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reduce the buffer when using high dimensional data in distributed mode. #2485

Merged
merged 17 commits into from
Oct 15, 2019
Merged

reduce the buffer when using high dimensional data in distributed mode. #2485

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a67f686
reduce the buffer when using high dimensional data in distributed mode.
guolinke Oct 1, 2019
958ecfb
Update dataset_loader.cpp
guolinke Oct 3, 2019
3d2b82a
Merge remote-tracking branch 'origin/master' into dist-memory-reduce
guolinke Oct 3, 2019
fc3e573
refix
guolinke Oct 3, 2019
e363201
typo
guolinke Oct 3, 2019
b1598a0
fix number of bin accumulation.
guolinke Oct 4, 2019
1618260
avoid overflow
guolinke Oct 4, 2019
7e4f2ab
fix warning
guolinke Oct 4, 2019
affcc88
efficient solution.
guolinke Oct 4, 2019
3b59e76
Update dataset.h
guolinke Oct 4, 2019
96db1a6
fix bin count output
guolinke Oct 4, 2019
a73d4fd
fix warning
guolinke Oct 4, 2019
c25557c
bug in dist number of feature check
guolinke Oct 4, 2019
1847238
fix possible edge case
guolinke Oct 4, 2019
3152a3c
Update dataset.cpp
guolinke Oct 5, 2019
25ac715
possible bug fix
guolinke Oct 8, 2019
7e1e11d
fix
guolinke Oct 8, 2019
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1 change: 1 addition & 0 deletions include/LightGBM/dataset.h
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Original file line number Diff line number Diff line change
Expand Up @@ -294,6 +294,7 @@ class Dataset {
const std::vector<std::vector<double>>& forced_bins,
int** sample_non_zero_indices,
const int* num_per_col,
int num_sample_col,
size_t total_sample_cnt,
const Config& io_config);

Expand Down
13 changes: 13 additions & 0 deletions include/LightGBM/network.h
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Expand Up @@ -261,6 +261,19 @@ class Network {
return global;
}

template<class T>
static std::vector<T> GlobalArray(T local) {
std::vector<T> global(num_machines_, 0);
int type_size = sizeof(T);
std::vector<comm_size_t> block_start(num_machines_);
std::vector<comm_size_t> block_len(num_machines_, type_size);
for (int i = 1; i < num_machines_; ++i) {
block_start[i] = block_start[i - 1] + block_len[i - 1];
}
Allgather(reinterpret_cast<char*>(&local), block_start.data(), block_len.data(), reinterpret_cast<char*>(global.data()), type_size*num_machines_);
return global;
}

private:
static void AllgatherBruck(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size);

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4 changes: 3 additions & 1 deletion src/io/bin.cpp
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Expand Up @@ -19,7 +19,9 @@

namespace LightGBM {

BinMapper::BinMapper() {
BinMapper::BinMapper(): num_bin_(1), is_trivial_(true), bin_type_(BinType::NumericalBin) {
bin_upper_bound_.clear();
bin_upper_bound_.push_back(std::numeric_limits<double>::infinity());
}

// deep copy function for BinMapper
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28 changes: 20 additions & 8 deletions src/io/dataset.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,7 @@ std::vector<std::vector<int>> FindGroups(const std::vector<std::unique_ptr<BinMa
const std::vector<int>& find_order,
int** sample_indices,
const int* num_per_col,
int num_sample_col,
size_t total_sample_cnt,
data_size_t max_error_cnt,
data_size_t filter_cnt,
Expand All @@ -85,7 +86,8 @@ std::vector<std::vector<int>> FindGroups(const std::vector<std::unique_ptr<BinMa
std::vector<int> group_num_bin;

for (auto fidx : find_order) {
const size_t cur_non_zero_cnt = num_per_col[fidx];
bool is_filtered_feature = fidx >= num_sample_col;
const size_t cur_non_zero_cnt = is_filtered_feature ? 0: num_per_col[fidx];
bool need_new_group = true;
std::vector<int> available_groups;
for (int gid = 0; gid < static_cast<int>(features_in_group.size()); ++gid) {
Expand All @@ -107,7 +109,7 @@ std::vector<std::vector<int>> FindGroups(const std::vector<std::unique_ptr<BinMa
}
for (auto gid : search_groups) {
const int rest_max_cnt = max_error_cnt - group_conflict_cnt[gid];
int cnt = GetConfilctCount(conflict_marks[gid], sample_indices[fidx], num_per_col[fidx], rest_max_cnt);
const int cnt = is_filtered_feature ? 0 : GetConfilctCount(conflict_marks[gid], sample_indices[fidx], num_per_col[fidx], rest_max_cnt);
if (cnt >= 0 && cnt <= rest_max_cnt) {
data_size_t rest_non_zero_data = static_cast<data_size_t>(
static_cast<double>(cur_non_zero_cnt - cnt) * num_data / total_sample_cnt);
Expand All @@ -116,7 +118,9 @@ std::vector<std::vector<int>> FindGroups(const std::vector<std::unique_ptr<BinMa
features_in_group[gid].push_back(fidx);
group_conflict_cnt[gid] += cnt;
group_non_zero_cnt[gid] += cur_non_zero_cnt - cnt;
MarkUsed(&conflict_marks[gid], sample_indices[fidx], num_per_col[fidx]);
if (!is_filtered_feature) {
MarkUsed(&conflict_marks[gid], sample_indices[fidx], num_per_col[fidx]);
}
if (is_use_gpu) {
group_num_bin[gid] += bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0);
}
Expand All @@ -128,7 +132,9 @@ std::vector<std::vector<int>> FindGroups(const std::vector<std::unique_ptr<BinMa
features_in_group.back().push_back(fidx);
group_conflict_cnt.push_back(0);
conflict_marks.emplace_back(total_sample_cnt, false);
MarkUsed(&(conflict_marks.back()), sample_indices[fidx], num_per_col[fidx]);
if (!is_filtered_feature) {
MarkUsed(&(conflict_marks.back()), sample_indices[fidx], num_per_col[fidx]);
}
group_non_zero_cnt.emplace_back(cur_non_zero_cnt);
if (is_use_gpu) {
group_num_bin.push_back(1 + bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0));
Expand All @@ -141,6 +147,7 @@ std::vector<std::vector<int>> FindGroups(const std::vector<std::unique_ptr<BinMa
std::vector<std::vector<int>> FastFeatureBundling(const std::vector<std::unique_ptr<BinMapper>>& bin_mappers,
int** sample_indices,
const int* num_per_col,
int num_sample_col,
size_t total_sample_cnt,
const std::vector<int>& used_features,
double max_conflict_rate,
Expand All @@ -156,7 +163,11 @@ std::vector<std::vector<int>> FastFeatureBundling(const std::vector<std::unique_
feature_non_zero_cnt.reserve(used_features.size());
// put dense feature first
for (auto fidx : used_features) {
feature_non_zero_cnt.emplace_back(num_per_col[fidx]);
if (fidx < num_sample_col) {
feature_non_zero_cnt.emplace_back(num_per_col[fidx]);
} else {
feature_non_zero_cnt.emplace_back(0);
}
}
// sort by non zero cnt
std::vector<int> sorted_idx;
Expand All @@ -175,8 +186,8 @@ std::vector<std::vector<int>> FastFeatureBundling(const std::vector<std::unique_
for (auto sidx : sorted_idx) {
feature_order_by_cnt.push_back(used_features[sidx]);
}
auto features_in_group = FindGroups(bin_mappers, used_features, sample_indices, num_per_col, total_sample_cnt, max_error_cnt, filter_cnt, num_data, is_use_gpu);
auto group2 = FindGroups(bin_mappers, feature_order_by_cnt, sample_indices, num_per_col, total_sample_cnt, max_error_cnt, filter_cnt, num_data, is_use_gpu);
auto features_in_group = FindGroups(bin_mappers, used_features, sample_indices, num_per_col, num_sample_col, total_sample_cnt, max_error_cnt, filter_cnt, num_data, is_use_gpu);
auto group2 = FindGroups(bin_mappers, feature_order_by_cnt, sample_indices, num_per_col, num_sample_col, total_sample_cnt, max_error_cnt, filter_cnt, num_data, is_use_gpu);
if (features_in_group.size() > group2.size()) {
features_in_group = group2;
}
Expand Down Expand Up @@ -219,6 +230,7 @@ void Dataset::Construct(
const std::vector<std::vector<double>>& forced_bins,
int** sample_non_zero_indices,
const int* num_per_col,
int num_sample_col,
size_t total_sample_cnt,
const Config& io_config) {
num_total_features_ = num_total_features;
Expand All @@ -238,7 +250,7 @@ void Dataset::Construct(

if (io_config.enable_bundle && !used_features.empty()) {
features_in_group = FastFeatureBundling(*bin_mappers,
sample_non_zero_indices, num_per_col, total_sample_cnt,
sample_non_zero_indices, num_per_col, num_sample_col, total_sample_cnt,
used_features, io_config.max_conflict_rate,
num_data_, io_config.min_data_in_leaf,
sparse_threshold_, io_config.is_enable_sparse, io_config.device_type == std::string("gpu"));
Expand Down
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