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[breaking] Drop single precision histogram (#7892)

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Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2022-05-13 19:54:55 +08:00
committed by GitHub
parent c8f9d4b6e6
commit 1b6538b4e5
18 changed files with 171 additions and 407 deletions
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24
tests/cpp/tree/hist/test_evaluate_splits.cc
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@@ -12,7 +12,7 @@
namespace xgboost {
namespace tree {
template <typename GradientSumT> void TestEvaluateSplits() {
void TestEvaluateSplits() {
int static constexpr kRows = 8, kCols = 16;
auto orig = omp_get_max_threads();
int32_t n_threads = std::min(omp_get_max_threads(), 4);
@@ -24,9 +24,8 @@ template <typename GradientSumT> void TestEvaluateSplits() {
auto dmat = RandomDataGenerator(kRows, kCols, 0).Seed(3).GenerateDMatrix();
auto evaluator =
HistEvaluator<GradientSumT, CPUExpandEntry>{param, dmat->Info(), n_threads, sampler};
common::HistCollection<GradientSumT> hist;
auto evaluator = HistEvaluator<CPUExpandEntry>{param, dmat->Info(), n_threads, sampler};
common::HistCollection hist;
std::vector<GradientPair> row_gpairs = {
{1.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {2.27f, 0.28f},
{0.27f, 0.29f}, {0.37f, 0.39f}, {-0.47f, 0.49f}, {0.57f, 0.59f}};
@@ -40,7 +39,7 @@ template <typename GradientSumT> void TestEvaluateSplits() {
std::iota(row_indices.begin(), row_indices.end(), 0);
row_set_collection.Init();
auto hist_builder = common::GHistBuilder<GradientSumT>(gmat.cut.Ptrs().back());
auto hist_builder = common::GHistBuilder(gmat.cut.Ptrs().back());
hist.Init(gmat.cut.Ptrs().back());
hist.AddHistRow(0);
hist.AllocateAllData();
@@ -85,10 +84,7 @@ template <typename GradientSumT> void TestEvaluateSplits() {
omp_set_num_threads(orig);
}
TEST(HistEvaluator, Evaluate) {
TestEvaluateSplits<float>();
TestEvaluateSplits<double>();
}
TEST(HistEvaluator, Evaluate) { TestEvaluateSplits(); }
TEST(HistEvaluator, Apply) {
RegTree tree;
@@ -97,7 +93,7 @@ TEST(HistEvaluator, Apply) {
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 evaluator_ = HistEvaluator<float, CPUExpandEntry>{param, dmat->Info(), 4, sampler};
auto evaluator_ = HistEvaluator<CPUExpandEntry>{param, dmat->Info(), 4, sampler};
CPUExpandEntry entry{0, 0, 10.0f};
entry.split.left_sum = GradStats{0.4, 0.6f};
@@ -123,8 +119,7 @@ TEST_F(TestPartitionBasedSplit, CPUHist) {
// check the evaluator is returning the optimal split
std::vector<FeatureType> ft{FeatureType::kCategorical};
auto sampler = std::make_shared<common::ColumnSampler>();
HistEvaluator<double, CPUExpandEntry> evaluator{param_, info_, common::OmpGetNumThreads(0),
sampler};
HistEvaluator<CPUExpandEntry> evaluator{param_, info_, common::OmpGetNumThreads(0), sampler};
evaluator.InitRoot(GradStats{total_gpair_});
RegTree tree;
std::vector<CPUExpandEntry> entries(1);
@@ -155,12 +150,11 @@ auto CompareOneHotAndPartition(bool onehot) {
int32_t n_threads = 16;
auto sampler = std::make_shared<common::ColumnSampler>();
auto evaluator =
HistEvaluator<GradientSumT, CPUExpandEntry>{param, dmat->Info(), n_threads, sampler};
auto evaluator = HistEvaluator<CPUExpandEntry>{param, dmat->Info(), n_threads, sampler};
std::vector<CPUExpandEntry> entries(1);
for (auto const &gmat : dmat->GetBatches<GHistIndexMatrix>({32, param.sparse_threshold})) {
common::HistCollection<GradientSumT> hist;
common::HistCollection hist;
entries.front().nid = 0;
entries.front().depth = 0;