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Implement GK sketching on GPU. (#5846)

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* Implement GK sketching on GPU.
* Strong tests on quantile building.
* Handle sparse dataset by binary searching the column index.
* Hypothesis test on dask.
This commit is contained in:
Jiaming Yuan
2020-07-07 12:16:21 +08:00
committed by GitHub
parent ac3f0e78dc
commit 048d969be4
25 changed files with 2045 additions and 405 deletions
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10
src/common/hist_util.cc
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@@ -158,7 +158,6 @@ void SparseCuts::SingleThreadBuild(SparsePage const& page, MetaInfo const& info,
uint32_t beg_col, uint32_t end_col,
uint32_t thread_id) {
CHECK_GE(end_col, beg_col);
constexpr float kFactor = 8;
// Data groups, used in ranking.
std::vector<bst_uint> const& group_ptr = info.group_ptr_;
@@ -175,11 +174,12 @@ void SparseCuts::SingleThreadBuild(SparsePage const& page, MetaInfo const& info,
max_num_bins);
if (n_bins == 0) {
// cut_ptrs_ is initialized with a zero, so there's always an element at the back
CHECK_GE(local_ptrs.size(), 1);
local_ptrs.emplace_back(local_ptrs.back());
continue;
}
sketch.Init(info.num_row_, 1.0 / (n_bins * kFactor));
sketch.Init(info.num_row_, 1.0 / (n_bins * WQSketch::kFactor));
for (auto const& entry : column) {
uint32_t weight_ind = 0;
if (use_group_ind) {
@@ -329,7 +329,6 @@ void DenseCuts::Build(DMatrix* p_fmat, uint32_t max_num_bins) {
const MetaInfo& info = p_fmat->Info();
// safe factor for better accuracy
constexpr int kFactor = 8;
std::vector<WQSketch> sketchs;
const int nthread = omp_get_max_threads();
@@ -339,7 +338,7 @@ void DenseCuts::Build(DMatrix* p_fmat, uint32_t max_num_bins) {
unsigned const ncol = static_cast<unsigned>(info.num_col_);
sketchs.resize(info.num_col_);
for (auto& s : sketchs) {
s.Init(info.num_row_, 1.0 / (max_num_bins * kFactor));
s.Init(info.num_row_, 1.0 / (max_num_bins * WQSketch::kFactor));
}
// Data groups, used in ranking.
@@ -410,9 +409,8 @@ void DenseCuts::Init
// This allows efficient training on wide data
size_t global_max_rows = max_rows;
rabit::Allreduce<rabit::op::Sum>(&global_max_rows, 1);
constexpr int kFactor = 8;
size_t intermediate_num_cuts =
std::min(global_max_rows, static_cast<size_t>(max_num_bins * kFactor));
std::min(global_max_rows, static_cast<size_t>(max_num_bins * WQSketch::kFactor));
// gather the histogram data
rabit::SerializeReducer<WQSketch::SummaryContainer> sreducer;
std::vector<WQSketch::SummaryContainer> summary_array;