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Remove public access to tree model param. (#8902)
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* Make tree model param a private member.
* Number of features and targets are immutable after construction.

This is to reduce the number of places where we can run configuration.
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@trivialfis
trivialfis authored Mar 13, 2023
1 parent 5ba3509 commit 9bade72
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Showing 14 changed files with 149 additions and 159 deletions.
146 changes: 75 additions & 71 deletions include/xgboost/tree_model.h
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Original file line number Diff line number Diff line change
Expand Up @@ -178,51 +178,33 @@ class RegTree : public Model {
}

/*! \brief index of left child */
XGBOOST_DEVICE [[nodiscard]] int LeftChild() const {
return this->cleft_;
}
[[nodiscard]] XGBOOST_DEVICE int LeftChild() const { return this->cleft_; }
/*! \brief index of right child */
XGBOOST_DEVICE [[nodiscard]] int RightChild() const {
return this->cright_;
}
[[nodiscard]] XGBOOST_DEVICE int RightChild() const { return this->cright_; }
/*! \brief index of default child when feature is missing */
XGBOOST_DEVICE [[nodiscard]] int DefaultChild() const {
[[nodiscard]] XGBOOST_DEVICE int DefaultChild() const {
return this->DefaultLeft() ? this->LeftChild() : this->RightChild();
}
/*! \brief feature index of split condition */
XGBOOST_DEVICE [[nodiscard]] unsigned SplitIndex() const {
[[nodiscard]] XGBOOST_DEVICE unsigned SplitIndex() const {
return sindex_ & ((1U << 31) - 1U);
}
/*! \brief when feature is unknown, whether goes to left child */
XGBOOST_DEVICE [[nodiscard]] bool DefaultLeft() const {
return (sindex_ >> 31) != 0;
}
[[nodiscard]] XGBOOST_DEVICE bool DefaultLeft() const { return (sindex_ >> 31) != 0; }
/*! \brief whether current node is leaf node */
XGBOOST_DEVICE [[nodiscard]] bool IsLeaf() const {
return cleft_ == kInvalidNodeId;
}
[[nodiscard]] XGBOOST_DEVICE bool IsLeaf() const { return cleft_ == kInvalidNodeId; }
/*! \return get leaf value of leaf node */
XGBOOST_DEVICE [[nodiscard]] float LeafValue() const {
return (this->info_).leaf_value;
}
[[nodiscard]] XGBOOST_DEVICE float LeafValue() const { return (this->info_).leaf_value; }
/*! \return get split condition of the node */
XGBOOST_DEVICE [[nodiscard]] SplitCondT SplitCond() const {
return (this->info_).split_cond;
}
[[nodiscard]] XGBOOST_DEVICE SplitCondT SplitCond() const { return (this->info_).split_cond; }
/*! \brief get parent of the node */
XGBOOST_DEVICE [[nodiscard]] int Parent() const {
return parent_ & ((1U << 31) - 1);
}
[[nodiscard]] XGBOOST_DEVICE int Parent() const { return parent_ & ((1U << 31) - 1); }
/*! \brief whether current node is left child */
XGBOOST_DEVICE [[nodiscard]] bool IsLeftChild() const {
return (parent_ & (1U << 31)) != 0;
}
[[nodiscard]] XGBOOST_DEVICE bool IsLeftChild() const { return (parent_ & (1U << 31)) != 0; }
/*! \brief whether this node is deleted */
XGBOOST_DEVICE [[nodiscard]] bool IsDeleted() const {
return sindex_ == kDeletedNodeMarker;
}
[[nodiscard]] XGBOOST_DEVICE bool IsDeleted() const { return sindex_ == kDeletedNodeMarker; }
/*! \brief whether current node is root */
XGBOOST_DEVICE [[nodiscard]] bool IsRoot() const { return parent_ == kInvalidNodeId; }
[[nodiscard]] XGBOOST_DEVICE bool IsRoot() const { return parent_ == kInvalidNodeId; }
/*!
* \brief set the left child
* \param nid node id to right child
Expand Down Expand Up @@ -337,15 +319,13 @@ class RegTree : public Model {
this->ChangeToLeaf(rid, value);
}

/*! \brief model parameter */
TreeParam param;
RegTree() {
param.Init(Args{});
nodes_.resize(param.num_nodes);
stats_.resize(param.num_nodes);
split_types_.resize(param.num_nodes, FeatureType::kNumerical);
split_categories_segments_.resize(param.num_nodes);
for (int i = 0; i < param.num_nodes; i++) {
param_.Init(Args{});
nodes_.resize(param_.num_nodes);
stats_.resize(param_.num_nodes);
split_types_.resize(param_.num_nodes, FeatureType::kNumerical);
split_categories_segments_.resize(param_.num_nodes);
for (int i = 0; i < param_.num_nodes; i++) {
nodes_[i].SetLeaf(0.0f);
nodes_[i].SetParent(kInvalidNodeId);
}
Expand All @@ -354,10 +334,10 @@ class RegTree : public Model {
* \brief Constructor that initializes the tree model with shape.
*/
explicit RegTree(bst_target_t n_targets, bst_feature_t n_features) : RegTree{} {
param.num_feature = n_features;
param.size_leaf_vector = n_targets;
param_.num_feature = n_features;
param_.size_leaf_vector = n_targets;
if (n_targets > 1) {
this->p_mt_tree_.reset(new MultiTargetTree{&param});
this->p_mt_tree_.reset(new MultiTargetTree{&param_});
}
}

Expand Down Expand Up @@ -401,7 +381,7 @@ class RegTree : public Model {

bool operator==(const RegTree& b) const {
return nodes_ == b.nodes_ && stats_ == b.stats_ &&
deleted_nodes_ == b.deleted_nodes_ && param == b.param;
deleted_nodes_ == b.deleted_nodes_ && param_ == b.param_;
}
/* \brief Iterate through all nodes in this tree.
*
Expand Down Expand Up @@ -459,7 +439,9 @@ class RegTree : public Model {
bst_float loss_change, float sum_hess, float left_sum,
float right_sum,
bst_node_t leaf_right_child = kInvalidNodeId);

/**
* \brief Expands a leaf node into two additional leaf nodes for a multi-target tree.
*/
void ExpandNode(bst_node_t nidx, bst_feature_t split_index, float split_cond, bool default_left,
linalg::VectorView<float const> base_weight,
linalg::VectorView<float const> left_weight,
Expand All @@ -485,19 +467,48 @@ class RegTree : public Model {
bst_float base_weight, bst_float left_leaf_weight,
bst_float right_leaf_weight, bst_float loss_change, float sum_hess,
float left_sum, float right_sum);

[[nodiscard]] bool HasCategoricalSplit() const {
return !split_categories_.empty();
}
/**
* \brief Whether this tree has categorical split.
*/
[[nodiscard]] bool HasCategoricalSplit() const { return !split_categories_.empty(); }
/**
* \brief Whether this is a multi-target tree.
*/
[[nodiscard]] bool IsMultiTarget() const { return static_cast<bool>(p_mt_tree_); }
[[nodiscard]] bst_target_t NumTargets() const { return param.size_leaf_vector; }
/**
* \brief The size of leaf weight.
*/
[[nodiscard]] bst_target_t NumTargets() const { return param_.size_leaf_vector; }
/**
* \brief Get the underlying implementaiton of multi-target tree.
*/
[[nodiscard]] auto GetMultiTargetTree() const {
CHECK(IsMultiTarget());
return p_mt_tree_.get();
}
/**
* \brief Get the number of features.
*/
[[nodiscard]] bst_feature_t NumFeatures() const noexcept { return param_.num_feature; }
/**
* \brief Get the total number of nodes including deleted ones in this tree.
*/
[[nodiscard]] bst_node_t NumNodes() const noexcept { return param_.num_nodes; }
/**
* \brief Get the total number of valid nodes in this tree.
*/
[[nodiscard]] bst_node_t NumValidNodes() const noexcept {
return param_.num_nodes - param_.num_deleted;
}
/**
* \brief number of extra nodes besides the root
*/
[[nodiscard]] bst_node_t NumExtraNodes() const noexcept {
return param_.num_nodes - 1 - param_.num_deleted;
}
/* \brief Count number of leaves in tree. */
[[nodiscard]] bst_node_t GetNumLeaves() const;
[[nodiscard]] bst_node_t GetNumSplitNodes() const;

/*!
* \brief get current depth
Expand All @@ -514,6 +525,9 @@ class RegTree : public Model {
}
return depth;
}
/**
* \brief Set the leaf weight for a multi-target tree.
*/
void SetLeaf(bst_node_t nidx, linalg::VectorView<float const> weight) {
CHECK(IsMultiTarget());
return this->p_mt_tree_->SetLeaf(nidx, weight);
Expand All @@ -525,25 +539,13 @@ class RegTree : public Model {
*/
[[nodiscard]] int MaxDepth(int nid) const {
if (nodes_[nid].IsLeaf()) return 0;
return std::max(MaxDepth(nodes_[nid].LeftChild())+1,
MaxDepth(nodes_[nid].RightChild())+1);
return std::max(MaxDepth(nodes_[nid].LeftChild()) + 1, MaxDepth(nodes_[nid].RightChild()) + 1);
}

/*!
* \brief get maximum depth
*/
int MaxDepth() {
return MaxDepth(0);
}

/*! \brief number of extra nodes besides the root */
[[nodiscard]] int NumExtraNodes() const {
return param.num_nodes - 1 - param.num_deleted;
}

/* \brief Count number of leaves in tree. */
[[nodiscard]] bst_node_t GetNumLeaves() const;
[[nodiscard]] bst_node_t GetNumSplitNodes() const;
int MaxDepth() { return MaxDepth(0); }

/*!
* \brief dense feature vector that can be taken by RegTree
Expand Down Expand Up @@ -735,6 +737,8 @@ class RegTree : public Model {
template <bool typed>
void LoadCategoricalSplit(Json const& in);
void SaveCategoricalSplit(Json* p_out) const;
/*! \brief model parameter */
TreeParam param_;
// vector of nodes
std::vector<Node> nodes_;
// free node space, used during training process
Expand All @@ -752,20 +756,20 @@ class RegTree : public Model {
// allocate a new node,
// !!!!!! NOTE: may cause BUG here, nodes.resize
bst_node_t AllocNode() {
if (param.num_deleted != 0) {
if (param_.num_deleted != 0) {
int nid = deleted_nodes_.back();
deleted_nodes_.pop_back();
nodes_[nid].Reuse();
--param.num_deleted;
--param_.num_deleted;
return nid;
}
int nd = param.num_nodes++;
CHECK_LT(param.num_nodes, std::numeric_limits<int>::max())
int nd = param_.num_nodes++;
CHECK_LT(param_.num_nodes, std::numeric_limits<int>::max())
<< "number of nodes in the tree exceed 2^31";
nodes_.resize(param.num_nodes);
stats_.resize(param.num_nodes);
split_types_.resize(param.num_nodes, FeatureType::kNumerical);
split_categories_segments_.resize(param.num_nodes);
nodes_.resize(param_.num_nodes);
stats_.resize(param_.num_nodes);
split_types_.resize(param_.num_nodes, FeatureType::kNumerical);
split_categories_segments_.resize(param_.num_nodes);
return nd;
}
// delete a tree node, keep the parent field to allow trace back
Expand All @@ -780,7 +784,7 @@ class RegTree : public Model {

deleted_nodes_.push_back(nid);
nodes_[nid].MarkDelete();
++param.num_deleted;
++param_.num_deleted;
}
};

Expand Down
4 changes: 2 additions & 2 deletions src/gbm/gbtree.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -360,8 +360,8 @@ void GBTree::BoostNewTrees(HostDeviceVector<GradientPair>* gpair, DMatrix* p_fma
<< "Set `process_type` to `update` if you want to update existing "
"trees.";
// create new tree
std::unique_ptr<RegTree> ptr(new RegTree());
ptr->param.UpdateAllowUnknown(this->cfg_);
std::unique_ptr<RegTree> ptr(new RegTree{this->model_.learner_model_param->LeafLength(),
this->model_.learner_model_param->num_feature});
new_trees.push_back(ptr.get());
ret->push_back(std::move(ptr));
} else if (tparam_.process_type == TreeProcessType::kUpdate) {
Expand Down
2 changes: 0 additions & 2 deletions src/learner.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -775,8 +775,6 @@ class LearnerConfiguration : public Learner {
}
CHECK_NE(mparam_.num_feature, 0)
<< "0 feature is supplied. Are you using raw Booster interface?";
// Remove these once binary IO is gone.
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
}

void ConfigureGBM(LearnerTrainParam const& old, Args const& args) {
Expand Down
2 changes: 1 addition & 1 deletion src/predictor/cpu_predictor.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -275,7 +275,7 @@ float FillNodeMeanValues(RegTree const *tree, bst_node_t nidx, std::vector<float
}

void FillNodeMeanValues(RegTree const* tree, std::vector<float>* mean_values) {
size_t num_nodes = tree->param.num_nodes;
size_t num_nodes = tree->NumNodes();
if (mean_values->size() == num_nodes) {
return;
}
Expand Down
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