Support deterministic (#3494)

* implement

* fix compilation

* Update config.cpp

* unify wordings

Co-authored-by: StrikerRUS <nekit94-12@hotmail.com>

docs/Parameters.rst

@@ -193,6 +193,18 @@ Core Parameters
 
    -  this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly
 
+-  ``deterministic`` :raw-html:`<a id="deterministic" title="Permalink to this parameter" href="#deterministic">&#x1F517;&#xFE0E;</a>`, default = ``false``, type = bool
+
+   -  used only with ``cpu`` device type
+
+   -  setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``)
+
+   -  when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different
+
+   -  you can `raise issues <https://github.com/microsoft/LightGBM/issues>`__ in LightGBM GitHub repo when you meet the unstable results
+
+   -  **Note**: setting this to ``true`` may slow down the training
+
 Learning Control Parameters
 ---------------------------
 

include/LightGBM/config.h

@@ -218,6 +218,13 @@ struct Config {
   // desc = this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly
   int seed = 0;
 
+  // desc = used only with ``cpu`` device type
+  // desc = setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``)
+  // desc = when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different
+  // desc = you can `raise issues <https://github.com/microsoft/LightGBM/issues>`__ in LightGBM GitHub repo when you meet the unstable results
+  // desc = **Note**: setting this to ``true`` may slow down the training
+  bool deterministic = false;
+
   #pragma endregion
 
   #pragma region Learning Control Parameters

src/io/config.cpp

@@ -329,6 +329,9 @@ void Config::CheckParamConflict() {
   if (device_type == std::string("gpu") || device_type == std::string("cuda")) {
     force_col_wise = true;
     force_row_wise = false;
+    if (deterministic) {
+      Log::Warning("Although \"deterministic\" is set, the results ran by GPU may be non-deterministic.");
+    }
   }
 
   // force gpu_use_dp for CUDA

src/io/config_auto.cpp

@@ -183,6 +183,7 @@ const std::unordered_set<std::string>& Config::parameter_set() {
   "num_threads",
   "device_type",
   "seed",
+  "deterministic",
   "force_col_wise",
   "force_row_wise",
   "histogram_pool_size",
@@ -321,6 +322,8 @@ void Config::GetMembersFromString(const std::unordered_map<std::string, std::str
 
   GetInt(params, "num_threads", &num_threads);
 
+  GetBool(params, "deterministic", &deterministic);
+
   GetBool(params, "force_col_wise", &force_col_wise);
 
   GetBool(params, "force_row_wise", &force_row_wise);
@@ -625,6 +628,7 @@ std::string Config::SaveMembersToString() const {
   str_buf << "[learning_rate: " << learning_rate << "]\n";
   str_buf << "[num_leaves: " << num_leaves << "]\n";
   str_buf << "[num_threads: " << num_threads << "]\n";
+  str_buf << "[deterministic: " << deterministic << "]\n";
   str_buf << "[force_col_wise: " << force_col_wise << "]\n";
   str_buf << "[force_row_wise: " << force_row_wise << "]\n";
   str_buf << "[histogram_pool_size: " << histogram_pool_size << "]\n";

src/treelearner/leaf_splits.hpp

@@ -5,6 +5,7 @@
 #ifndef LIGHTGBM_TREELEARNER_LEAF_SPLITS_HPP_
 #define LIGHTGBM_TREELEARNER_LEAF_SPLITS_HPP_
 
+#include <LightGBM/config.h>
 #include <LightGBM/meta.h>
 #include <LightGBM/utils/threading.h>
 
@@ -20,8 +21,8 @@ namespace LightGBM {
 */
 class LeafSplits {
  public:
-  explicit LeafSplits(data_size_t num_data)
-    :num_data_in_leaf_(num_data), num_data_(num_data),
+  LeafSplits(data_size_t num_data, const Config* config)
+    :config_(config), num_data_in_leaf_(num_data), num_data_(num_data),
     data_indices_(nullptr), weight_(0) {
   }
   void ResetNumData(data_size_t num_data) {
@@ -70,7 +71,7 @@ class LeafSplits {
     data_indices_ = nullptr;
     double tmp_sum_gradients = 0.0f;
     double tmp_sum_hessians = 0.0f;
-#pragma omp parallel for schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians) if (num_data_in_leaf_ >= 1024)
+#pragma omp parallel for schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians) if (num_data_in_leaf_ >= 1024 && !config_->deterministic)
     for (data_size_t i = 0; i < num_data_in_leaf_; ++i) {
       tmp_sum_gradients += gradients[i];
       tmp_sum_hessians += hessians[i];
@@ -92,7 +93,7 @@ class LeafSplits {
     data_indices_ = data_partition->GetIndexOnLeaf(leaf, &num_data_in_leaf_);
     double tmp_sum_gradients = 0.0f;
     double tmp_sum_hessians = 0.0f;
-#pragma omp parallel for schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians) if (num_data_in_leaf_ >= 1024)
+#pragma omp parallel for schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians) if (num_data_in_leaf_ >= 1024 && !config_->deterministic)
     for (data_size_t i = 0; i < num_data_in_leaf_; ++i) {
       const data_size_t idx = data_indices_[i];
       tmp_sum_gradients += gradients[idx];
@@ -145,6 +146,7 @@ class LeafSplits {
 
 
  private:
+  const Config* config_;
   /*! \brief current leaf index */
   int leaf_index_;
   /*! \brief number of data on current leaf */

src/treelearner/serial_tree_learner.cpp

@@ -49,8 +49,8 @@ void SerialTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian
   constraints_.reset(LeafConstraintsBase::Create(config_, config_->num_leaves, train_data_->num_features()));
 
   // initialize splits for leaf
-  smaller_leaf_splits_.reset(new LeafSplits(train_data_->num_data()));
-  larger_leaf_splits_.reset(new LeafSplits(train_data_->num_data()));
+  smaller_leaf_splits_.reset(new LeafSplits(train_data_->num_data(), config_));
+  larger_leaf_splits_.reset(new LeafSplits(train_data_->num_data(), config_));
 
   // initialize data partition
   data_partition_.reset(new DataPartition(num_data_, config_->num_leaves));
@@ -775,7 +775,7 @@ void SerialTreeLearner::RecomputeBestSplitForLeaf(int leaf, SplitInfo* split) {
   int num_data = split->left_count + split->right_count;
 
   std::vector<SplitInfo> bests(share_state_->num_threads);
-  LeafSplits leaf_splits(num_data);
+  LeafSplits leaf_splits(num_data, config_);
   leaf_splits.Init(leaf, sum_gradients, sum_hessians);
 
   // can't use GetParentOutput because leaf_splits doesn't have weight property set

src/treelearner/voting_parallel_tree_learner.cpp

@@ -55,8 +55,8 @@ void VotingParallelTreeLearner<TREELEARNER_T>::Init(const Dataset* train_data, b
   larger_buffer_read_start_pos_.resize(this->num_features_);
   global_data_count_in_leaf_.resize(this->config_->num_leaves);
 
-  smaller_leaf_splits_global_.reset(new LeafSplits(train_data->num_data()));
-  larger_leaf_splits_global_.reset(new LeafSplits(train_data->num_data()));
+  smaller_leaf_splits_global_.reset(new LeafSplits(train_data->num_data(), this->config_));
+  larger_leaf_splits_global_.reset(new LeafSplits(train_data->num_data(), this->config_));
 
   local_config_ = *this->config_;
   local_config_.min_data_in_leaf /= num_machines_;