Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Refactor parts of fast histogram utilities (#3564)

Browse Source 
* Refactor parts of fast histogram utilities

* Removed byte packing from column matrix
This commit is contained in:
Rory Mitchell
2018-08-09 17:59:57 +12:00
committed by GitHub
parent 3c72654e3b
commit bbb771f32e
8 changed files with 184 additions and 288 deletions
Download Patch File Download Diff File Expand all files Collapse all files

90
src/common/hist_util.cc
View File

@@ -114,12 +114,23 @@ void HistCutMatrix::Init
}
}
void GHistIndexMatrix::Init(DMatrix* p_fmat) {
CHECK(cut != nullptr); // NOLINT
uint32_t HistCutMatrix::GetBinIdx(const Entry& e) {
unsigned fid = e.index;
auto cbegin = cut.begin() + row_ptr[fid];
auto cend = cut.begin() + row_ptr[fid + 1];
CHECK(cbegin != cend);
auto it = std::upper_bound(cbegin, cend, e.fvalue);
if (it == cend) it = cend - 1;
uint32_t idx = static_cast<uint32_t>(it - cut.begin());
return idx;
}
void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
cut.Init(p_fmat, max_num_bins);
auto iter = p_fmat->RowIterator();
const int nthread = omp_get_max_threads();
const uint32_t nbins = cut->row_ptr.back();
const uint32_t nbins = cut.row_ptr.back();
hit_count.resize(nbins, 0);
hit_count_tloc_.resize(nthread * nbins, 0);
@@ -133,8 +144,8 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat) {
}
index.resize(row_ptr.back());
CHECK_GT(cut->cut.size(), 0U);
CHECK_EQ(cut->row_ptr.back(), cut->cut.size());
CHECK_GT(cut.cut.size(), 0U);
CHECK_EQ(cut.row_ptr.back(), cut.cut.size());
auto bsize = static_cast<omp_ulong>(batch.Size());
#pragma omp parallel for num_threads(nthread) schedule(static)
@@ -145,13 +156,7 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat) {
SparsePage::Inst inst = batch[i];
CHECK_EQ(ibegin + inst.length, iend);
for (bst_uint j = 0; j < inst.length; ++j) {
unsigned fid = inst[j].index;
auto cbegin = cut->cut.begin() + cut->row_ptr[fid];
auto cend = cut->cut.begin() + cut->row_ptr[fid + 1];
CHECK(cbegin != cend);
auto it = std::upper_bound(cbegin, cend, inst[j].fvalue);
if (it == cend) it = cend - 1;
uint32_t idx = static_cast<uint32_t>(it - cut->cut.begin());
uint32_t idx = cut.GetBinIdx(inst[j]);
index[ibegin + j] = idx;
++hit_count_tloc_[tid * nbins + idx];
}
@@ -167,14 +172,13 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat) {
}
}
template <typename T>
static size_t GetConflictCount(const std::vector<bool>& mark,
const Column<T>& column,
const Column& column,
size_t max_cnt) {
size_t ret = 0;
if (column.type == xgboost::common::kDenseColumn) {
for (size_t i = 0; i < column.len; ++i) {
if (column.index[i] != std::numeric_limits<T>::max() && mark[i]) {
if (column.GetType() == xgboost::common::kDenseColumn) {
for (size_t i = 0; i < column.Size(); ++i) {
if (column.GetFeatureBinIdx(i) != std::numeric_limits<uint32_t>::max() && mark[i]) {
++ret;
if (ret > max_cnt) {
return max_cnt + 1;
@@ -182,8 +186,8 @@ static size_t GetConflictCount(const std::vector<bool>& mark,
}
}
} else {
for (size_t i = 0; i < column.len; ++i) {
if (mark[column.row_ind[i]]) {
for (size_t i = 0; i < column.Size(); ++i) {
if (mark[column.GetRowIdx(i)]) {
++ret;
if (ret > max_cnt) {
return max_cnt + 1;
@@ -194,30 +198,28 @@ static size_t GetConflictCount(const std::vector<bool>& mark,
return ret;
}
template <typename T>
inline void
MarkUsed(std::vector<bool>* p_mark, const Column<T>& column) {
MarkUsed(std::vector<bool>* p_mark, const Column& column) {
std::vector<bool>& mark = *p_mark;
if (column.type == xgboost::common::kDenseColumn) {
for (size_t i = 0; i < column.len; ++i) {
if (column.index[i] != std::numeric_limits<T>::max()) {
if (column.GetType() == xgboost::common::kDenseColumn) {
for (size_t i = 0; i < column.Size(); ++i) {
if (column.GetFeatureBinIdx(i) != std::numeric_limits<uint32_t>::max()) {
mark[i] = true;
}
}
} else {
for (size_t i = 0; i < column.len; ++i) {
mark[column.row_ind[i]] = true;
for (size_t i = 0; i < column.Size(); ++i) {
mark[column.GetRowIdx(i)] = true;
}
}
}
template <typename T>
inline std::vector<std::vector<unsigned>>
FindGroups_(const std::vector<unsigned>& feature_list,
const std::vector<size_t>& feature_nnz,
const ColumnMatrix& colmat,
size_t nrow,
const FastHistParam& param) {
FindGroups(const std::vector<unsigned>& feature_list,
const std::vector<size_t>& feature_nnz,
const ColumnMatrix& colmat,
size_t nrow,
const FastHistParam& param) {
/* Goal: Bundle features together that has little or no "overlap", i.e.
only a few data points should have nonzero values for
member features.
@@ -231,7 +233,7 @@ FindGroups_(const std::vector<unsigned>& feature_list,
= static_cast<size_t>(param.max_conflict_rate * nrow);
for (auto fid : feature_list) {
const Column<T>& column = colmat.GetColumn<T>(fid);
const Column& column = colmat.GetColumn(fid);
const size_t cur_fid_nnz = feature_nnz[fid];
bool need_new_group = true;
@@ -276,24 +278,12 @@ FindGroups_(const std::vector<unsigned>& feature_list,
return groups;
}
inline std::vector<std::vector<unsigned>>
FindGroups(const std::vector<unsigned>& feature_list,
const std::vector<size_t>& feature_nnz,
const ColumnMatrix& colmat,
size_t nrow,
const FastHistParam& param) {
XGBOOST_TYPE_SWITCH(colmat.dtype, {
return FindGroups_<DType>(feature_list, feature_nnz, colmat, nrow, param);
});
return std::vector<std::vector<unsigned>>(); // to avoid warning message
}
inline std::vector<std::vector<unsigned>>
FastFeatureGrouping(const GHistIndexMatrix& gmat,
const ColumnMatrix& colmat,
const FastHistParam& param) {
const size_t nrow = gmat.row_ptr.size() - 1;
const size_t nfeature = gmat.cut->row_ptr.size() - 1;
const size_t nfeature = gmat.cut.row_ptr.size() - 1;
std::vector<unsigned> feature_list(nfeature);
std::iota(feature_list.begin(), feature_list.end(), 0);
@@ -346,10 +336,10 @@ FastFeatureGrouping(const GHistIndexMatrix& gmat,
void GHistIndexBlockMatrix::Init(const GHistIndexMatrix& gmat,
const ColumnMatrix& colmat,
const FastHistParam& param) {
cut_ = gmat.cut;
cut_ = &gmat.cut;
const size_t nrow = gmat.row_ptr.size() - 1;
const uint32_t nbins = gmat.cut->row_ptr.back();
const uint32_t nbins = gmat.cut.row_ptr.back();
/* step 1: form feature groups */
auto groups = FastFeatureGrouping(gmat, colmat, param);
@@ -359,8 +349,8 @@ void GHistIndexBlockMatrix::Init(const GHistIndexMatrix& gmat,
std::vector<uint32_t> bin2block(nbins); // lookup table [bin id] => [block id]
for (uint32_t group_id = 0; group_id < nblock; ++group_id) {
for (auto& fid : groups[group_id]) {
const uint32_t bin_begin = gmat.cut->row_ptr[fid];
const uint32_t bin_end = gmat.cut->row_ptr[fid + 1];
const uint32_t bin_begin = gmat.cut.row_ptr[fid];
const uint32_t bin_end = gmat.cut.row_ptr[fid + 1];
for (uint32_t bin_id = bin_begin; bin_id < bin_end; ++bin_id) {
bin2block[bin_id] = group_id;
}