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Fix GPU categorical split memory allocation. (#9529)

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Jiaming Yuan 2023-08-29 10:06:03 +08:00 committed by GitHub
parent be6a552956
commit 942b957eef
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5 changed files with 67 additions and 64 deletions
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2
src/common/categorical.h
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@ -52,7 +52,7 @@ inline XGBOOST_DEVICE bool InvalidCat(float cat) {
*
* Go to left if it's NOT the matching category, which matches one-hot encoding.
*/
inline XGBOOST_DEVICE bool Decision(common::Span<uint32_t const> cats, float cat) {
inline XGBOOST_DEVICE bool Decision(common::Span<CatBitField::value_type const> cats, float cat) {
KCatBitField const s_cats(cats);
if (XGBOOST_EXPECT(InvalidCat(cat), false)) {
return true;

21
src/tree/gpu_hist/evaluate_splits.cu
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@ -1,5 +1,5 @@
/*!
* Copyright 2020-2022 by XGBoost Contributors
/**
* Copyright 2020-2023, XGBoost Contributors
*/
#include <algorithm> // std::max
#include <vector>
@ -11,9 +11,7 @@
#include "evaluate_splits.cuh"
#include "expand_entry.cuh"
namespace xgboost {
namespace tree {
namespace xgboost::tree {
// With constraints
XGBOOST_DEVICE float LossChangeMissing(const GradientPairInt64 &scan,
const GradientPairInt64 &missing,
@ -315,11 +313,11 @@ __device__ void SetCategoricalSplit(const EvaluateSplitSharedInputs &shared_inpu
common::Span<common::CatBitField::value_type> out,
DeviceSplitCandidate *p_out_split) {
auto &out_split = *p_out_split;
out_split.split_cats = common::CatBitField{out};
auto out_cats = common::CatBitField{out};
// Simple case for one hot split
if (common::UseOneHot(shared_inputs.FeatureBins(fidx), shared_inputs.param.max_cat_to_onehot)) {
out_split.split_cats.Set(common::AsCat(out_split.thresh));
out_cats.Set(common::AsCat(out_split.thresh));
return;
}
@ -339,7 +337,7 @@ __device__ void SetCategoricalSplit(const EvaluateSplitSharedInputs &shared_inpu
assert(partition > 0 && "Invalid partition.");
thrust::for_each(thrust::seq, beg, beg + partition, [&](size_t c) {
auto cat = shared_inputs.feature_values[c - node_offset];
out_split.SetCat(cat);
out_cats.Set(common::AsCat(cat));
});
}
@ -444,8 +442,7 @@ void GPUHistEvaluator::EvaluateSplits(
if (split.is_cat) {
SetCategoricalSplit(shared_inputs, d_sorted_idx, fidx, i,
device_cats_accessor.GetNodeCatStorage(input.nidx),
&out_splits[i]);
device_cats_accessor.GetNodeCatStorage(input.nidx), &out_splits[i]);
}
float base_weight =
@ -477,6 +474,4 @@ GPUExpandEntry GPUHistEvaluator::EvaluateSingleSplit(
cudaMemcpyDeviceToHost));
return root_entry;
}
} // namespace tree
} // namespace xgboost
} // namespace xgboost::tree

22
src/tree/gpu_hist/evaluate_splits.cuh
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@ -37,8 +37,8 @@ struct EvaluateSplitSharedInputs {
common::Span<const float> feature_values;
common::Span<const float> min_fvalue;
bool is_dense;
XGBOOST_DEVICE auto Features() const { return feature_segments.size() - 1; }
__device__ auto FeatureBins(bst_feature_t fidx) const {
[[nodiscard]] XGBOOST_DEVICE auto Features() const { return feature_segments.size() - 1; }
[[nodiscard]] __device__ std::uint32_t FeatureBins(bst_feature_t fidx) const {
return feature_segments[fidx + 1] - feature_segments[fidx];
}
};
@ -105,7 +105,7 @@ class GPUHistEvaluator {
}
/**
* \brief Get device category storage of nidx for internal calculation.
* @brief Get device category storage of nidx for internal calculation.
*/
auto DeviceCatStorage(const std::vector<bst_node_t> &nidx) {
if (!has_categoricals_) return CatAccessor{};
@ -120,8 +120,8 @@ class GPUHistEvaluator {
/**
* \brief Get sorted index storage based on the left node of inputs.
*/
auto SortedIdx(int num_nodes, bst_feature_t total_bins) {
if(!need_sort_histogram_) return common::Span<bst_feature_t>();
auto SortedIdx(int num_nodes, bst_bin_t total_bins) {
if (!need_sort_histogram_) return common::Span<bst_feature_t>{};
cat_sorted_idx_.resize(num_nodes * total_bins);
return dh::ToSpan(cat_sorted_idx_);
}
@ -146,12 +146,22 @@ class GPUHistEvaluator {
* \brief Get host category storage for nidx. Different from the internal version, this
* returns strictly 1 node.
*/
common::Span<CatST const> GetHostNodeCats(bst_node_t nidx) const {
[[nodiscard]] common::Span<CatST const> GetHostNodeCats(bst_node_t nidx) const {
copy_stream_.View().Sync();
auto cats_out = common::Span<CatST const>{h_split_cats_}.subspan(
nidx * node_categorical_storage_size_, node_categorical_storage_size_);
return cats_out;
}
[[nodiscard]] auto GetDeviceNodeCats(bst_node_t nidx) {
copy_stream_.View().Sync();
if (has_categoricals_) {
CatAccessor accessor = {dh::ToSpan(split_cats_), node_categorical_storage_size_};
return common::KCatBitField{accessor.GetNodeCatStorage(nidx)};
} else {
return common::KCatBitField{};
}
}
/**
* \brief Add a split to the internal tree evaluator.
*/

12
src/tree/updater_gpu_common.cuh
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@ -64,7 +64,6 @@ struct DeviceSplitCandidate {
// split.
bst_cat_t thresh{-1};
common::CatBitField split_cats;
bool is_cat { false };
GradientPairInt64 left_sum;
@ -72,12 +71,6 @@ struct DeviceSplitCandidate {
XGBOOST_DEVICE DeviceSplitCandidate() {} // NOLINT
template <typename T>
XGBOOST_DEVICE void SetCat(T c) {
this->split_cats.Set(common::AsCat(c));
fvalue = std::max(this->fvalue, static_cast<float>(c));
}
XGBOOST_DEVICE void Update(float loss_chg_in, DefaultDirection dir_in, float fvalue_in,
int findex_in, GradientPairInt64 left_sum_in,
GradientPairInt64 right_sum_in, bool cat,
@ -100,7 +93,8 @@ struct DeviceSplitCandidate {
*/
XGBOOST_DEVICE void UpdateCat(float loss_chg_in, DefaultDirection dir_in, bst_cat_t thresh_in,
bst_feature_t findex_in, GradientPairInt64 left_sum_in,
GradientPairInt64 right_sum_in, GPUTrainingParam const& param, const GradientQuantiser& quantiser) {
GradientPairInt64 right_sum_in, GPUTrainingParam const& param,
const GradientQuantiser& quantiser) {
if (loss_chg_in > loss_chg &&
quantiser.ToFloatingPoint(left_sum_in).GetHess() >= param.min_child_weight &&
quantiser.ToFloatingPoint(right_sum_in).GetHess() >= param.min_child_weight) {
@ -115,7 +109,7 @@ struct DeviceSplitCandidate {
}
}
XGBOOST_DEVICE bool IsValid() const { return loss_chg > 0.0f; }
[[nodiscard]] XGBOOST_DEVICE bool IsValid() const { return loss_chg > 0.0f; }
friend std::ostream& operator<<(std::ostream& os, DeviceSplitCandidate const& c) {
os << "loss_chg:" << c.loss_chg << ", "

46
src/tree/updater_gpu_hist.cu
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@ -7,9 +7,9 @@
#include <algorithm>
#include <cmath>
#include <limits>
#include <memory>
#include <utility>
#include <cstddef> // for size_t
#include <memory> // for unique_ptr, make_unique
#include <utility> // for move
#include <vector>
#include "../collective/communicator-inl.cuh"
@ -216,9 +216,9 @@ struct GPUHistMakerDevice {
void InitFeatureGroupsOnce() {
if (!feature_groups) {
CHECK(page);
feature_groups.reset(new FeatureGroups(page->Cuts(), page->is_dense,
feature_groups = std::make_unique<FeatureGroups>(page->Cuts(), page->is_dense,
dh::MaxSharedMemoryOptin(ctx_->gpu_id),
sizeof(GradientPairPrecise)));
sizeof(GradientPairPrecise));
}
}
@ -245,10 +245,10 @@ struct GPUHistMakerDevice {
this->evaluator_.Reset(page->Cuts(), feature_types, dmat->Info().num_col_, param,
dmat->Info().IsColumnSplit(), ctx_->gpu_id);
quantiser.reset(new GradientQuantiser(this->gpair));
quantiser = std::make_unique<GradientQuantiser>(this->gpair);
row_partitioner.reset(); // Release the device memory first before reallocating
row_partitioner.reset(new RowPartitioner(ctx_->gpu_id, sample.sample_rows));
row_partitioner = std::make_unique<RowPartitioner>(ctx_->gpu_id, sample.sample_rows);
// Init histogram
hist.Init(ctx_->gpu_id, page->Cuts().TotalBins());
@ -295,7 +295,7 @@ struct GPUHistMakerDevice {
dh::TemporaryArray<GPUExpandEntry> entries(2 * candidates.size());
// Store the feature set ptrs so they dont go out of scope before the kernel is called
std::vector<std::shared_ptr<HostDeviceVector<bst_feature_t>>> feature_sets;
for (size_t i = 0; i < candidates.size(); i++) {
for (std::size_t i = 0; i < candidates.size(); i++) {
auto candidate = candidates.at(i);
int left_nidx = tree[candidate.nid].LeftChild();
int right_nidx = tree[candidate.nid].RightChild();
@ -328,9 +328,8 @@ struct GPUHistMakerDevice {
d_node_inputs.data().get(), h_node_inputs.data(),
h_node_inputs.size() * sizeof(EvaluateSplitInputs), cudaMemcpyDefault));
this->evaluator_.EvaluateSplits(nidx, max_active_features,
dh::ToSpan(d_node_inputs), shared_inputs,
dh::ToSpan(entries));
this->evaluator_.EvaluateSplits(nidx, max_active_features, dh::ToSpan(d_node_inputs),
shared_inputs, dh::ToSpan(entries));
dh::safe_cuda(cudaMemcpyAsync(pinned_candidates_out.data(),
entries.data().get(), sizeof(GPUExpandEntry) * entries.size(),
cudaMemcpyDeviceToHost));
@ -367,23 +366,29 @@ struct GPUHistMakerDevice {
struct NodeSplitData {
RegTree::Node split_node;
FeatureType split_type;
common::CatBitField node_cats;
common::KCatBitField node_cats;
};
void UpdatePosition(const std::vector<GPUExpandEntry>& candidates, RegTree* p_tree) {
if (candidates.empty()) return;
std::vector<int> nidx(candidates.size());
std::vector<int> left_nidx(candidates.size());
std::vector<int> right_nidx(candidates.size());
void UpdatePosition(std::vector<GPUExpandEntry> const& candidates, RegTree* p_tree) {
if (candidates.empty()) {
return;
}
std::vector<bst_node_t> nidx(candidates.size());
std::vector<bst_node_t> left_nidx(candidates.size());
std::vector<bst_node_t> right_nidx(candidates.size());
std::vector<NodeSplitData> split_data(candidates.size());
for (size_t i = 0; i < candidates.size(); i++) {
auto& e = candidates[i];
auto const& e = candidates[i];
RegTree::Node split_node = (*p_tree)[e.nid];
auto split_type = p_tree->NodeSplitType(e.nid);
nidx.at(i) = e.nid;
left_nidx.at(i) = split_node.LeftChild();
right_nidx.at(i) = split_node.RightChild();
split_data.at(i) = NodeSplitData{split_node, split_type, e.split.split_cats};
split_data.at(i) = NodeSplitData{split_node, split_type, evaluator_.GetDeviceNodeCats(e.nid)};
CHECK_EQ(split_type == FeatureType::kCategorical, e.split.is_cat);
}
auto d_matrix = page->GetDeviceAccessor(ctx_->gpu_id);
@ -391,7 +396,7 @@ struct GPUHistMakerDevice {
nidx, left_nidx, right_nidx, split_data,
[=] __device__(bst_uint ridx, const NodeSplitData& data) {
// given a row index, returns the node id it belongs to
bst_float cut_value = d_matrix.GetFvalue(ridx, data.split_node.SplitIndex());
float cut_value = d_matrix.GetFvalue(ridx, data.split_node.SplitIndex());
// Missing value
bool go_left = true;
if (isnan(cut_value)) {
@ -621,7 +626,6 @@ struct GPUHistMakerDevice {
CHECK(common::CheckNAN(candidate.split.fvalue));
std::vector<common::CatBitField::value_type> split_cats;
CHECK_GT(candidate.split.split_cats.Bits().size(), 0);
auto h_cats = this->evaluator_.GetHostNodeCats(candidate.nid);
auto n_bins_feature = page->Cuts().FeatureBins(candidate.split.findex);
split_cats.resize(common::CatBitField::ComputeStorageSize(n_bins_feature), 0);