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Require leaf statistics when expanding tree (#4015)

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* Cache left and right gradient sums

* Require leaf statistics when expanding tree
This commit is contained in:
Rory Mitchell
2019-01-18 07:12:20 +02:00
committed by Philip Hyunsu Cho
parent 0f8af85f64
commit 1fc37e4749
11 changed files with 143 additions and 85 deletions
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45
src/tree/updater_gpu_hist.cu
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@@ -1182,42 +1182,35 @@ class GPUHistMakerSpecialised{
}
void ApplySplit(const ExpandEntry& candidate, RegTree* p_tree) {
// Add new leaves
RegTree& tree = *p_tree;
tree.ExpandNode(candidate.nid, candidate.split.findex, candidate.split.fvalue,
candidate.split.dir == kLeftDir);
auto& parent = tree[candidate.nid];
tree.Stat(candidate.nid).loss_chg = candidate.split.loss_chg;
// Set up child constraints
node_value_constraints_.resize(tree.GetNodes().size());
GradStats left_stats(param_);
left_stats.Add(candidate.split.left_sum);
GradStats right_stats(param_);
right_stats.Add(candidate.split.right_sum);
node_value_constraints_[candidate.nid].SetChild(
param_, parent.SplitIndex(), left_stats, right_stats,
&node_value_constraints_[parent.LeftChild()],
&node_value_constraints_[parent.RightChild()]);
// Configure left child
GradStats parent_sum(param_);
parent_sum.Add(left_stats);
parent_sum.Add(right_stats);
node_value_constraints_.resize(tree.GetNodes().size());
auto base_weight = node_value_constraints_[candidate.nid].CalcWeight(param_, parent_sum);
auto left_weight =
node_value_constraints_[parent.LeftChild()].CalcWeight(param_, left_stats);
tree[parent.LeftChild()].SetLeaf(left_weight * param_.learning_rate, 0);
tree.Stat(parent.LeftChild()).base_weight = left_weight;
tree.Stat(parent.LeftChild()).sum_hess = candidate.split.left_sum.GetHess();
// Configure right child
node_value_constraints_[candidate.nid].CalcWeight(param_, left_stats)*param_.learning_rate;
auto right_weight =
node_value_constraints_[parent.RightChild()].CalcWeight(param_, right_stats);
tree[parent.RightChild()].SetLeaf(right_weight * param_.learning_rate, 0);
tree.Stat(parent.RightChild()).base_weight = right_weight;
tree.Stat(parent.RightChild()).sum_hess = candidate.split.right_sum.GetHess();
node_value_constraints_[candidate.nid].CalcWeight(param_, right_stats)*param_.learning_rate;
tree.ExpandNode(candidate.nid, candidate.split.findex,
candidate.split.fvalue, candidate.split.dir == kLeftDir,
base_weight, left_weight, right_weight,
candidate.split.loss_chg, parent_sum.sum_hess);
// Set up child constraints
node_value_constraints_.resize(tree.GetNodes().size());
node_value_constraints_[candidate.nid].SetChild(
param_, tree[candidate.nid].SplitIndex(), left_stats, right_stats,
&node_value_constraints_[tree[candidate.nid].LeftChild()],
&node_value_constraints_[tree[candidate.nid].RightChild()]);
// Store sum gradients
for (auto& shard : shards_) {
shard->node_sum_gradients[parent.LeftChild()] = candidate.split.left_sum;
shard->node_sum_gradients[parent.RightChild()] = candidate.split.right_sum;
shard->node_sum_gradients[tree[candidate.nid].LeftChild()] = candidate.split.left_sum;
shard->node_sum_gradients[tree[candidate.nid].RightChild()] = candidate.split.right_sum;
}
}