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Implement column sampler in CUDA. (#9785)

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- CUDA implementation.
- Extract the broadcasting logic, we will need the context parameter after revamping the collective implementation.
- Some changes to the event loop for fixing a deadlock in CI.
- Move argsort into algorithms.cuh, add support for cuda stream.
This commit is contained in:
Jiaming Yuan
2023-11-17 04:29:08 +08:00
committed by GitHub
parent 178cfe70a8
commit fedd9674c8
20 changed files with 447 additions and 232 deletions
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12
tests/cpp/tree/hist/test_evaluate_splits.cc
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@@ -28,7 +28,7 @@ void TestEvaluateSplits(bool force_read_by_column) {
Context ctx;
ctx.nthread = 4;
int static constexpr kRows = 8, kCols = 16;
auto sampler = std::make_shared<common::ColumnSampler>();
auto sampler = std::make_shared<common::ColumnSampler>(1u);
TrainParam param;
param.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0"}});
@@ -102,7 +102,7 @@ TEST(HistMultiEvaluator, Evaluate) {
TrainParam param;
param.Init(Args{{"min_child_weight", "0"}, {"reg_lambda", "0"}});
auto sampler = std::make_shared<common::ColumnSampler>();
auto sampler = std::make_shared<common::ColumnSampler>(1u);
std::size_t n_samples = 3;
bst_feature_t n_features = 2;
@@ -166,7 +166,7 @@ TEST(HistEvaluator, Apply) {
TrainParam param;
param.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0.0"}});
auto dmat = RandomDataGenerator(kNRows, kNCols, 0).Seed(3).GenerateDMatrix();
auto sampler = std::make_shared<common::ColumnSampler>();
auto sampler = std::make_shared<common::ColumnSampler>(1u);
auto evaluator_ = HistEvaluator{&ctx, &param, dmat->Info(), sampler};
CPUExpandEntry entry{0, 0};
@@ -194,7 +194,7 @@ TEST_F(TestPartitionBasedSplit, CPUHist) {
Context ctx;
// check the evaluator is returning the optimal split
std::vector<FeatureType> ft{FeatureType::kCategorical};
auto sampler = std::make_shared<common::ColumnSampler>();
auto sampler = std::make_shared<common::ColumnSampler>(1u);
HistEvaluator evaluator{&ctx, &param_, info_, sampler};
evaluator.InitRoot(GradStats{total_gpair_});
RegTree tree;
@@ -224,7 +224,7 @@ auto CompareOneHotAndPartition(bool onehot) {
auto dmat =
RandomDataGenerator(kRows, kCols, 0).Seed(3).Type(ft).MaxCategory(n_cats).GenerateDMatrix();
auto sampler = std::make_shared<common::ColumnSampler>();
auto sampler = std::make_shared<common::ColumnSampler>(1u);
auto evaluator = HistEvaluator{&ctx, &param, dmat->Info(), sampler};
std::vector<CPUExpandEntry> entries(1);
HistMakerTrainParam hist_param;
@@ -271,7 +271,7 @@ TEST_F(TestCategoricalSplitWithMissing, HistEvaluator) {
ASSERT_EQ(node_hist.size(), feature_histogram_.size());
std::copy(feature_histogram_.cbegin(), feature_histogram_.cend(), node_hist.begin());
auto sampler = std::make_shared<common::ColumnSampler>();
auto sampler = std::make_shared<common::ColumnSampler>(1u);
MetaInfo info;
info.num_col_ = 1;
info.feature_types = {FeatureType::kCategorical};

12
tests/cpp/tree/test_constraints.cc
View File

@@ -1,3 +1,6 @@
/**
* Copyright 2019-2023, XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/base.h>
#include <xgboost/logging.h>
@@ -9,9 +12,7 @@
#include "../../../src/tree/hist/evaluate_splits.h"
#include "../helpers.h"
namespace xgboost {
namespace tree {
namespace xgboost::tree {
TEST(CPUFeatureInteractionConstraint, Empty) {
TrainParam param;
param.UpdateAllowUnknown(Args{});
@@ -77,7 +78,7 @@ TEST(CPUMonoConstraint, Basic) {
param.UpdateAllowUnknown(Args{{"monotone_constraints", str_mono}});
auto Xy = RandomDataGenerator{kRows, kCols, 0.0}.GenerateDMatrix(true);
auto sampler = std::make_shared<common::ColumnSampler>();
auto sampler = std::make_shared<common::ColumnSampler>(1u);
HistEvaluator evalutor{&ctx, &param, Xy->Info(), sampler};
evalutor.InitRoot(GradStats{2.0, 2.0});
@@ -90,5 +91,4 @@ TEST(CPUMonoConstraint, Basic) {
ASSERT_TRUE(evalutor.Evaluator().has_constraint);
}
} // namespace tree
} // namespace xgboost
} // namespace xgboost::tree