Define bin type. (#7850)
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@@ -34,8 +34,8 @@ class Column {
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public:
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static constexpr int32_t kMissingId = -1;
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Column(ColumnType type, common::Span<const BinIdxType> index, const uint32_t index_base)
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: type_(type), index_(index), index_base_(index_base) {}
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Column(ColumnType type, common::Span<const BinIdxType> index, const bst_bin_t index_base)
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: type_(type), index_(index), index_base_{index_base} {}
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virtual ~Column() = default;
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@@ -60,19 +60,19 @@ class Column {
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/* bin indexes in range [0, max_bins - 1] */
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common::Span<const BinIdxType> index_;
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/* bin index offset for specific feature */
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const uint32_t index_base_;
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bst_bin_t const index_base_;
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};
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template <typename BinIdxType>
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class SparseColumn : public Column<BinIdxType> {
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public:
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SparseColumn(ColumnType type, common::Span<const BinIdxType> index, uint32_t index_base,
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SparseColumn(ColumnType type, common::Span<const BinIdxType> index, bst_bin_t index_base,
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common::Span<const size_t> row_ind)
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: Column<BinIdxType>(type, index, index_base), row_ind_(row_ind) {}
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const size_t* GetRowData() const { return row_ind_.data(); }
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int32_t GetBinIdx(size_t rid, size_t* state) const {
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bst_bin_t GetBinIdx(size_t rid, size_t* state) const {
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const size_t column_size = this->Size();
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if (!((*state) < column_size)) {
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return this->kMissingId;
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@@ -40,8 +40,6 @@ class HistogramCuts {
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float max_cat_{-1.0f};
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protected:
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using BinIdx = uint32_t;
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void Swap(HistogramCuts&& that) noexcept(true) {
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std::swap(cut_values_, that.cut_values_);
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std::swap(cut_ptrs_, that.cut_ptrs_);
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@@ -110,31 +108,31 @@ class HistogramCuts {
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// Return the index of a cut point that is strictly greater than the input
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// value, or the last available index if none exists
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BinIdx SearchBin(float value, bst_feature_t column_id, std::vector<uint32_t> const& ptrs,
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std::vector<float> const& values) const {
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bst_bin_t SearchBin(float value, bst_feature_t column_id, std::vector<uint32_t> const& ptrs,
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std::vector<float> const& values) const {
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auto end = ptrs[column_id + 1];
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auto beg = ptrs[column_id];
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auto it = std::upper_bound(values.cbegin() + beg, values.cbegin() + end, value);
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BinIdx idx = it - values.cbegin();
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bst_bin_t idx = it - values.cbegin();
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idx -= !!(idx == end);
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return idx;
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}
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BinIdx SearchBin(float value, bst_feature_t column_id) const {
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bst_bin_t SearchBin(float value, bst_feature_t column_id) const {
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return this->SearchBin(value, column_id, Ptrs(), Values());
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}
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/**
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* \brief Search the bin index for numerical feature.
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*/
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BinIdx SearchBin(Entry const& e) const {
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bst_bin_t SearchBin(Entry const& e) const {
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return SearchBin(e.fvalue, e.index);
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}
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/**
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* \brief Search the bin index for categorical feature.
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*/
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BinIdx SearchCatBin(Entry const &e) const {
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bst_bin_t SearchCatBin(Entry const &e) const {
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auto const &ptrs = this->Ptrs();
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auto const &vals = this->Values();
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auto end = ptrs.at(e.index + 1) + vals.cbegin();
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@@ -296,10 +294,10 @@ struct Index {
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};
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template <typename GradientIndex>
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int32_t XGBOOST_HOST_DEV_INLINE BinarySearchBin(size_t begin, size_t end,
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GradientIndex const &data,
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uint32_t const fidx_begin,
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uint32_t const fidx_end) {
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bst_bin_t XGBOOST_HOST_DEV_INLINE BinarySearchBin(size_t begin, size_t end,
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GradientIndex const& data,
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uint32_t const fidx_begin,
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uint32_t const fidx_end) {
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size_t previous_middle = std::numeric_limits<size_t>::max();
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while (end != begin) {
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size_t middle = begin + (end - begin) / 2;
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@@ -324,8 +322,6 @@ int32_t XGBOOST_HOST_DEV_INLINE BinarySearchBin(size_t begin, size_t end,
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return -1;
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}
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class ColumnMatrix;
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template<typename GradientSumT>
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using GHistRow = Span<xgboost::detail::GradientPairInternal<GradientSumT> >;
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