 }
 
 void OverallConfig::CheckParamConflict() {
+  GBDTConfig* gbdt_config = dynamic_cast<GBDTConfig*>(boosting_config);
   if (network_config.num_machines > 1) {
     is_parallel = true;
   } else {
     is_parallel = false;
-    dynamic_cast<GBDTConfig*>(boosting_config)->tree_learner_type =
-                                                TreeLearnerType::kSerialTreeLearner;
+    gbdt_config->tree_learner_type = TreeLearnerType::kSerialTreeLearner;
   }
 
-  if (dynamic_cast<GBDTConfig*>(boosting_config)->tree_learner_type ==
-                                                TreeLearnerType::kSerialTreeLearner) {
+  if (gbdt_config->tree_learner_type == TreeLearnerType::kSerialTreeLearner) {
     is_parallel = false;
     network_config.num_machines = 1;
   }
 
-  if (dynamic_cast<GBDTConfig*>(boosting_config)->tree_learner_type ==
-                                                 TreeLearnerType::kSerialTreeLearner ||
-    dynamic_cast<GBDTConfig*>(boosting_config)->tree_learner_type ==
-                                                 TreeLearnerType::kFeatureParallelTreelearner) {
+  if (gbdt_config->tree_learner_type == TreeLearnerType::kSerialTreeLearner ||
+    gbdt_config->tree_learner_type == TreeLearnerType::kFeatureParallelTreelearner) {
     is_parallel_find_bin = false;
-  } else if (dynamic_cast<GBDTConfig*>(boosting_config)->tree_learner_type ==
-                                                 TreeLearnerType::kDataParallelTreeLearner) {
+  } else if (gbdt_config->tree_learner_type == TreeLearnerType::kDataParallelTreeLearner) {
     is_parallel_find_bin = true;
+    if (gbdt_config->tree_config.histogram_pool_size >= 0) {
+      Log::Error("Histogram LRU queue was enabled (histogram_pool_size=%f). Will disable this for reducing communication cost."
+                 , gbdt_config->tree_config.histogram_pool_size);
+      // Change pool size to -1(not limit) when using data parallel for reducing communication cost
+      gbdt_config->tree_config.histogram_pool_size = -1;
+    }
+
   }
 }
 
@@ -218,10 +221,11 @@ void TreeConfig::Set(const std::unordered_map<std::string, std::string>& params)
   GetDouble(params, "min_sum_hessian_in_leaf", &min_sum_hessian_in_leaf);
   CHECK(min_sum_hessian_in_leaf > 1.0f || min_data_in_leaf > 0);
   GetInt(params, "num_leaves", &num_leaves);
-  CHECK(num_leaves > 0);
+  CHECK(num_leaves > 1);
   GetInt(params, "feature_fraction_seed", &feature_fraction_seed);
   GetDouble(params, "feature_fraction", &feature_fraction);
   CHECK(feature_fraction > 0.0 && feature_fraction <= 1.0);
+  GetDouble(params, "histogram_pool_size", &histogram_pool_size);
 }
 
 

src/treelearner/data_parallel_tree_learner.cpp

@@ -143,8 +143,8 @@ void DataParallelTreeLearner::FindBestThresholds() {
                                                              smaller_leaf_splits_->num_data_in_leaf(),
                                                              smaller_leaf_splits_->sum_gradients(),
                                                              smaller_leaf_splits_->sum_hessians(),
-                                                             ptr_to_ordered_gradients_,
-                                                             ptr_to_ordered_hessians_);
+                                                             ptr_to_ordered_gradients_smaller_leaf_,
+                                                             ptr_to_ordered_hessians_smaller_leaf_);
     } else {
       smaller_leaf_histogram_array_[feature_index].Construct(ordered_bins_[feature_index],
                                                              smaller_leaf_splits_->LeafIndex(),

src/treelearner/serial_tree_learner.cpp

@@ -8,8 +8,7 @@
 namespace LightGBM {
 
 SerialTreeLearner::SerialTreeLearner(const TreeConfig& tree_config)
-  :data_partition_(nullptr), is_feature_used_(nullptr),
-  historical_histogram_array_(nullptr), smaller_leaf_histogram_array_(nullptr),
+  :data_partition_(nullptr), is_feature_used_(nullptr), smaller_leaf_histogram_array_(nullptr),
   larger_leaf_histogram_array_(nullptr),
   smaller_leaf_splits_(nullptr), larger_leaf_splits_(nullptr),
   ordered_gradients_(nullptr), ordered_hessians_(nullptr), is_data_in_leaf_(nullptr) {
@@ -19,6 +18,7 @@ SerialTreeLearner::SerialTreeLearner(const TreeConfig& tree_config)
   min_sum_hessian_one_leaf_ = static_cast<float>(tree_config.min_sum_hessian_in_leaf);
   feature_fraction_ = tree_config.feature_fraction;
   random_ = Random(tree_config.feature_fraction_seed);
+  histogram_pool_size_ = tree_config.histogram_pool_size;
 }
 
 SerialTreeLearner::~SerialTreeLearner() {
@@ -26,11 +26,11 @@ SerialTreeLearner::~SerialTreeLearner() {
   if (smaller_leaf_splits_ != nullptr) { delete smaller_leaf_splits_; }
   if (larger_leaf_splits_ != nullptr) { delete larger_leaf_splits_; }
   for (int i = 0; i < num_leaves_; ++i) {
-    if (historical_histogram_array_[i] != nullptr) {
-      delete[] historical_histogram_array_[i];
+    FeatureHistogram* ptr = nullptr;
+    if (histogram_pool_.Get(i, &ptr)) {
+      delete[] ptr;
     }
   }
-  if (historical_histogram_array_ != nullptr) { delete[] historical_histogram_array_; }
   if (is_feature_used_ != nullptr) { delete[] is_feature_used_; }
   if (ordered_gradients_ != nullptr) { delete[] ordered_gradients_; }
   if (ordered_hessians_ != nullptr) { delete[] ordered_hessians_; }
@@ -46,16 +46,31 @@ void SerialTreeLearner::Init(const Dataset* train_data) {
   train_data_ = train_data;
   num_data_ = train_data_->num_data();
   num_features_ = train_data_->num_features();
+  int max_cache_size = 0;
+  // Get the max size of pool
+  if (histogram_pool_size_ < 0) {
+    max_cache_size = num_leaves_;
+  } else {
+    size_t total_histogram_size = 0;
+    for (int i = 0; i < train_data_->num_features(); ++i) {
+      total_histogram_size += sizeof(HistogramBinEntry) * train_data_->FeatureAt(i)->num_bin();
+    }
+    max_cache_size = static_cast<int>(histogram_pool_size_ * 1024 * 1024 / total_histogram_size);
+  }
+  // at least need 2 leaves
+  max_cache_size = Common::Max(2, max_cache_size);
+  max_cache_size = Common::Min(max_cache_size, num_leaves_);
+  histogram_pool_.ResetSize(max_cache_size, num_leaves_);
 
-  // allocate the space for historical_histogram_array_
-  historical_histogram_array_ = new FeatureHistogram*[num_leaves_];
-  for (int i = 0; i < num_leaves_; ++i) {
-    historical_histogram_array_[i] = new FeatureHistogram[train_data_->num_features()];
+  for (int i = 0; i < max_cache_size; ++i) {
+    FeatureHistogram* tmp_histogram_array = new FeatureHistogram[train_data_->num_features()];
     for (int j = 0; j < train_data_->num_features(); ++j) {
-      historical_histogram_array_[i][j].Init(train_data_->FeatureAt(j),
+      tmp_histogram_array[j].Init(train_data_->FeatureAt(j),
                                              j, min_num_data_one_leaf_,
                                              min_sum_hessian_one_leaf_);
     }
+    // set data at i-th position
+    histogram_pool_.Set(i, tmp_histogram_array);
   }
   // push split information for all leaves
   for (int i = 0; i < num_leaves_; ++i) {
@@ -136,6 +151,8 @@ Tree* SerialTreeLearner::Train(const score_t* gradients, const score_t *hessians
 }
 
 void SerialTreeLearner::BeforeTrain() {
+  // reset histogram pool
+  histogram_pool_.ResetMap();
   // initialize used features
   for (int i = 0; i < num_features_; ++i) {
     is_feature_used_[i] = false;
@@ -146,13 +163,7 @@ void SerialTreeLearner::BeforeTrain() {
   for (auto idx : used_feature_indices) {
     is_feature_used_[idx] = true;
   }
-  // set all histogram to splittable
-  #pragma omp parallel for schedule(static)
-  for (int i = 0; i < num_leaves_; ++i) {
-    for (int j = 0; j < train_data_->num_features(); ++j) {
-      historical_histogram_array_[i][j].set_is_splittable(true);
-    }
-  }
+
   // initialize data partition
   data_partition_->Init();
 
@@ -166,8 +177,8 @@ void SerialTreeLearner::BeforeTrain() {
     // use all data
     smaller_leaf_splits_->Init(gradients_, hessians_);
     // point to gradients, avoid copy
-    ptr_to_ordered_gradients_ = gradients_;
-    ptr_to_ordered_hessians_ = hessians_;
+    ptr_to_ordered_gradients_smaller_leaf_ = gradients_;
+    ptr_to_ordered_hessians_smaller_leaf_  = hessians_;
   } else {
     // use bagging, only use part of data
     smaller_leaf_splits_->Init(0, data_partition_, gradients_, hessians_);
@@ -180,10 +191,13 @@ void SerialTreeLearner::BeforeTrain() {
       ordered_hessians_[i] = hessians_[indices[i]];
     }
     // point to ordered_gradients_ and ordered_hessians_
-    ptr_to_ordered_gradients_ = ordered_gradients_;
-    ptr_to_ordered_hessians_ = ordered_hessians_;
+    ptr_to_ordered_gradients_smaller_leaf_ = ordered_gradients_;
+    ptr_to_ordered_hessians_smaller_leaf_ = ordered_hessians_;
   }
 
+  ptr_to_ordered_gradients_larger_leaf_ = nullptr;
+  ptr_to_ordered_hessians_larger_leaf_ = nullptr;
+
   larger_leaf_splits_->Init();
 
   // if has ordered bin, need to initialize the ordered bin
@@ -231,26 +245,28 @@ bool SerialTreeLearner::BeforeFindBestSplit(int left_leaf, int right_leaf) {
     }
     return false;
   }
+  parent_leaf_histogram_array_ = nullptr;
   // -1 if only has one leaf. else equal the index of smaller leaf
   int smaller_leaf = -1;
+  int larger_leaf = -1;
   // only have root
   if (right_leaf < 0) {
-    smaller_leaf_histogram_array_ = historical_histogram_array_[left_leaf];
+    histogram_pool_.Get(left_leaf, &smaller_leaf_histogram_array_);
     larger_leaf_histogram_array_ = nullptr;
+    
   } else if (num_data_in_left_child < num_data_in_right_child) {
     smaller_leaf = left_leaf;
+    larger_leaf = right_leaf;
     // put parent(left) leaf's histograms into larger leaf's histgrams
-    larger_leaf_histogram_array_ = historical_histogram_array_[left_leaf];
-    smaller_leaf_histogram_array_ = historical_histogram_array_[right_leaf];
-
-    // We will construc histograms for smaller leaf, and smaller_leaf=left_leaf = parent.
-    // if we don't swap the cache, we will overwrite the parent's hisogram cache.
-    std::swap(historical_histogram_array_[left_leaf], historical_histogram_array_[right_leaf]);
+    if (histogram_pool_.Get(left_leaf, &larger_leaf_histogram_array_)) { parent_leaf_histogram_array_ = larger_leaf_histogram_array_; }
+    histogram_pool_.Move(left_leaf, right_leaf);
+    histogram_pool_.Get(left_leaf, &smaller_leaf_histogram_array_);
   } else {
     smaller_leaf = right_leaf;
+    larger_leaf = left_leaf;
     // put parent(left) leaf's histograms to larger leaf's histgrams
-    larger_leaf_histogram_array_ = historical_histogram_array_[left_leaf];
-    smaller_leaf_histogram_array_ = historical_histogram_array_[right_leaf];
+    if (histogram_pool_.Get(left_leaf, &larger_leaf_histogram_array_)) { parent_leaf_histogram_array_ = larger_leaf_histogram_array_; }
+    histogram_pool_.Get(right_leaf, &smaller_leaf_histogram_array_);
   }
 
   // init for the ordered gradients, only initialize when have 2 leaves
@@ -268,8 +284,23 @@ bool SerialTreeLearner::BeforeFindBestSplit(int left_leaf, int right_leaf) {
       ordered_hessians_[i - begin] = hessians_[indices[i]];
     }
     // assign pointer
-    ptr_to_ordered_gradients_ = ordered_gradients_;
-    ptr_to_ordered_hessians_ = ordered_hessians_;
+    ptr_to_ordered_gradients_smaller_leaf_ = ordered_gradients_;
+    ptr_to_ordered_hessians_smaller_leaf_ = ordered_hessians_;
+
+    if (parent_leaf_histogram_array_ == nullptr) {
+      // need order gradient for larger leaf
+      data_size_t smaller_size = end - begin;
+      data_size_t larger_begin = data_partition_->leaf_begin(larger_leaf);
+      data_size_t larger_end = larger_begin + data_partition_->leaf_count(larger_leaf);
+      // copy
+      #pragma omp parallel for schedule(static)
+      for (data_size_t i = larger_begin; i < larger_end; ++i) {
+        ordered_gradients_[smaller_size + i - larger_begin] = gradients_[indices[i]];
+        ordered_hessians_[smaller_size + i - larger_begin] = hessians_[indices[i]];
+      }
+      ptr_to_ordered_gradients_larger_leaf_ = ordered_gradients_ + smaller_size;
+      ptr_to_ordered_hessians_larger_leaf_ = ordered_hessians_ + smaller_size;
+    }
   }
 
   // split for the ordered bin
@@ -301,7 +332,7 @@ void SerialTreeLearner::FindBestThresholds() {
     // feature is not used
     if ((is_feature_used_ != nullptr && is_feature_used_[feature_index] == false)) continue;
     // if parent(larger) leaf cannot split at current feature
-    if (larger_leaf_histogram_array_ != nullptr && !larger_leaf_histogram_array_[feature_index].is_splittable()) {
+    if (parent_leaf_histogram_array_ != nullptr && !parent_leaf_histogram_array_[feature_index].is_splittable()) {
       smaller_leaf_histogram_array_[feature_index].set_is_splittable(false);
       continue;
     }
@@ -313,8 +344,8 @@ void SerialTreeLearner::FindBestThresholds() {
         smaller_leaf_splits_->num_data_in_leaf(),
         smaller_leaf_splits_->sum_gradients(),
         smaller_leaf_splits_->sum_hessians(),
-        ptr_to_ordered_gradients_,
-        ptr_to_ordered_hessians_);
+        ptr_to_ordered_gradients_smaller_leaf_,
+        ptr_to_ordered_hessians_smaller_leaf_);
     } else {
       // used ordered bin
       smaller_leaf_histogram_array_[feature_index].Construct(ordered_bins_[feature_index],
@@ -331,9 +362,30 @@ void SerialTreeLearner::FindBestThresholds() {
     // only has root leaf
     if (larger_leaf_splits_ == nullptr || larger_leaf_splits_->LeafIndex() < 0) continue;
 
+    if (parent_leaf_histogram_array_ != nullptr) {
       // construct histgroms for large leaf, we initialize larger leaf as the parent,
       // so we can just subtract the smaller leaf's histograms
       larger_leaf_histogram_array_[feature_index].Subtract(smaller_leaf_histogram_array_[feature_index]);
+    } else {
+      if (ordered_bins_[feature_index] == nullptr) {
+        // if not use ordered bin
+        larger_leaf_histogram_array_[feature_index].Construct(larger_leaf_splits_->data_indices(),
+          larger_leaf_splits_->num_data_in_leaf(),
+          larger_leaf_splits_->sum_gradients(),
+          larger_leaf_splits_->sum_hessians(),
+          ptr_to_ordered_gradients_larger_leaf_,
+          ptr_to_ordered_hessians_larger_leaf_);
+      } else {
+        // used ordered bin
+        larger_leaf_histogram_array_[feature_index].Construct(ordered_bins_[feature_index],
+          larger_leaf_splits_->LeafIndex(),
+          larger_leaf_splits_->num_data_in_leaf(),
+          larger_leaf_splits_->sum_gradients(),
+          larger_leaf_splits_->sum_hessians(),
+          gradients_,
+          hessians_);
+      }
+    }
 
     // find best threshold for larger child
     larger_leaf_histogram_array_[feature_index].FindBestThreshold(&larger_leaf_splits_->BestSplitPerFeature()[feature_index]);

src/treelearner/serial_tree_learner.h

@@ -3,6 +3,7 @@
 
 #include <LightGBM/utils/random.h>
 #include <LightGBM/utils/array_args.h>
+#include <LightGBM/utils/lru_pool.h>
 
 #include <LightGBM/tree_learner.h>
 #include <LightGBM/dataset.h>
@@ -122,8 +123,8 @@ protected:
   Random random_;
   /*! \brief used for sub feature training, is_feature_used_[i] = falase means don't used feature i */
   bool* is_feature_used_;
-  /*! \brief cache historical histogram to speed up */
-  FeatureHistogram** historical_histogram_array_;
+  /*! \brief pointer to histograms array of parent of current leaves */
+  FeatureHistogram* parent_leaf_histogram_array_;
   /*! \brief pointer to histograms array of smaller leaf */
   FeatureHistogram* smaller_leaf_histogram_array_;
   /*! \brief pointer to histograms array of larger leaf */
@@ -143,15 +144,25 @@ protected:
   score_t* ordered_hessians_;
 
   /*! \brief Pointer to ordered_gradients_, use this to avoid copy at BeforeTrain */
-  const score_t* ptr_to_ordered_gradients_;
+  const score_t* ptr_to_ordered_gradients_smaller_leaf_;
   /*! \brief Pointer to ordered_hessians_, use this to avoid copy at BeforeTrain*/
-  const score_t* ptr_to_ordered_hessians_;
+  const score_t* ptr_to_ordered_hessians_smaller_leaf_;
+
+  /*! \brief Pointer to ordered_gradients_, use this to avoid copy at BeforeTrain */
+  const score_t* ptr_to_ordered_gradients_larger_leaf_;
+  /*! \brief Pointer to ordered_hessians_, use this to avoid copy at BeforeTrain*/
+  const score_t* ptr_to_ordered_hessians_larger_leaf_;
+
   /*! \brief Store ordered bin */
   std::vector<OrderedBin*> ordered_bins_;
   /*! \brief True if has ordered bin */
   bool has_ordered_bin_ = false;
   /*! \brief  is_data_in_leaf_[i] != 0 means i-th data is marked */
   char* is_data_in_leaf_;
+  /*! \brief  max cache size(unit:GB) for historical histogram. < 0 means not limit */
+  double histogram_pool_size_;
+  /*! \brief used to cache historical histogram to speed up*/
+  LRUPool<FeatureHistogram*> histogram_pool_;
 };
 
 

windows/LightGBM.vcxproj

@@ -170,6 +170,7 @@
     <ClInclude Include="..\include\LightGBM\utils\array_args.h" />
     <ClInclude Include="..\include\LightGBM\utils\common.h" />
     <ClInclude Include="..\include\LightGBM\utils\log.h" />
+    <ClInclude Include="..\include\LightGBM\utils\lru_pool.h" />
     <ClInclude Include="..\include\LightGBM\utils\pipeline_reader.h" />
     <ClInclude Include="..\include\LightGBM\utils\random.h" />
     <ClInclude Include="..\include\LightGBM\utils\text_reader.h" />

windows/LightGBM.vcxproj.filters

@@ -156,6 +156,9 @@
     <ClInclude Include="..\include\LightGBM\utils\threading.h">
       <Filter>include\LightGBM\utils</Filter>
     </ClInclude>
+    <ClInclude Include="..\include\LightGBM\utils\lru_pool.h">
+      <Filter>include\LightGBM\utils</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <ClCompile Include="..\src\application\application.cpp">