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Group aware GPU sketching. (#5551)

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* Group aware GPU weighted sketching.

* Distribute group weights to each data point.
* Relax the test.
* Validate input meta info.
* Fix metainfo copy ctor.
This commit is contained in:
Jiaming Yuan
2020-04-20 17:18:52 +08:00
committed by GitHub
parent 397d8f0ee7
commit 29a4cfe400
9 changed files with 296 additions and 124 deletions
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74
tests/cpp/common/test_hist_util.cu
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@@ -3,22 +3,19 @@
#include <algorithm>
#include <cmath>
#include <thrust/device_vector.h>
#include "xgboost/c_api.h"
#include <xgboost/data.h>
#include <xgboost/c_api.h>
#include "test_hist_util.h"
#include "../helpers.h"
#include "../data/test_array_interface.h"
#include "../../../src/common/device_helpers.cuh"
#include "../../../src/common/hist_util.h"
#include "../helpers.h"
#include <xgboost/data.h>
#include "../../../src/data/device_adapter.cuh"
#include "../data/test_array_interface.h"
#include "../../../src/common/math.h"
#include "../../../src/data/simple_dmatrix.h"
#include "test_hist_util.h"
#include "../../../include/xgboost/logging.h"
namespace xgboost {
@@ -143,7 +140,6 @@ TEST(HistUtil, DeviceSketchMultipleColumns) {
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
}
TEST(HistUtil, DeviceSketchMultipleColumnsWeights) {
@@ -161,6 +157,29 @@ TEST(HistUtil, DeviceSketchMultipleColumnsWeights) {
}
}
TEST(HistUitl, DeviceSketchWeights) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100, 1000, 1500};
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto weighted_dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto& h_weights = weighted_dmat->Info().weights_.HostVector();
h_weights.resize(num_rows);
std::fill(h_weights.begin(), h_weights.end(), 1.0f);
for (auto num_bins : bin_sizes) {
auto cuts = DeviceSketch(0, dmat.get(), num_bins);
auto wcuts = DeviceSketch(0, weighted_dmat.get(), num_bins);
ASSERT_EQ(cuts.MinValues(), wcuts.MinValues());
ASSERT_EQ(cuts.Ptrs(), wcuts.Ptrs());
ASSERT_EQ(cuts.Values(), wcuts.Values());
ValidateCuts(cuts, dmat.get(), num_bins);
ValidateCuts(wcuts, weighted_dmat.get(), num_bins);
}
}
}
TEST(HistUtil, DeviceSketchBatches) {
int num_bins = 256;
int num_rows = 5000;
@@ -190,8 +209,7 @@ TEST(HistUtil, DeviceSketchMultipleColumnsExternal) {
}
}
TEST(HistUtil, AdapterDeviceSketch)
{
TEST(HistUtil, AdapterDeviceSketch) {
int rows = 5;
int cols = 1;
int num_bins = 4;
@@ -235,7 +253,7 @@ TEST(HistUtil, AdapterDeviceSketchMemory) {
bytes_num_elements + bytes_cuts + bytes_num_columns + bytes_constant);
}
TEST(HistUtil, AdapterDeviceSketchCategorical) {
TEST(HistUtil, AdapterDeviceSketchCategorical) {
int categorical_sizes[] = {2, 6, 8, 12};
int num_bins = 256;
int sizes[] = {25, 100, 1000};
@@ -268,6 +286,7 @@ TEST(HistUtil, AdapterDeviceSketchMultipleColumns) {
}
}
}
TEST(HistUtil, AdapterDeviceSketchBatches) {
int num_bins = 256;
int num_rows = 5000;
@@ -305,7 +324,38 @@ TEST(HistUtil, SketchingEquivalent) {
EXPECT_EQ(dmat_cuts.MinValues(), adapter_cuts.MinValues());
}
}
}
TEST(HistUtil, DeviceSketchFromGroupWeights) {
size_t constexpr kRows = 3000, kCols = 200, kBins = 256;
size_t constexpr kGroups = 10;
auto m = RandomDataGenerator {kRows, kCols, 0}.GenerateDMatrix();
auto& h_weights = m->Info().weights_.HostVector();
h_weights.resize(kRows);
std::fill(h_weights.begin(), h_weights.end(), 1.0f);
std::vector<bst_group_t> groups(kGroups);
for (size_t i = 0; i < kGroups; ++i) {
groups[i] = kRows / kGroups;
}
m->Info().SetInfo("group", groups.data(), DataType::kUInt32, kGroups);
HistogramCuts weighted_cuts = DeviceSketch(0, m.get(), kBins, 0);
h_weights.clear();
HistogramCuts cuts = DeviceSketch(0, m.get(), kBins, 0);
ASSERT_EQ(cuts.Values().size(), weighted_cuts.Values().size());
ASSERT_EQ(cuts.MinValues().size(), weighted_cuts.MinValues().size());
ASSERT_EQ(cuts.Ptrs().size(), weighted_cuts.Ptrs().size());
for (size_t i = 0; i < cuts.Values().size(); ++i) {
EXPECT_EQ(cuts.Values()[i], weighted_cuts.Values()[i]) << "i:"<< i;
}
for (size_t i = 0; i < cuts.MinValues().size(); ++i) {
ASSERT_EQ(cuts.MinValues()[i], weighted_cuts.MinValues()[i]);
}
for (size_t i = 0; i < cuts.Ptrs().size(); ++i) {
ASSERT_EQ(cuts.Ptrs().at(i), weighted_cuts.Ptrs().at(i));
}
}
} // namespace common
} // namespace xgboost