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Support optimal partitioning for GPU hist. (#7652)

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* Implement `MaxCategory` in quantile.
* Implement partition-based split for GPU evaluation.  Currently, it's based on the existing evaluation function.
* Extract an evaluator from GPU Hist to store the needed states.
* Added some CUDA stream/event utilities.
* Update document with references.
* Fixed a bug in approx evaluator where the number of data points is less than the number of categories.
This commit is contained in:
Jiaming Yuan
2022-02-15 03:03:12 +08:00
committed by GitHub
parent 2369d55e9a
commit 0d0abe1845
26 changed files with 1088 additions and 528 deletions
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76
src/common/hist_util.h
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@@ -33,66 +33,84 @@ namespace common {
*/
using GHistIndexRow = Span<uint32_t const>;
// A CSC matrix representing histogram cuts, used in CPU quantile hist.
// A CSC matrix representing histogram cuts.
// The cut values represent upper bounds of bins containing approximately equal numbers of elements
class HistogramCuts {
bool has_categorical_{false};
float max_cat_{-1.0f};
protected:
using BinIdx = uint32_t;
void Swap(HistogramCuts&& that) noexcept(true) {
std::swap(cut_values_, that.cut_values_);
std::swap(cut_ptrs_, that.cut_ptrs_);
std::swap(min_vals_, that.min_vals_);
std::swap(has_categorical_, that.has_categorical_);
std::swap(max_cat_, that.max_cat_);
}
void Copy(HistogramCuts const& that) {
cut_values_.Resize(that.cut_values_.Size());
cut_ptrs_.Resize(that.cut_ptrs_.Size());
min_vals_.Resize(that.min_vals_.Size());
cut_values_.Copy(that.cut_values_);
cut_ptrs_.Copy(that.cut_ptrs_);
min_vals_.Copy(that.min_vals_);
has_categorical_ = that.has_categorical_;
max_cat_ = that.max_cat_;
}
public:
HostDeviceVector<bst_float> cut_values_; // NOLINT
HostDeviceVector<uint32_t> cut_ptrs_; // NOLINT
HostDeviceVector<float> cut_values_; // NOLINT
HostDeviceVector<uint32_t> cut_ptrs_; // NOLINT
// storing minimum value in a sketch set.
HostDeviceVector<float> min_vals_; // NOLINT
HistogramCuts();
HistogramCuts(HistogramCuts const& that) {
cut_values_.Resize(that.cut_values_.Size());
cut_ptrs_.Resize(that.cut_ptrs_.Size());
min_vals_.Resize(that.min_vals_.Size());
cut_values_.Copy(that.cut_values_);
cut_ptrs_.Copy(that.cut_ptrs_);
min_vals_.Copy(that.min_vals_);
}
HistogramCuts(HistogramCuts const& that) { this->Copy(that); }
HistogramCuts(HistogramCuts&& that) noexcept(true) {
*this = std::forward<HistogramCuts&&>(that);
this->Swap(std::forward<HistogramCuts>(that));
}
HistogramCuts& operator=(HistogramCuts const& that) {
cut_values_.Resize(that.cut_values_.Size());
cut_ptrs_.Resize(that.cut_ptrs_.Size());
min_vals_.Resize(that.min_vals_.Size());
cut_values_.Copy(that.cut_values_);
cut_ptrs_.Copy(that.cut_ptrs_);
min_vals_.Copy(that.min_vals_);
this->Copy(that);
return *this;
}
HistogramCuts& operator=(HistogramCuts&& that) noexcept(true) {
cut_ptrs_ = std::move(that.cut_ptrs_);
cut_values_ = std::move(that.cut_values_);
min_vals_ = std::move(that.min_vals_);
this->Swap(std::forward<HistogramCuts>(that));
return *this;
}
uint32_t FeatureBins(uint32_t feature) const {
return cut_ptrs_.ConstHostVector().at(feature + 1) -
cut_ptrs_.ConstHostVector()[feature];
uint32_t FeatureBins(bst_feature_t feature) const {
return cut_ptrs_.ConstHostVector().at(feature + 1) - cut_ptrs_.ConstHostVector()[feature];
}
// Getters. Cuts should be of no use after building histogram indices, but currently
// they are deeply linked with quantile_hist, gpu sketcher and gpu_hist, so we preserve
// these for now.
std::vector<uint32_t> const& Ptrs() const { return cut_ptrs_.ConstHostVector(); }
std::vector<float> const& Values() const { return cut_values_.ConstHostVector(); }
std::vector<float> const& MinValues() const { return min_vals_.ConstHostVector(); }
bool HasCategorical() const { return has_categorical_; }
float MaxCategory() const { return max_cat_; }
/**
* \brief Set meta info about categorical features.
*
* \param has_cat Do we have categorical feature in the data?
* \param max_cat The maximum categorical value in all features.
*/
void SetCategorical(bool has_cat, float max_cat) {
has_categorical_ = has_cat;
max_cat_ = max_cat;
}
size_t TotalBins() const { return cut_ptrs_.ConstHostVector().back(); }
// Return the index of a cut point that is strictly greater than the input
// value, or the last available index if none exists
BinIdx SearchBin(float value, uint32_t column_id, std::vector<uint32_t> const& ptrs,
BinIdx SearchBin(float value, bst_feature_t column_id, std::vector<uint32_t> const& ptrs,
std::vector<float> const& values) const {
auto end = ptrs[column_id + 1];
auto beg = ptrs[column_id];
@@ -102,7 +120,7 @@ class HistogramCuts {
return idx;
}
BinIdx SearchBin(float value, uint32_t column_id) const {
BinIdx SearchBin(float value, bst_feature_t column_id) const {
return this->SearchBin(value, column_id, Ptrs(), Values());
}