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Implement categorical prediction for CPU and GPU predict leaf. #7001

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merged 7 commits into from Jun 11, 2021
Merged

Implement categorical prediction for CPU and GPU predict leaf. #7001

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c3c82e4
Categorical prediction with CPU predictor and GPU predict leaf.
trivialfis May 26, 2021
4f444c2
Move into cpu predictor.
trivialfis Jun 1, 2021
8be6b46
Fix.
trivialfis Jun 1, 2021
9a68292
Lint.
trivialfis Jun 1, 2021
907ce2e
fix perf
Jun 4, 2021
bf1af8f
Remove unused variable.
trivialfis Jun 5, 2021
21bb93d
Lint.
trivialfis Jun 5, 2021
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66 changes: 18 additions & 48 deletions include/xgboost/tree_model.h
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Expand Up @@ -445,6 +445,10 @@ class RegTree : public Model {
bst_float right_leaf_weight, bst_float loss_change,
float sum_hess, float left_sum, float right_sum);

bool HasCategoricalSplit() const {
return !split_categories_.empty();
}

/*!
* \brief get current depth
* \param nid node id
Expand Down Expand Up @@ -537,13 +541,6 @@ class RegTree : public Model {
std::vector<Entry> data_;
bool has_missing_;
};
/*!
* \brief get the leaf index
* \param feat dense feature vector, if the feature is missing the field is set to NaN
* \return the leaf index of the given feature
*/
template <bool has_missing = true>
int GetLeafIndex(const FVec& feat) const;

/*!
* \brief calculate the feature contributions (https://arxiv.org/abs/1706.06060) for the tree
Expand Down Expand Up @@ -582,14 +579,6 @@ class RegTree : public Model {
*/
void CalculateContributionsApprox(const RegTree::FVec& feat,
bst_float* out_contribs) const;
/*!
* \brief get next position of the tree given current pid
* \param pid Current node id.
* \param fvalue feature value if not missing.
* \param is_unknown Whether current required feature is missing.
*/
template <bool has_missing = true>
inline int GetNext(int pid, bst_float fvalue, bool is_unknown) const;
/*!
* \brief dump the model in the requested format as a text string
* \param fmap feature map that may help give interpretations of feature
Expand Down Expand Up @@ -627,6 +616,20 @@ class RegTree : public Model {
size_t size {0};
};

struct CategoricalSplitMatrix {
common::Span<FeatureType const> split_type;
common::Span<uint32_t const> categories;
common::Span<Segment const> node_ptr;
};

CategoricalSplitMatrix GetCategoriesMatrix() const {
CategoricalSplitMatrix view;
view.split_type = common::Span<FeatureType const>(this->GetSplitTypes());
view.categories = this->GetSplitCategories();
view.node_ptr = common::Span<Segment const>(split_categories_segments_);
return view;
}

private:
void LoadCategoricalSplit(Json const& in);
void SaveCategoricalSplit(Json* p_out) const;
Expand Down Expand Up @@ -724,38 +727,5 @@ inline bool RegTree::FVec::IsMissing(size_t i) const {
inline bool RegTree::FVec::HasMissing() const {
return has_missing_;
}

template <bool has_missing>
inline int RegTree::GetLeafIndex(const RegTree::FVec& feat) const {
bst_node_t nid = 0;
while (!(*this)[nid].IsLeaf()) {
unsigned split_index = (*this)[nid].SplitIndex();
nid = this->GetNext<has_missing>(nid, feat.GetFvalue(split_index),
has_missing && feat.IsMissing(split_index));
}
return nid;
}

/*! \brief get next position of the tree given current pid */
template <bool has_missing>
inline int RegTree::GetNext(int pid, bst_float fvalue, bool is_unknown) const {
if (has_missing) {
if (is_unknown) {
return (*this)[pid].DefaultChild();
} else {
if (fvalue < (*this)[pid].SplitCond()) {
return (*this)[pid].LeftChild();
} else {
return (*this)[pid].RightChild();
}
}
} else {
// 35% speed up due to reduced miss branch predictions
// The following expression returns the left child if (fvalue < split_cond);
// the right child otherwise.
return (*this)[pid].LeftChild() + !(fvalue < (*this)[pid].SplitCond());
}
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}

} // namespace xgboost
#endif // XGBOOST_TREE_MODEL_H_
103 changes: 76 additions & 27 deletions src/predictor/cpu_predictor.cc
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Expand Up @@ -16,16 +16,31 @@
#include "xgboost/logging.h"
#include "xgboost/host_device_vector.h"

#include "predict_fn.h"
#include "../data/adapter.h"
#include "../common/math.h"
#include "../common/threading_utils.h"
#include "../common/categorical.h"
#include "../gbm/gbtree_model.h"

namespace xgboost {
namespace predictor {

DMLC_REGISTRY_FILE_TAG(cpu_predictor);

template <bool has_missing, bool has_categorical>
bst_node_t GetLeafIndex(RegTree const &tree, const RegTree::FVec &feat,
RegTree::CategoricalSplitMatrix const& cats) {
bst_node_t nid = 0;
while (!tree[nid].IsLeaf()) {
unsigned split_index = tree[nid].SplitIndex();
auto fvalue = feat.GetFvalue(split_index);
nid = GetNextNode<has_missing, has_categorical>(
tree[nid], nid, fvalue, has_missing && feat.IsMissing(split_index), cats);
}
return nid;
}

bst_float PredValue(const SparsePage::Inst &inst,
const std::vector<std::unique_ptr<RegTree>> &trees,
const std::vector<int> &tree_info, int bst_group,
Expand All @@ -35,32 +50,59 @@ bst_float PredValue(const SparsePage::Inst &inst,
p_feats->Fill(inst);
for (size_t i = tree_begin; i < tree_end; ++i) {
if (tree_info[i] == bst_group) {
int tid = trees[i]->GetLeafIndex(*p_feats);
psum += (*trees[i])[tid].LeafValue();
auto const &tree = *trees[i];
bool has_categorical = tree.HasCategoricalSplit();

auto categories = common::Span<uint32_t const>{tree.GetSplitCategories()};
auto split_types = tree.GetSplitTypes();
auto categories_ptr =
common::Span<RegTree::Segment const>{tree.GetSplitCategoriesPtr()};
auto cats = tree.GetCategoriesMatrix();
bst_node_t nidx = -1;
if (has_categorical) {
nidx = GetLeafIndex<true, true>(tree, *p_feats, cats);
} else {
nidx = GetLeafIndex<true, false>(tree, *p_feats, cats);
}
psum += (*trees[i])[nidx].LeafValue();
}
}
p_feats->Drop(inst);
return psum;
}

inline bst_float PredValueByOneTree(const RegTree::FVec& p_feats,
const std::unique_ptr<RegTree>& tree) {
const int lid = p_feats.HasMissing() ? tree->GetLeafIndex<true>(p_feats) :
tree->GetLeafIndex<false>(p_feats); // 35% speed up
return (*tree)[lid].LeafValue();
template <bool has_categorical>
bst_float
PredValueByOneTree(const RegTree::FVec &p_feats, RegTree const &tree,
RegTree::CategoricalSplitMatrix const& cats) {
const bst_node_t leaf = p_feats.HasMissing() ?
GetLeafIndex<true, has_categorical>(tree, p_feats, cats) :
GetLeafIndex<false, has_categorical>(tree, p_feats, cats);
return tree[leaf].LeafValue();
}

inline void PredictByAllTrees(gbm::GBTreeModel const &model, const size_t tree_begin,
const size_t tree_end, std::vector<bst_float>* out_preds,
const size_t predict_offset, const size_t num_group,
const std::vector<RegTree::FVec> &thread_temp,
const size_t offset, const size_t block_size) {
void PredictByAllTrees(gbm::GBTreeModel const &model, const size_t tree_begin,
const size_t tree_end, std::vector<bst_float> *out_preds,
const size_t predict_offset, const size_t num_group,
const std::vector<RegTree::FVec> &thread_temp,
const size_t offset, const size_t block_size) {
std::vector<bst_float> &preds = *out_preds;
for (size_t tree_id = tree_begin; tree_id < tree_end; ++tree_id) {
const size_t gid = model.tree_info[tree_id];
for (size_t i = 0; i < block_size; ++i) {
preds[(predict_offset + i) * num_group + gid] += PredValueByOneTree(thread_temp[offset + i],
model.trees[tree_id]);
auto const &tree = *model.trees[tree_id];
auto const& cats = tree.GetCategoriesMatrix();
auto has_categorical = tree.HasCategoricalSplit();

if (has_categorical) {
for (size_t i = 0; i < block_size; ++i) {
preds[(predict_offset + i) * num_group + gid] +=
PredValueByOneTree<true>(thread_temp[offset + i], tree, cats);
}
} else {
for (size_t i = 0; i < block_size; ++i) {
preds[(predict_offset + i) * num_group + gid] +=
PredValueByOneTree<false>(thread_temp[offset + i], tree, cats);
}
}
}
}
Expand All @@ -77,6 +119,7 @@ void FVecFill(const size_t block_size, const size_t batch_offset, const int num_
feats.Fill(inst);
}
}

template <typename DataView>
void FVecDrop(const size_t block_size, const size_t batch_offset, DataView* batch,
const size_t fvec_offset, std::vector<RegTree::FVec>* p_feats) {
Expand Down Expand Up @@ -145,28 +188,32 @@ class AdapterView {
};

template <typename DataView, size_t block_of_rows_size>
void PredictBatchByBlockOfRowsKernel(DataView batch, std::vector<bst_float> *out_preds,
gbm::GBTreeModel const &model, int32_t tree_begin,
int32_t tree_end,
std::vector<RegTree::FVec> *p_thread_temp) {
auto& thread_temp = *p_thread_temp;
void PredictBatchByBlockOfRowsKernel(
DataView batch, std::vector<bst_float> *out_preds,
gbm::GBTreeModel const &model, int32_t tree_begin, int32_t tree_end,
std::vector<RegTree::FVec> *p_thread_temp) {
auto &thread_temp = *p_thread_temp;
int32_t const num_group = model.learner_model_param->num_output_group;

CHECK_EQ(model.param.size_leaf_vector, 0)
<< "size_leaf_vector is enforced to 0 so far";
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
const int num_feature = model.learner_model_param->num_feature;
const bst_omp_uint n_row_blocks = (nsize) / block_of_rows_size + !!((nsize) % block_of_rows_size);
common::ParallelFor(n_row_blocks, [&](bst_omp_uint block_id) {
omp_ulong n_blocks = common::DivRoundUp(nsize, block_of_rows_size);

common::ParallelFor(n_blocks, [&](bst_omp_uint block_id) {
const size_t batch_offset = block_id * block_of_rows_size;
const size_t block_size = std::min(nsize - batch_offset, block_of_rows_size);
const size_t block_size =
std::min(nsize - batch_offset, block_of_rows_size);
const size_t fvec_offset = omp_get_thread_num() * block_of_rows_size;

FVecFill(block_size, batch_offset, num_feature, &batch, fvec_offset, p_thread_temp);
FVecFill(block_size, batch_offset, num_feature, &batch, fvec_offset,
p_thread_temp);
// process block of rows through all trees to keep cache locality
PredictByAllTrees(model, tree_begin, tree_end, out_preds, batch_offset + batch.base_rowid,
num_group, thread_temp, fvec_offset, block_size);
PredictByAllTrees(model, tree_begin, tree_end, out_preds,
batch_offset + batch.base_rowid, num_group, thread_temp,
fvec_offset, block_size);
FVecDrop(block_size, batch_offset, &batch, fvec_offset, p_thread_temp);
});
}
Expand Down Expand Up @@ -344,7 +391,9 @@ class CPUPredictor : public Predictor {
}
feats.Fill(page[i]);
for (unsigned j = 0; j < ntree_limit; ++j) {
int tid = model.trees[j]->GetLeafIndex(feats);
auto const& tree = *model.trees[j];
auto const& cats = tree.GetCategoriesMatrix();
bst_node_t tid = GetLeafIndex<true, true>(tree, feats, cats);
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Should we also add a check for has_categorical here? Or are we purposefully removing it as GetLeaf is not performance critical?

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also it's possible feats has no missing values

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Yes, I'm assuming it's not critical. Otherwise we will have a lot more specializations

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This is the previous default so I'm not slowing it down here. If optimization is needed (being the computation bottleneck of some algorithms/models), we can come back to it in a different PR that focuses on optimization.

preds[ridx * ntree_limit + j] = static_cast<bst_float>(tid);
}
feats.Drop(page[i]);
Expand Down