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Support categorical data in GPU sketching. (#6137)

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This commit is contained in:
Jiaming Yuan
2020-09-21 13:53:06 +08:00
committed by GitHub
parent c932fb50a1
commit 210c131ce7
6 changed files with 196 additions and 62 deletions
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43
tests/cpp/common/test_hist_util.cu
View File

@@ -108,7 +108,7 @@ TEST(HistUtil, DeviceSketchDeterminism) {
}
}
TEST(HistUtil, DeviceSketchCategorical) {
TEST(HistUtil, DeviceSketchCategoricalAsNumeric) {
int categorical_sizes[] = {2, 6, 8, 12};
int num_bins = 256;
int sizes[] = {25, 100, 1000};
@@ -122,6 +122,33 @@ TEST(HistUtil, DeviceSketchCategorical) {
}
}
void TestCategoricalSketch(size_t n, size_t num_categories, int32_t num_bins) {
auto x = GenerateRandomCategoricalSingleColumn(n, num_categories);
auto dmat = GetDMatrixFromData(x, n, 1);
dmat->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
ASSERT_EQ(dmat->Info().feature_types.Size(), 1);
auto cuts = DeviceSketch(0, dmat.get(), num_bins);
std::sort(x.begin(), x.end());
auto n_uniques = std::unique(x.begin(), x.end()) - x.begin();
ASSERT_NE(n_uniques, x.size());
ASSERT_EQ(cuts.TotalBins(), n_uniques);
ASSERT_EQ(n_uniques, num_categories);
auto& values = cuts.cut_values_.HostVector();
ASSERT_TRUE(std::is_sorted(values.cbegin(), values.cend()));
auto is_unique = (std::unique(values.begin(), values.end()) - values.begin()) == n_uniques;
ASSERT_TRUE(is_unique);
x.resize(n_uniques);
for (size_t i = 0; i < n_uniques; ++i) {
ASSERT_EQ(x[i], values[i]);
}
}
TEST(HistUtil, DeviceSketchCategoricalFeatures) {
TestCategoricalSketch(1000, 256, 32);
}
TEST(HistUtil, DeviceSketchMultipleColumns) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100, 1000, 1500};
@@ -237,7 +264,8 @@ TEST(HistUtil, DeviceSketchExternalMemoryWithWeights) {
template <typename Adapter>
auto MakeUnweightedCutsForTest(Adapter adapter, int32_t num_bins, float missing, size_t batch_size = 0) {
common::HistogramCuts batched_cuts;
SketchContainer sketch_container(num_bins, adapter.NumColumns(), adapter.NumRows(), 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_container(ft, num_bins, adapter.NumColumns(), adapter.NumRows(), 0);
MetaInfo info;
AdapterDeviceSketch(adapter.Value(), num_bins, info, std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
@@ -305,7 +333,8 @@ TEST(HistUtil, AdapterSketchSlidingWindowMemory) {
dh::GlobalMemoryLogger().Clear();
ConsoleLogger::Configure({{"verbosity", "3"}});
common::HistogramCuts batched_cuts;
SketchContainer sketch_container(num_bins, num_columns, num_rows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_container(ft, num_bins, num_columns, num_rows, 0);
AdapterDeviceSketch(adapter.Value(), num_bins, info, std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
HistogramCuts cuts;
@@ -332,10 +361,12 @@ TEST(HistUtil, AdapterSketchSlidingWindowWeightedMemory) {
dh::GlobalMemoryLogger().Clear();
ConsoleLogger::Configure({{"verbosity", "3"}});
common::HistogramCuts batched_cuts;
SketchContainer sketch_container(num_bins, num_columns, num_rows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_container(ft, num_bins, num_columns, num_rows, 0);
AdapterDeviceSketch(adapter.Value(), num_bins, info,
std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
HistogramCuts cuts;
sketch_container.MakeCuts(&cuts);
ConsoleLogger::Configure({{"verbosity", "0"}});
@@ -477,9 +508,11 @@ void TestAdapterSketchFromWeights(bool with_group) {
data::CupyAdapter adapter(m);
auto const& batch = adapter.Value();
SketchContainer sketch_container(kBins, kCols, kRows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_container(ft, kBins, kCols, kRows, 0);
AdapterDeviceSketch(adapter.Value(), kBins, info, std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
common::HistogramCuts cuts;
sketch_container.MakeCuts(&cuts);

51
tests/cpp/common/test_quantile.cu
View File

@@ -8,7 +8,8 @@ namespace xgboost {
namespace common {
TEST(GPUQuantile, Basic) {
constexpr size_t kRows = 1000, kCols = 100, kBins = 256;
SketchContainer sketch(kBins, kCols, kRows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch(ft, kBins, kCols, kRows, 0);
dh::caching_device_vector<Entry> entries;
dh::device_vector<bst_row_t> cuts_ptr(kCols+1);
thrust::fill(cuts_ptr.begin(), cuts_ptr.end(), 0);
@@ -20,7 +21,8 @@ TEST(GPUQuantile, Basic) {
void TestSketchUnique(float sparsity) {
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(kRows, [kRows, kCols, sparsity](int32_t seed, size_t n_bins, MetaInfo const& info) {
SketchContainer sketch(n_bins, kCols, kRows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch(ft, n_bins, kCols, kRows, 0);
HostDeviceVector<float> storage;
std::string interface_str = RandomDataGenerator{kRows, kCols, sparsity}
@@ -93,8 +95,10 @@ void TestQuantileElemRank(int32_t device, Span<SketchEntry const> in,
TEST(GPUQuantile, Prune) {
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins, MetaInfo const& info) {
SketchContainer sketch(n_bins, kCols, kRows, 0);
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins,
MetaInfo const &info) {
HostDeviceVector<FeatureType> ft;
SketchContainer sketch(ft, n_bins, kCols, kRows, 0);
HostDeviceVector<float> storage;
std::string interface_str = RandomDataGenerator{kRows, kCols, 0}
@@ -111,8 +115,8 @@ TEST(GPUQuantile, Prune) {
if (n_bins <= kRows) {
ASSERT_EQ(sketch.Data().size(), n_bins * kCols);
} else {
// LE because kRows * kCols is pushed into sketch, after removing duplicated entries
// we might not have that much inputs for prune.
// LE because kRows * kCols is pushed into sketch, after removing
// duplicated entries we might not have that much inputs for prune.
ASSERT_LE(sketch.Data().size(), kRows * kCols);
}
// This is not necessarily true for all inputs without calling unique after
@@ -127,7 +131,8 @@ TEST(GPUQuantile, Prune) {
TEST(GPUQuantile, MergeEmpty) {
constexpr size_t kRows = 1000, kCols = 100;
size_t n_bins = 10;
SketchContainer sketch_0(n_bins, kCols, kRows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_0(ft, n_bins, kCols, kRows, 0);
HostDeviceVector<float> storage_0;
std::string interface_str_0 =
RandomDataGenerator{kRows, kCols, 0}.Device(0).GenerateArrayInterface(
@@ -166,7 +171,8 @@ TEST(GPUQuantile, MergeEmpty) {
TEST(GPUQuantile, MergeBasic) {
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins, MetaInfo const& info) {
SketchContainer sketch_0(n_bins, kCols, kRows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_0(ft, n_bins, kCols, kRows, 0);
HostDeviceVector<float> storage_0;
std::string interface_str_0 = RandomDataGenerator{kRows, kCols, 0}
.Device(0)
@@ -176,7 +182,7 @@ TEST(GPUQuantile, MergeBasic) {
AdapterDeviceSketch(adapter_0.Value(), n_bins, info,
std::numeric_limits<float>::quiet_NaN(), &sketch_0);
SketchContainer sketch_1(n_bins, kCols, kRows * kRows, 0);
SketchContainer sketch_1(ft, n_bins, kCols, kRows * kRows, 0);
HostDeviceVector<float> storage_1;
std::string interface_str_1 = RandomDataGenerator{kRows, kCols, 0}
.Device(0)
@@ -212,7 +218,8 @@ TEST(GPUQuantile, MergeBasic) {
void TestMergeDuplicated(int32_t n_bins, size_t cols, size_t rows, float frac) {
MetaInfo info;
int32_t seed = 0;
SketchContainer sketch_0(n_bins, cols, rows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_0(ft, n_bins, cols, rows, 0);
HostDeviceVector<float> storage_0;
std::string interface_str_0 = RandomDataGenerator{rows, cols, 0}
.Device(0)
@@ -224,7 +231,7 @@ void TestMergeDuplicated(int32_t n_bins, size_t cols, size_t rows, float frac) {
&sketch_0);
size_t f_rows = rows * frac;
SketchContainer sketch_1(n_bins, cols, f_rows, 0);
SketchContainer sketch_1(ft, n_bins, cols, f_rows, 0);
HostDeviceVector<float> storage_1;
std::string interface_str_1 = RandomDataGenerator{f_rows, cols, 0}
.Device(0)
@@ -286,12 +293,14 @@ TEST(GPUQuantile, AllReduceBasic) {
}
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins, MetaInfo const& info) {
// Set up single node version;
SketchContainer sketch_on_single_node(n_bins, kCols, kRows, 0);
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins,
MetaInfo const &info) {
// Set up single node version
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_on_single_node(ft, n_bins, kCols, kRows, 0);
size_t intermediate_num_cuts =
std::min(kRows * world, static_cast<size_t>(n_bins * WQSketch::kFactor));
size_t intermediate_num_cuts = std::min(
kRows * world, static_cast<size_t>(n_bins * WQSketch::kFactor));
std::vector<SketchContainer> containers;
for (auto rank = 0; rank < world; ++rank) {
HostDeviceVector<float> storage;
@@ -300,12 +309,13 @@ TEST(GPUQuantile, AllReduceBasic) {
.Seed(rank + seed)
.GenerateArrayInterface(&storage);
data::CupyAdapter adapter(interface_str);
containers.emplace_back(n_bins, kCols, kRows, 0);
HostDeviceVector<FeatureType> ft;
containers.emplace_back(ft, n_bins, kCols, kRows, 0);
AdapterDeviceSketch(adapter.Value(), n_bins, info,
std::numeric_limits<float>::quiet_NaN(),
&containers.back());
}
for (auto& sketch : containers) {
for (auto &sketch : containers) {
sketch.Prune(intermediate_num_cuts);
sketch_on_single_node.Merge(sketch.ColumnsPtr(), sketch.Data());
sketch_on_single_node.FixError();
@@ -317,7 +327,7 @@ TEST(GPUQuantile, AllReduceBasic) {
// Set up distributed version. We rely on using rank as seed to generate
// the exact same copy of data.
auto rank = rabit::GetRank();
SketchContainer sketch_distributed(n_bins, kCols, kRows, 0);
SketchContainer sketch_distributed(ft, n_bins, kCols, kRows, 0);
HostDeviceVector<float> storage;
std::string interface_str = RandomDataGenerator{kRows, kCols, 0}
.Device(0)
@@ -376,7 +386,8 @@ TEST(GPUQuantile, SameOnAllWorkers) {
RunWithSeedsAndBins(kRows, [=](int32_t seed, size_t n_bins,
MetaInfo const &info) {
auto rank = rabit::GetRank();
SketchContainer sketch_distributed(n_bins, kCols, kRows, 0);
HostDeviceVector<FeatureType> ft;
SketchContainer sketch_distributed(ft, n_bins, kCols, kRows, 0);
HostDeviceVector<float> storage;
std::string interface_str = RandomDataGenerator{kRows, kCols, 0}
.Device(0)