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Small refactor to categoricals (#7858)

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This commit is contained in:
Rory Mitchell
2022-05-05 17:47:02 +02:00
committed by GitHub
parent 14ef38b834
commit 7ef54e39ec
7 changed files with 110 additions and 150 deletions
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32
src/tree/gpu_hist/evaluate_splits.cuh
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@@ -58,9 +58,12 @@ class GPUHistEvaluator {
dh::device_vector<bst_feature_t> feature_idx_;
// Training param used for evaluation
TrainParam param_;
// whether the input data requires sort based split, which is more complicated so we try
// to avoid it if possible.
bool has_sort_{false};
// Do we have any categorical features that require sorting histograms?
// use this to skip the expensive sort step
bool need_sort_histogram_ = false;
// Number of elements of categorical storage type
// needed to hold categoricals for a single mode
std::size_t node_categorical_storage_size_ = 0;
// Copy the categories from device to host asynchronously.
void CopyToHost(EvaluateSplitInputs<GradientSumT> const &input, common::Span<CatST> cats_out);
@@ -69,12 +72,17 @@ class GPUHistEvaluator {
* \brief Get host category storage of nidx for internal calculation.
*/
auto HostCatStorage(bst_node_t nidx) {
auto cat_bits = h_split_cats_.size() / param_.MaxNodes();
std::size_t min_size=(nidx+2)*node_categorical_storage_size_;
if(h_split_cats_.size()<min_size){
h_split_cats_.resize(min_size);
}
if (nidx == RegTree::kRoot) {
auto cats_out = common::Span<CatST>{h_split_cats_}.subspan(nidx * cat_bits, cat_bits);
auto cats_out = common::Span<CatST>{h_split_cats_}.subspan(nidx * node_categorical_storage_size_, node_categorical_storage_size_);
return cats_out;
}
auto cats_out = common::Span<CatST>{h_split_cats_}.subspan(nidx * cat_bits, cat_bits * 2);
auto cats_out = common::Span<CatST>{h_split_cats_}.subspan(nidx * node_categorical_storage_size_, node_categorical_storage_size_ * 2);
return cats_out;
}
@@ -82,12 +90,15 @@ class GPUHistEvaluator {
* \brief Get device category storage of nidx for internal calculation.
*/
auto DeviceCatStorage(bst_node_t nidx) {
auto cat_bits = split_cats_.size() / param_.MaxNodes();
std::size_t min_size=(nidx+2)*node_categorical_storage_size_;
if(split_cats_.size()<min_size){
split_cats_.resize(min_size);
}
if (nidx == RegTree::kRoot) {
auto cats_out = dh::ToSpan(split_cats_).subspan(nidx * cat_bits, cat_bits);
auto cats_out = dh::ToSpan(split_cats_).subspan(nidx * node_categorical_storage_size_, node_categorical_storage_size_);
return cats_out;
}
auto cats_out = dh::ToSpan(split_cats_).subspan(nidx * cat_bits, cat_bits * 2);
auto cats_out = dh::ToSpan(split_cats_).subspan(nidx * node_categorical_storage_size_, node_categorical_storage_size_ * 2);
return cats_out;
}
@@ -123,8 +134,7 @@ class GPUHistEvaluator {
*/
common::Span<CatST const> GetHostNodeCats(bst_node_t nidx) const {
copy_stream_.View().Sync();
auto cat_bits = h_split_cats_.size() / param_.MaxNodes();
auto cats_out = common::Span<CatST const>{h_split_cats_}.subspan(nidx * cat_bits, cat_bits);
auto cats_out = common::Span<CatST const>{h_split_cats_}.subspan(nidx * node_categorical_storage_size_, node_categorical_storage_size_);
return cats_out;
}
/**