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data_parallel_tree_learner.cpp
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data_parallel_tree_learner.cpp
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/*!
* Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
*/
#include <cstring>
#include <tuple>
#include <vector>
#include "parallel_tree_learner.h"
namespace LightGBM {
template <typename TREELEARNER_T>
DataParallelTreeLearner<TREELEARNER_T>::DataParallelTreeLearner(const Config* config)
:TREELEARNER_T(config) {
}
template <typename TREELEARNER_T>
DataParallelTreeLearner<TREELEARNER_T>::~DataParallelTreeLearner() {
}
template <typename TREELEARNER_T>
void DataParallelTreeLearner<TREELEARNER_T>::Init(const Dataset* train_data, bool is_constant_hessian) {
// initialize SerialTreeLearner
TREELEARNER_T::Init(train_data, is_constant_hessian);
// Get local rank and global machine size
rank_ = Network::rank();
num_machines_ = Network::num_machines();
auto max_cat_threshold = this->config_->max_cat_threshold;
// need to be able to hold smaller and larger best splits in SyncUpGlobalBestSplit
size_t split_info_size = static_cast<size_t>(SplitInfo::Size(max_cat_threshold) * 2);
size_t histogram_size = this->config_->use_quantized_grad ?
static_cast<size_t>(this->share_state_->num_hist_total_bin() * kInt32HistEntrySize) :
static_cast<size_t>(this->share_state_->num_hist_total_bin() * kHistEntrySize);
// allocate buffer for communication
size_t buffer_size = std::max(histogram_size, split_info_size);
input_buffer_.resize(buffer_size);
output_buffer_.resize(buffer_size);
is_feature_aggregated_.resize(this->num_features_);
block_start_.resize(num_machines_);
block_len_.resize(num_machines_);
if (this->config_->use_quantized_grad) {
block_start_int16_.resize(num_machines_);
block_len_int16_.resize(num_machines_);
}
buffer_write_start_pos_.resize(this->num_features_);
buffer_read_start_pos_.resize(this->num_features_);
if (this->config_->use_quantized_grad) {
buffer_write_start_pos_int16_.resize(this->num_features_);
buffer_read_start_pos_int16_.resize(this->num_features_);
}
global_data_count_in_leaf_.resize(this->config_->num_leaves);
}
template <typename TREELEARNER_T>
void DataParallelTreeLearner<TREELEARNER_T>::ResetConfig(const Config* config) {
TREELEARNER_T::ResetConfig(config);
global_data_count_in_leaf_.resize(this->config_->num_leaves);
}
template <typename TREELEARNER_T>
void DataParallelTreeLearner<TREELEARNER_T>::PrepareBufferPos(
const std::vector<std::vector<int>>& feature_distribution,
std::vector<comm_size_t>* block_start,
std::vector<comm_size_t>* block_len,
std::vector<comm_size_t>* buffer_write_start_pos,
std::vector<comm_size_t>* buffer_read_start_pos,
comm_size_t* reduce_scatter_size,
size_t hist_entry_size) {
// get block start and block len for reduce scatter
*reduce_scatter_size = 0;
for (int i = 0; i < num_machines_; ++i) {
(*block_len)[i] = 0;
for (auto fid : feature_distribution[i]) {
auto num_bin = this->train_data_->FeatureNumBin(fid);
if (this->train_data_->FeatureBinMapper(fid)->GetMostFreqBin() == 0) {
num_bin -= 1;
}
(*block_len)[i] += num_bin * hist_entry_size;
}
*reduce_scatter_size += (*block_len)[i];
}
(*block_start)[0] = 0;
for (int i = 1; i < num_machines_; ++i) {
(*block_start)[i] = (*block_start)[i - 1] + (*block_len)[i - 1];
}
// get buffer_write_start_pos
int bin_size = 0;
for (int i = 0; i < num_machines_; ++i) {
for (auto fid : feature_distribution[i]) {
(*buffer_write_start_pos)[fid] = bin_size;
auto num_bin = this->train_data_->FeatureNumBin(fid);
if (this->train_data_->FeatureBinMapper(fid)->GetMostFreqBin() == 0) {
num_bin -= 1;
}
bin_size += num_bin * hist_entry_size;
}
}
// get buffer_read_start_pos
bin_size = 0;
for (auto fid : feature_distribution[rank_]) {
(*buffer_read_start_pos)[fid] = bin_size;
auto num_bin = this->train_data_->FeatureNumBin(fid);
if (this->train_data_->FeatureBinMapper(fid)->GetMostFreqBin() == 0) {
num_bin -= 1;
}
bin_size += num_bin * hist_entry_size;
}
}
template <typename TREELEARNER_T>
void DataParallelTreeLearner<TREELEARNER_T>::BeforeTrain() {
TREELEARNER_T::BeforeTrain();
// generate feature partition for current tree
std::vector<std::vector<int>> feature_distribution(num_machines_, std::vector<int>());
std::vector<int> num_bins_distributed(num_machines_, 0);
for (int i = 0; i < this->train_data_->num_total_features(); ++i) {
int inner_feature_index = this->train_data_->InnerFeatureIndex(i);
if (inner_feature_index == -1) { continue; }
if (this->col_sampler_.is_feature_used_bytree()[inner_feature_index]) {
int cur_min_machine = static_cast<int>(ArrayArgs<int>::ArgMin(num_bins_distributed));
feature_distribution[cur_min_machine].push_back(inner_feature_index);
auto num_bin = this->train_data_->FeatureNumBin(inner_feature_index);
if (this->train_data_->FeatureBinMapper(inner_feature_index)->GetMostFreqBin() == 0) {
num_bin -= 1;
}
num_bins_distributed[cur_min_machine] += num_bin;
}
is_feature_aggregated_[inner_feature_index] = false;
}
// get local used feature
for (auto fid : feature_distribution[rank_]) {
is_feature_aggregated_[fid] = true;
}
// get block start and block len for reduce scatter
if (this->config_->use_quantized_grad) {
PrepareBufferPos(feature_distribution, &block_start_, &block_len_, &buffer_write_start_pos_,
&buffer_read_start_pos_, &reduce_scatter_size_, kInt32HistEntrySize);
PrepareBufferPos(feature_distribution, &block_start_int16_, &block_len_int16_, &buffer_write_start_pos_int16_,
&buffer_read_start_pos_int16_, &reduce_scatter_size_int16_, kInt16HistEntrySize);
} else {
PrepareBufferPos(feature_distribution, &block_start_, &block_len_, &buffer_write_start_pos_,
&buffer_read_start_pos_, &reduce_scatter_size_, kHistEntrySize);
}
if (this->config_->use_quantized_grad) {
// sync global data sumup info
std::tuple<data_size_t, double, double, int64_t> data(this->smaller_leaf_splits_->num_data_in_leaf(),
this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians(),
this->smaller_leaf_splits_->int_sum_gradients_and_hessians());
int size = sizeof(data);
std::memcpy(input_buffer_.data(), &data, size);
// global sumup reduce
Network::Allreduce(input_buffer_.data(), size, sizeof(std::tuple<data_size_t, double, double, int64_t>), output_buffer_.data(), [](const char *src, char *dst, int type_size, comm_size_t len) {
comm_size_t used_size = 0;
const std::tuple<data_size_t, double, double, int64_t> *p1;
std::tuple<data_size_t, double, double, int64_t> *p2;
while (used_size < len) {
p1 = reinterpret_cast<const std::tuple<data_size_t, double, double, int64_t> *>(src);
p2 = reinterpret_cast<std::tuple<data_size_t, double, double, int64_t> *>(dst);
std::get<0>(*p2) = std::get<0>(*p2) + std::get<0>(*p1);
std::get<1>(*p2) = std::get<1>(*p2) + std::get<1>(*p1);
std::get<2>(*p2) = std::get<2>(*p2) + std::get<2>(*p1);
std::get<3>(*p2) = std::get<3>(*p2) + std::get<3>(*p1);
src += type_size;
dst += type_size;
used_size += type_size;
}
});
// copy back
std::memcpy(reinterpret_cast<void*>(&data), output_buffer_.data(), size);
// set global sumup info
this->smaller_leaf_splits_->Init(std::get<1>(data), std::get<2>(data), std::get<3>(data));
// init global data count in leaf
global_data_count_in_leaf_[0] = std::get<0>(data);
// reset hist num bits according to global num data
this->gradient_discretizer_->template SetNumBitsInHistogramBin<true>(0, -1, GetGlobalDataCountInLeaf(0), 0);
} else {
// sync global data sumup info
std::tuple<data_size_t, double, double> data(this->smaller_leaf_splits_->num_data_in_leaf(),
this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians());
int size = sizeof(data);
std::memcpy(input_buffer_.data(), &data, size);
// global sumup reduce
Network::Allreduce(input_buffer_.data(), size, sizeof(std::tuple<data_size_t, double, double>), output_buffer_.data(), [](const char *src, char *dst, int type_size, comm_size_t len) {
comm_size_t used_size = 0;
const std::tuple<data_size_t, double, double> *p1;
std::tuple<data_size_t, double, double> *p2;
while (used_size < len) {
p1 = reinterpret_cast<const std::tuple<data_size_t, double, double> *>(src);
p2 = reinterpret_cast<std::tuple<data_size_t, double, double> *>(dst);
std::get<0>(*p2) = std::get<0>(*p2) + std::get<0>(*p1);
std::get<1>(*p2) = std::get<1>(*p2) + std::get<1>(*p1);
std::get<2>(*p2) = std::get<2>(*p2) + std::get<2>(*p1);
src += type_size;
dst += type_size;
used_size += type_size;
}
});
// copy back
std::memcpy(reinterpret_cast<void*>(&data), output_buffer_.data(), size);
// set global sumup info
this->smaller_leaf_splits_->Init(std::get<1>(data), std::get<2>(data));
// init global data count in leaf
global_data_count_in_leaf_[0] = std::get<0>(data);
}
}
template <typename TREELEARNER_T>
void DataParallelTreeLearner<TREELEARNER_T>::FindBestSplits(const Tree* tree) {
TREELEARNER_T::ConstructHistograms(
this->col_sampler_.is_feature_used_bytree(), true);
const int smaller_leaf_index = this->smaller_leaf_splits_->leaf_index();
const data_size_t local_data_on_smaller_leaf = this->data_partition_->leaf_count(smaller_leaf_index);
if (local_data_on_smaller_leaf <= 0) {
// clear histogram buffer before synchronizing
// otherwise histogram contents from the previous iteration will be sent
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) {
if (this->col_sampler_.is_feature_used_bytree()[feature_index] == false)
continue;
const BinMapper* feature_bin_mapper = this->train_data_->FeatureBinMapper(feature_index);
const int offset = static_cast<int>(feature_bin_mapper->GetMostFreqBin() == 0);
const int num_bin = feature_bin_mapper->num_bin();
if (this->config_->use_quantized_grad) {
int32_t* hist_ptr = this->smaller_leaf_histogram_array_[feature_index].RawDataInt32();
std::memset(reinterpret_cast<void*>(hist_ptr), 0, (num_bin - offset) * kInt32HistEntrySize);
int16_t* hist_ptr_int16 = this->smaller_leaf_histogram_array_[feature_index].RawDataInt16();
std::memset(reinterpret_cast<void*>(hist_ptr_int16), 0, (num_bin - offset) * kInt16HistEntrySize);
} else {
hist_t* hist_ptr = this->smaller_leaf_histogram_array_[feature_index].RawData();
std::memset(reinterpret_cast<void*>(hist_ptr), 0, (num_bin - offset) * kHistEntrySize);
}
}
}
// construct local histograms
global_timer.Start("DataParallelTreeLearner::ReduceHistogram");
global_timer.Start("DataParallelTreeLearner::ReduceHistogram::Copy");
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) {
if (this->col_sampler_.is_feature_used_bytree()[feature_index] == false)
continue;
// copy to buffer
if (this->config_->use_quantized_grad) {
const uint8_t local_smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<false>(this->smaller_leaf_splits_->leaf_index());
const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->smaller_leaf_splits_->leaf_index());
if (smaller_leaf_num_bits <= 16) {
std::memcpy(input_buffer_.data() + buffer_write_start_pos_int16_[feature_index],
this->smaller_leaf_histogram_array_[feature_index].RawDataInt16(),
this->smaller_leaf_histogram_array_[feature_index].SizeOfInt16Histgram());
} else {
if (local_smaller_leaf_num_bits == 32) {
std::memcpy(input_buffer_.data() + buffer_write_start_pos_[feature_index],
this->smaller_leaf_histogram_array_[feature_index].RawDataInt32(),
this->smaller_leaf_histogram_array_[feature_index].SizeOfInt32Histgram());
} else {
this->smaller_leaf_histogram_array_[feature_index].CopyFromInt16ToInt32(
input_buffer_.data() + buffer_write_start_pos_[feature_index]);
}
}
} else {
std::memcpy(input_buffer_.data() + buffer_write_start_pos_[feature_index],
this->smaller_leaf_histogram_array_[feature_index].RawData(),
this->smaller_leaf_histogram_array_[feature_index].SizeOfHistgram());
}
}
global_timer.Stop("DataParallelTreeLearner::ReduceHistogram::Copy");
// Reduce scatter for histogram
global_timer.Start("DataParallelTreeLearner::ReduceHistogram::ReduceScatter");
if (!this->config_->use_quantized_grad) {
Network::ReduceScatter(input_buffer_.data(), reduce_scatter_size_, sizeof(hist_t), block_start_.data(),
block_len_.data(), output_buffer_.data(), static_cast<comm_size_t>(output_buffer_.size()), &HistogramSumReducer);
} else {
const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->smaller_leaf_splits_->leaf_index());
if (smaller_leaf_num_bits <= 16) {
Network::ReduceScatter(input_buffer_.data(), reduce_scatter_size_int16_, sizeof(int16_t), block_start_int16_.data(),
block_len_int16_.data(), output_buffer_.data(), static_cast<comm_size_t>(output_buffer_.size()), &Int16HistogramSumReducer);
} else {
Network::ReduceScatter(input_buffer_.data(), reduce_scatter_size_, sizeof(int_hist_t), block_start_.data(),
block_len_.data(), output_buffer_.data(), static_cast<comm_size_t>(output_buffer_.size()), &Int32HistogramSumReducer);
}
}
global_timer.Stop("DataParallelTreeLearner::ReduceHistogram::ReduceScatter");
global_timer.Stop("DataParallelTreeLearner::ReduceHistogram");
this->FindBestSplitsFromHistograms(
this->col_sampler_.is_feature_used_bytree(), true, tree);
}
template <typename TREELEARNER_T>
void DataParallelTreeLearner<TREELEARNER_T>::FindBestSplitsFromHistograms(const std::vector<int8_t>&, bool, const Tree* tree) {
std::vector<SplitInfo> smaller_bests_per_thread(this->share_state_->num_threads);
std::vector<SplitInfo> larger_bests_per_thread(this->share_state_->num_threads);
std::vector<int8_t> smaller_node_used_features =
this->col_sampler_.GetByNode(tree, this->smaller_leaf_splits_->leaf_index());
std::vector<int8_t> larger_node_used_features =
this->col_sampler_.GetByNode(tree, this->larger_leaf_splits_->leaf_index());
double smaller_leaf_parent_output = this->GetParentOutput(tree, this->smaller_leaf_splits_.get());
double larger_leaf_parent_output = this->GetParentOutput(tree, this->larger_leaf_splits_.get());
if (this->config_->use_quantized_grad && this->larger_leaf_splits_ != nullptr && this->larger_leaf_splits_->leaf_index() >= 0) {
const int parent_index = std::min(this->smaller_leaf_splits_->leaf_index(), this->larger_leaf_splits_->leaf_index());
const uint8_t parent_num_bits = this->gradient_discretizer_->template GetHistBitsInNode<true>(parent_index);
const uint8_t larger_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->larger_leaf_splits_->leaf_index());
const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->smaller_leaf_splits_->leaf_index());
if (parent_num_bits > 16 && larger_leaf_num_bits <= 16) {
CHECK_LE(smaller_leaf_num_bits, 16);
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) {
OMP_LOOP_EX_BEGIN();
if (!is_feature_aggregated_[feature_index]) continue;
this->larger_leaf_histogram_array_[feature_index].CopyToBuffer(this->gradient_discretizer_->GetChangeHistBitsBuffer(feature_index));
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
}
}
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) {
OMP_LOOP_EX_BEGIN();
if (!is_feature_aggregated_[feature_index]) continue;
const int tid = omp_get_thread_num();
const int real_feature_index = this->train_data_->RealFeatureIndex(feature_index);
// restore global histograms from buffer
if (this->config_->use_quantized_grad) {
const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->smaller_leaf_splits_->leaf_index());
if (smaller_leaf_num_bits <= 16) {
this->smaller_leaf_histogram_array_[feature_index].FromMemoryInt16(
output_buffer_.data() + buffer_read_start_pos_int16_[feature_index]);
} else {
this->smaller_leaf_histogram_array_[feature_index].FromMemoryInt32(
output_buffer_.data() + buffer_read_start_pos_[feature_index]);
}
} else {
this->smaller_leaf_histogram_array_[feature_index].FromMemory(
output_buffer_.data() + buffer_read_start_pos_[feature_index]);
}
if (this->config_->use_quantized_grad) {
const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->smaller_leaf_splits_->leaf_index());
const int64_t int_sum_gradient_and_hessian = this->smaller_leaf_splits_->int_sum_gradients_and_hessians();
if (smaller_leaf_num_bits <= 16) {
this->train_data_->template FixHistogramInt<int32_t, int32_t, 16, 16>(
feature_index,
int_sum_gradient_and_hessian,
reinterpret_cast<hist_t*>(this->smaller_leaf_histogram_array_[feature_index].RawDataInt16()));
} else {
this->train_data_->template FixHistogramInt<int64_t, int64_t, 32, 32>(
feature_index,
int_sum_gradient_and_hessian,
reinterpret_cast<hist_t*>(this->smaller_leaf_histogram_array_[feature_index].RawDataInt32()));
}
} else {
this->train_data_->FixHistogram(feature_index,
this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians(),
this->smaller_leaf_histogram_array_[feature_index].RawData());
}
this->ComputeBestSplitForFeature(
this->smaller_leaf_histogram_array_, feature_index, real_feature_index,
smaller_node_used_features[feature_index],
GetGlobalDataCountInLeaf(this->smaller_leaf_splits_->leaf_index()),
this->smaller_leaf_splits_.get(),
&smaller_bests_per_thread[tid],
smaller_leaf_parent_output);
// only root leaf
if (this->larger_leaf_splits_ == nullptr || this->larger_leaf_splits_->leaf_index() < 0) continue;
// construct histgroms for large leaf, we init larger leaf as the parent, so we can just subtract the smaller leaf's histograms
if (this->config_->use_quantized_grad) {
const int parent_index = std::min(this->smaller_leaf_splits_->leaf_index(), this->larger_leaf_splits_->leaf_index());
const uint8_t parent_num_bits = this->gradient_discretizer_->template GetHistBitsInNode<true>(parent_index);
const uint8_t larger_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->larger_leaf_splits_->leaf_index());
const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf<true>(this->smaller_leaf_splits_->leaf_index());
if (parent_num_bits <= 16) {
CHECK_LE(smaller_leaf_num_bits, 16);
CHECK_LE(larger_leaf_num_bits, 16);
this->larger_leaf_histogram_array_[feature_index].template Subtract<true, int32_t, int32_t, int32_t, 16, 16, 16>(
this->smaller_leaf_histogram_array_[feature_index]);
} else if (larger_leaf_num_bits <= 16) {
CHECK_LE(smaller_leaf_num_bits, 16);
this->larger_leaf_histogram_array_[feature_index].template Subtract<true, int64_t, int32_t, int32_t, 32, 16, 16>(
this->smaller_leaf_histogram_array_[feature_index], this->gradient_discretizer_->GetChangeHistBitsBuffer(feature_index));
} else if (smaller_leaf_num_bits <= 16) {
this->larger_leaf_histogram_array_[feature_index].template Subtract<true, int64_t, int32_t, int64_t, 32, 16, 32>(
this->smaller_leaf_histogram_array_[feature_index]);
} else {
this->larger_leaf_histogram_array_[feature_index].template Subtract<true, int64_t, int64_t, int64_t, 32, 32, 32>(
this->smaller_leaf_histogram_array_[feature_index]);
}
} else {
this->larger_leaf_histogram_array_[feature_index].Subtract(
this->smaller_leaf_histogram_array_[feature_index]);
}
this->ComputeBestSplitForFeature(
this->larger_leaf_histogram_array_, feature_index, real_feature_index,
larger_node_used_features[feature_index],
GetGlobalDataCountInLeaf(this->larger_leaf_splits_->leaf_index()),
this->larger_leaf_splits_.get(),
&larger_bests_per_thread[tid],
larger_leaf_parent_output);
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
auto smaller_best_idx = ArrayArgs<SplitInfo>::ArgMax(smaller_bests_per_thread);
int leaf = this->smaller_leaf_splits_->leaf_index();
this->best_split_per_leaf_[leaf] = smaller_bests_per_thread[smaller_best_idx];
if (this->larger_leaf_splits_ != nullptr && this->larger_leaf_splits_->leaf_index() >= 0) {
leaf = this->larger_leaf_splits_->leaf_index();
auto larger_best_idx = ArrayArgs<SplitInfo>::ArgMax(larger_bests_per_thread);
this->best_split_per_leaf_[leaf] = larger_bests_per_thread[larger_best_idx];
}
SplitInfo smaller_best_split, larger_best_split;
smaller_best_split = this->best_split_per_leaf_[this->smaller_leaf_splits_->leaf_index()];
// find local best split for larger leaf
if (this->larger_leaf_splits_->leaf_index() >= 0) {
larger_best_split = this->best_split_per_leaf_[this->larger_leaf_splits_->leaf_index()];
}
// sync global best info
SyncUpGlobalBestSplit(input_buffer_.data(), input_buffer_.data(), &smaller_best_split, &larger_best_split, this->config_->max_cat_threshold);
// set best split
this->best_split_per_leaf_[this->smaller_leaf_splits_->leaf_index()] = smaller_best_split;
if (this->larger_leaf_splits_->leaf_index() >= 0) {
this->best_split_per_leaf_[this->larger_leaf_splits_->leaf_index()] = larger_best_split;
}
}
template <typename TREELEARNER_T>
void DataParallelTreeLearner<TREELEARNER_T>::Split(Tree* tree, int best_Leaf, int* left_leaf, int* right_leaf) {
TREELEARNER_T::SplitInner(tree, best_Leaf, left_leaf, right_leaf, false);
const SplitInfo& best_split_info = this->best_split_per_leaf_[best_Leaf];
// need update global number of data in leaf
global_data_count_in_leaf_[*left_leaf] = best_split_info.left_count;
global_data_count_in_leaf_[*right_leaf] = best_split_info.right_count;
// reset hist num bits according to global num data
if (this->config_->use_quantized_grad) {
this->gradient_discretizer_->template SetNumBitsInHistogramBin<true>(*left_leaf, *right_leaf, GetGlobalDataCountInLeaf(*left_leaf), GetGlobalDataCountInLeaf(*right_leaf));
}
}
// instantiate template classes, otherwise linker cannot find the code
template class DataParallelTreeLearner<GPUTreeLearner>;
template class DataParallelTreeLearner<SerialTreeLearner>;
} // namespace LightGBM