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Use Booster context in DMatrix. (#8896)

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- Pass context from booster to DMatrix.
- Use context instead of integer for `n_threads`.
- Check the consistency configuration for `max_bin`.
- Test for all combinations of initialization options.
This commit is contained in:
Jiaming Yuan
2023-04-28 21:47:14 +08:00
committed by GitHub
parent 1f9a57d17b
commit 08ce495b5d
67 changed files with 1283 additions and 935 deletions
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76
tests/cpp/tree/hist/test_histogram.cc
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@@ -25,6 +25,7 @@ void InitRowPartitionForTest(common::RowSetCollection *row_set, size_t n_samples
} // anonymous namespace
void TestAddHistRows(bool is_distributed) {
auto ctx = CreateEmptyGenericParam(Context::kCpuId);
std::vector<CPUExpandEntry> nodes_for_explicit_hist_build_;
std::vector<CPUExpandEntry> nodes_for_subtraction_trick_;
int starting_index = std::numeric_limits<int>::max();
@@ -32,9 +33,9 @@ void TestAddHistRows(bool is_distributed) {
size_t constexpr kNRows = 8, kNCols = 16;
int32_t constexpr kMaxBins = 4;
auto p_fmat =
RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(BatchParam{kMaxBins, 0.5}).begin());
auto p_fmat = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto const &gmat =
*(p_fmat->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{kMaxBins, 0.5}).begin());
RegTree tree;
@@ -73,6 +74,7 @@ TEST(CPUHistogram, AddRows) {
void TestSyncHist(bool is_distributed) {
size_t constexpr kNRows = 8, kNCols = 16;
int32_t constexpr kMaxBins = 4;
auto ctx = CreateEmptyGenericParam(Context::kCpuId);
std::vector<CPUExpandEntry> nodes_for_explicit_hist_build_;
std::vector<CPUExpandEntry> nodes_for_subtraction_trick_;
@@ -80,9 +82,9 @@ void TestSyncHist(bool is_distributed) {
int sync_count = 0;
RegTree tree;
auto p_fmat =
RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(BatchParam{kMaxBins, 0.5}).begin());
auto p_fmat = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto const &gmat =
*(p_fmat->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{kMaxBins, 0.5}).begin());
HistogramBuilder<CPUExpandEntry> histogram;
uint32_t total_bins = gmat.cut.Ptrs().back();
@@ -227,12 +229,15 @@ TEST(CPUHistogram, SyncHist) {
void TestBuildHistogram(bool is_distributed, bool force_read_by_column, bool is_col_split) {
size_t constexpr kNRows = 8, kNCols = 16;
int32_t constexpr kMaxBins = 4;
auto p_fmat = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto ctx = CreateEmptyGenericParam(Context::kCpuId);
auto p_fmat =
RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
if (is_col_split) {
p_fmat = std::shared_ptr<DMatrix>{
p_fmat->SliceCol(collective::GetWorldSize(), collective::GetRank())};
}
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(BatchParam{kMaxBins, 0.5}).begin());
auto const &gmat =
*(p_fmat->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{kMaxBins, 0.5}).begin());
uint32_t total_bins = gmat.cut.Ptrs().back();
static double constexpr kEps = 1e-6;
@@ -257,9 +262,9 @@ void TestBuildHistogram(bool is_distributed, bool force_read_by_column, bool is_
CPUExpandEntry node{RegTree::kRoot, tree.GetDepth(0)};
std::vector<CPUExpandEntry> nodes_for_explicit_hist_build;
nodes_for_explicit_hist_build.push_back(node);
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>({kMaxBins, 0.5})) {
histogram.BuildHist(0, gidx, &tree, row_set_collection,
nodes_for_explicit_hist_build, {}, gpair, force_read_by_column);
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(&ctx, {kMaxBins, 0.5})) {
histogram.BuildHist(0, gidx, &tree, row_set_collection, nodes_for_explicit_hist_build, {},
gpair, force_read_by_column);
}
// Check if number of histogram bins is correct
@@ -325,6 +330,8 @@ void TestHistogramCategorical(size_t n_categories, bool force_read_by_column) {
auto x = GenerateRandomCategoricalSingleColumn(kRows, n_categories);
auto cat_m = GetDMatrixFromData(x, kRows, 1);
cat_m->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
auto ctx = CreateEmptyGenericParam(Context::kCpuId);
BatchParam batch_param{0, static_cast<int32_t>(kBins)};
RegTree tree;
@@ -345,12 +352,11 @@ void TestHistogramCategorical(size_t n_categories, bool force_read_by_column) {
* Generate hist with cat data.
*/
HistogramBuilder<CPUExpandEntry> cat_hist;
for (auto const &gidx : cat_m->GetBatches<GHistIndexMatrix>({kBins, 0.5})) {
for (auto const &gidx : cat_m->GetBatches<GHistIndexMatrix>(&ctx, {kBins, 0.5})) {
auto total_bins = gidx.cut.TotalBins();
cat_hist.Reset(total_bins, {kBins, 0.5}, omp_get_max_threads(), 1, false, false);
cat_hist.BuildHist(0, gidx, &tree, row_set_collection,
nodes_for_explicit_hist_build, {}, gpair.HostVector(),
force_read_by_column);
cat_hist.BuildHist(0, gidx, &tree, row_set_collection, nodes_for_explicit_hist_build, {},
gpair.HostVector(), force_read_by_column);
}
/**
@@ -359,12 +365,11 @@ void TestHistogramCategorical(size_t n_categories, bool force_read_by_column) {
auto x_encoded = OneHotEncodeFeature(x, n_categories);
auto encode_m = GetDMatrixFromData(x_encoded, kRows, n_categories);
HistogramBuilder<CPUExpandEntry> onehot_hist;
for (auto const &gidx : encode_m->GetBatches<GHistIndexMatrix>({kBins, 0.5})) {
for (auto const &gidx : encode_m->GetBatches<GHistIndexMatrix>(&ctx, {kBins, 0.5})) {
auto total_bins = gidx.cut.TotalBins();
onehot_hist.Reset(total_bins, {kBins, 0.5}, omp_get_max_threads(), 1, false, false);
onehot_hist.BuildHist(0, gidx, &tree, row_set_collection, nodes_for_explicit_hist_build, {},
gpair.HostVector(),
force_read_by_column);
gpair.HostVector(), force_read_by_column);
}
auto cat = cat_hist.Histogram()[0];
@@ -382,8 +387,8 @@ TEST(CPUHistogram, Categorical) {
}
}
namespace {
void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx, bool force_read_by_column) {
Context ctx;
void TestHistogramExternalMemory(Context const *ctx, BatchParam batch_param, bool is_approx,
bool force_read_by_column) {
size_t constexpr kEntries = 1 << 16;
auto m = CreateSparsePageDMatrix(kEntries, "cache");
@@ -410,7 +415,7 @@ void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx, bool fo
* Multi page
*/
std::vector<common::RowSetCollection> rows_set;
for (auto const &page : m->GetBatches<GHistIndexMatrix>(batch_param)) {
for (auto const &page : m->GetBatches<GHistIndexMatrix>(ctx, batch_param)) {
CHECK_LT(page.base_rowid, m->Info().num_row_);
auto n_rows_in_node = page.Size();
partition_size[0] = std::max(partition_size[0], n_rows_in_node);
@@ -426,12 +431,12 @@ void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx, bool fo
1, [&](size_t nidx_in_set) { return partition_size.at(nidx_in_set); },
256};
multi_build.Reset(total_bins, batch_param, ctx.Threads(), rows_set.size(), false, false);
multi_build.Reset(total_bins, batch_param, ctx->Threads(), rows_set.size(), false, false);
size_t page_idx{0};
for (auto const &page : m->GetBatches<GHistIndexMatrix>(batch_param)) {
multi_build.BuildHist(page_idx, space, page, &tree, rows_set.at(page_idx), nodes, {},
h_gpair, force_read_by_column);
for (auto const &page : m->GetBatches<GHistIndexMatrix>(ctx, batch_param)) {
multi_build.BuildHist(page_idx, space, page, &tree, rows_set.at(page_idx), nodes, {}, h_gpair,
force_read_by_column);
++page_idx;
}
ASSERT_EQ(page_idx, 2);
@@ -447,16 +452,16 @@ void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx, bool fo
common::RowSetCollection row_set_collection;
InitRowPartitionForTest(&row_set_collection, n_samples);
single_build.Reset(total_bins, batch_param, ctx.Threads(), 1, false, false);
single_build.Reset(total_bins, batch_param, ctx->Threads(), 1, false, false);
SparsePage concat;
std::vector<float> hess(m->Info().num_row_, 1.0f);
for (auto const& page : m->GetBatches<SparsePage>()) {
concat.Push(page);
}
auto cut = common::SketchOnDMatrix(m.get(), batch_param.max_bin, ctx.Threads(), false, hess);
auto cut = common::SketchOnDMatrix(ctx, m.get(), batch_param.max_bin, false, hess);
GHistIndexMatrix gmat(concat, {}, cut, batch_param.max_bin, false,
std::numeric_limits<double>::quiet_NaN(), ctx.Threads());
std::numeric_limits<double>::quiet_NaN(), ctx->Threads());
single_build.BuildHist(0, gmat, &tree, row_set_collection, nodes, {}, h_gpair, force_read_by_column);
single_page = single_build.Histogram()[0];
}
@@ -470,16 +475,17 @@ void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx, bool fo
TEST(CPUHistogram, ExternalMemory) {
int32_t constexpr kBins = 256;
TestHistogramExternalMemory(BatchParam{kBins, common::Span<float>{}, false}, true, false);
TestHistogramExternalMemory(BatchParam{kBins, common::Span<float>{}, false}, true, true);
auto ctx = CreateEmptyGenericParam(Context::kCpuId);
TestHistogramExternalMemory(&ctx, BatchParam{kBins, common::Span<float>{}, false}, true, false);
TestHistogramExternalMemory(&ctx, BatchParam{kBins, common::Span<float>{}, false}, true, true);
float sparse_thresh{0.5};
TestHistogramExternalMemory({kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory({kBins, sparse_thresh}, false, true);
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, true);
sparse_thresh = std::numeric_limits<float>::quiet_NaN();
TestHistogramExternalMemory({kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory({kBins, sparse_thresh}, false, true);
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, true);
}
} // namespace tree
} // namespace xgboost