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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.
This commit is contained in:
Jiaming Yuan
2023-03-13 20:55:10 +08:00
committed by GitHub
parent 5ba3509dd3
commit 9bade7203a
14 changed files with 149 additions and 159 deletions
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4
src/gbm/gbtree.cc
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@@ -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) {

2
src/learner.cc
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@@ -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) {

2
src/predictor/cpu_predictor.cc
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@@ -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;
}

78
src/tree/tree_model.cc
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@@ -815,9 +815,9 @@ void RegTree::ExpandNode(bst_node_t nidx, bst_feature_t split_index, float split
linalg::VectorView<float const> left_weight,
linalg::VectorView<float const> right_weight) {
CHECK(IsMultiTarget());
CHECK_LT(split_index, this->param.num_feature);
CHECK_LT(split_index, this->param_.num_feature);
CHECK(this->p_mt_tree_);
CHECK_GT(param.size_leaf_vector, 1);
CHECK_GT(param_.size_leaf_vector, 1);
this->p_mt_tree_->Expand(nidx, split_index, split_cond, default_left, base_weight, left_weight,
right_weight);
@@ -826,7 +826,7 @@ void RegTree::ExpandNode(bst_node_t nidx, bst_feature_t split_index, float split
split_categories_segments_.resize(this->Size());
this->split_types_.at(nidx) = FeatureType::kNumerical;
this->param.num_nodes = this->p_mt_tree_->Size();
this->param_.num_nodes = this->p_mt_tree_->Size();
}
void RegTree::ExpandCategorical(bst_node_t nid, bst_feature_t split_index,
@@ -850,13 +850,13 @@ void RegTree::ExpandCategorical(bst_node_t nid, bst_feature_t split_index,
}
void RegTree::Load(dmlc::Stream* fi) {
CHECK_EQ(fi->Read(&param, sizeof(TreeParam)), sizeof(TreeParam));
CHECK_EQ(fi->Read(&param_, sizeof(TreeParam)), sizeof(TreeParam));
if (!DMLC_IO_NO_ENDIAN_SWAP) {
param = param.ByteSwap();
param_ = param_.ByteSwap();
}
nodes_.resize(param.num_nodes);
stats_.resize(param.num_nodes);
CHECK_NE(param.num_nodes, 0);
nodes_.resize(param_.num_nodes);
stats_.resize(param_.num_nodes);
CHECK_NE(param_.num_nodes, 0);
CHECK_EQ(fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()),
sizeof(Node) * nodes_.size());
if (!DMLC_IO_NO_ENDIAN_SWAP) {
@@ -873,29 +873,29 @@ void RegTree::Load(dmlc::Stream* fi) {
}
// chg deleted nodes
deleted_nodes_.resize(0);
for (int i = 1; i < param.num_nodes; ++i) {
for (int i = 1; i < param_.num_nodes; ++i) {
if (nodes_[i].IsDeleted()) {
deleted_nodes_.push_back(i);
}
}
CHECK_EQ(static_cast<int>(deleted_nodes_.size()), param.num_deleted);
CHECK_EQ(static_cast<int>(deleted_nodes_.size()), param_.num_deleted);
split_types_.resize(param.num_nodes, FeatureType::kNumerical);
split_categories_segments_.resize(param.num_nodes);
split_types_.resize(param_.num_nodes, FeatureType::kNumerical);
split_categories_segments_.resize(param_.num_nodes);
}
void RegTree::Save(dmlc::Stream* fo) const {
CHECK_EQ(param.num_nodes, static_cast<int>(nodes_.size()));
CHECK_EQ(param.num_nodes, static_cast<int>(stats_.size()));
CHECK_EQ(param.deprecated_num_roots, 1);
CHECK_NE(param.num_nodes, 0);
CHECK_EQ(param_.num_nodes, static_cast<int>(nodes_.size()));
CHECK_EQ(param_.num_nodes, static_cast<int>(stats_.size()));
CHECK_EQ(param_.deprecated_num_roots, 1);
CHECK_NE(param_.num_nodes, 0);
CHECK(!HasCategoricalSplit())
<< "Please use JSON/UBJSON for saving models with categorical splits.";
if (DMLC_IO_NO_ENDIAN_SWAP) {
fo->Write(&param, sizeof(TreeParam));
fo->Write(&param_, sizeof(TreeParam));
} else {
TreeParam x = param.ByteSwap();
TreeParam x = param_.ByteSwap();
fo->Write(&x, sizeof(x));
}
@@ -1081,7 +1081,7 @@ void RegTree::LoadModel(Json const& in) {
bool typed = IsA<I32Array>(in[tf::kParent]);
auto const& in_obj = get<Object const>(in);
// basic properties
FromJson(in["tree_param"], &param);
FromJson(in["tree_param"], &param_);
// categorical splits
bool has_cat = in_obj.find("split_type") != in_obj.cend();
if (has_cat) {
@@ -1092,55 +1092,55 @@ void RegTree::LoadModel(Json const& in) {
}
}
// multi-target
if (param.size_leaf_vector > 1) {
this->p_mt_tree_.reset(new MultiTargetTree{&param});
if (param_.size_leaf_vector > 1) {
this->p_mt_tree_.reset(new MultiTargetTree{&param_});
this->GetMultiTargetTree()->LoadModel(in);
return;
}
bool feature_is_64 = IsA<I64Array>(in["split_indices"]);
if (typed && feature_is_64) {
LoadModelImpl<true, true>(in, param, &stats_, &nodes_);
LoadModelImpl<true, true>(in, param_, &stats_, &nodes_);
} else if (typed && !feature_is_64) {
LoadModelImpl<true, false>(in, param, &stats_, &nodes_);
LoadModelImpl<true, false>(in, param_, &stats_, &nodes_);
} else if (!typed && feature_is_64) {
LoadModelImpl<false, true>(in, param, &stats_, &nodes_);
LoadModelImpl<false, true>(in, param_, &stats_, &nodes_);
} else {
LoadModelImpl<false, false>(in, param, &stats_, &nodes_);
LoadModelImpl<false, false>(in, param_, &stats_, &nodes_);
}
if (!has_cat) {
this->split_categories_segments_.resize(this->param.num_nodes);
this->split_types_.resize(this->param.num_nodes);
this->split_categories_segments_.resize(this->param_.num_nodes);
this->split_types_.resize(this->param_.num_nodes);
std::fill(split_types_.begin(), split_types_.end(), FeatureType::kNumerical);
}
deleted_nodes_.clear();
for (bst_node_t i = 1; i < param.num_nodes; ++i) {
for (bst_node_t i = 1; i < param_.num_nodes; ++i) {
if (nodes_[i].IsDeleted()) {
deleted_nodes_.push_back(i);
}
}
// easier access to [] operator
auto& self = *this;
for (auto nid = 1; nid < param.num_nodes; ++nid) {
for (auto nid = 1; nid < param_.num_nodes; ++nid) {
auto parent = self[nid].Parent();
CHECK_NE(parent, RegTree::kInvalidNodeId);
self[nid].SetParent(self[nid].Parent(), self[parent].LeftChild() == nid);
}
CHECK_EQ(static_cast<bst_node_t>(deleted_nodes_.size()), param.num_deleted);
CHECK_EQ(this->split_categories_segments_.size(), param.num_nodes);
CHECK_EQ(static_cast<bst_node_t>(deleted_nodes_.size()), param_.num_deleted);
CHECK_EQ(this->split_categories_segments_.size(), param_.num_nodes);
}
void RegTree::SaveModel(Json* p_out) const {
auto& out = *p_out;
// basic properties
out["tree_param"] = ToJson(param);
out["tree_param"] = ToJson(param_);
// categorical splits
this->SaveCategoricalSplit(p_out);
// multi-target
if (this->IsMultiTarget()) {
CHECK_GT(param.size_leaf_vector, 1);
CHECK_GT(param_.size_leaf_vector, 1);
this->GetMultiTargetTree()->SaveModel(p_out);
return;
}
@@ -1150,11 +1150,11 @@ void RegTree::SaveModel(Json* p_out) const {
* pruner, and this pruner can be used inside another updater so leaf are not necessary
* at the end of node array.
*/
CHECK_EQ(param.num_nodes, static_cast<int>(nodes_.size()));
CHECK_EQ(param.num_nodes, static_cast<int>(stats_.size()));
CHECK_EQ(param_.num_nodes, static_cast<int>(nodes_.size()));
CHECK_EQ(param_.num_nodes, static_cast<int>(stats_.size()));
CHECK_EQ(get<String>(out["tree_param"]["num_nodes"]), std::to_string(param.num_nodes));
auto n_nodes = param.num_nodes;
CHECK_EQ(get<String>(out["tree_param"]["num_nodes"]), std::to_string(param_.num_nodes));
auto n_nodes = param_.num_nodes;
// stats
F32Array loss_changes(n_nodes);
@@ -1168,7 +1168,7 @@ void RegTree::SaveModel(Json* p_out) const {
F32Array conds(n_nodes);
U8Array default_left(n_nodes);
CHECK_EQ(this->split_types_.size(), param.num_nodes);
CHECK_EQ(this->split_types_.size(), param_.num_nodes);
namespace tf = tree_field;
@@ -1189,7 +1189,7 @@ void RegTree::SaveModel(Json* p_out) const {
default_left.Set(i, static_cast<uint8_t>(!!n.DefaultLeft()));
}
};
if (this->param.num_feature > static_cast<bst_feature_t>(std::numeric_limits<int32_t>::max())) {
if (this->param_.num_feature > static_cast<bst_feature_t>(std::numeric_limits<int32_t>::max())) {
I64Array indices_64(n_nodes);
save_tree(&indices_64);
out[tf::kSplitIdx] = std::move(indices_64);

6
src/tree/updater_colmaker.cc
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@@ -190,7 +190,7 @@ class ColMaker: public TreeUpdater {
(*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
}
// remember auxiliary statistics in the tree node
for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
for (int nid = 0; nid < p_tree->NumNodes(); ++nid) {
p_tree->Stat(nid).loss_chg = snode_[nid].best.loss_chg;
p_tree->Stat(nid).base_weight = snode_[nid].weight;
p_tree->Stat(nid).sum_hess = static_cast<float>(snode_[nid].stats.sum_hess);
@@ -255,9 +255,9 @@ class ColMaker: public TreeUpdater {
{
// setup statistics space for each tree node
for (auto& i : stemp_) {
i.resize(tree.param.num_nodes, ThreadEntry());
i.resize(tree.NumNodes(), ThreadEntry());
}
snode_.resize(tree.param.num_nodes, NodeEntry());
snode_.resize(tree.NumNodes(), NodeEntry());
}
const MetaInfo& info = fmat.Info();
// setup position

2
src/tree/updater_prune.cc
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@@ -72,7 +72,7 @@ class TreePruner : public TreeUpdater {
void DoPrune(TrainParam const* param, RegTree* p_tree) {
auto& tree = *p_tree;
bst_node_t npruned = 0;
for (int nid = 0; nid < tree.param.num_nodes; ++nid) {
for (int nid = 0; nid < tree.NumNodes(); ++nid) {
if (tree[nid].IsLeaf() && !tree[nid].IsDeleted()) {
npruned = this->TryPruneLeaf(param, p_tree, nid, tree.GetDepth(nid), npruned);
}

8
src/tree/updater_refresh.cc
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@@ -50,11 +50,11 @@ class TreeRefresher : public TreeUpdater {
int tid = omp_get_thread_num();
int num_nodes = 0;
for (auto tree : trees) {
num_nodes += tree->param.num_nodes;
num_nodes += tree->NumNodes();
}
stemp[tid].resize(num_nodes, GradStats());
std::fill(stemp[tid].begin(), stemp[tid].end(), GradStats());
fvec_temp[tid].Init(trees[0]->param.num_feature);
fvec_temp[tid].Init(trees[0]->NumFeatures());
});
}
exc.Rethrow();
@@ -77,7 +77,7 @@ class TreeRefresher : public TreeUpdater {
for (auto tree : trees) {
AddStats(*tree, feats, gpair_h, info, ridx,
dmlc::BeginPtr(stemp[tid]) + offset);
offset += tree->param.num_nodes;
offset += tree->NumNodes();
}
feats.Drop(inst);
});
@@ -96,7 +96,7 @@ class TreeRefresher : public TreeUpdater {
int offset = 0;
for (auto tree : trees) {
this->Refresh(param, dmlc::BeginPtr(stemp[0]) + offset, 0, tree);
offset += tree->param.num_nodes;
offset += tree->NumNodes();
}
}