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Implement GK sketching on GPU. (#5846)

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* Implement GK sketching on GPU.
* Strong tests on quantile building.
* Handle sparse dataset by binary searching the column index.
* Hypothesis test on dask.
This commit is contained in:
Jiaming Yuan
2020-07-07 12:16:21 +08:00
committed by GitHub
parent ac3f0e78dc
commit 048d969be4
25 changed files with 2045 additions and 405 deletions
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87
tests/cpp/common/test_hist_util.cu
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@@ -30,11 +30,12 @@ HistogramCuts GetHostCuts(AdapterT *adapter, int num_bins, float missing) {
builder.Build(&dmat, num_bins);
return cuts;
}
TEST(HistUtil, DeviceSketch) {
int num_rows = 5;
int num_columns = 1;
int num_bins = 4;
std::vector<float> x = {1.0, 2.0, 3.0, 4.0, 5.0};
std::vector<float> x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 7.0f, -1.0f};
int num_rows = x.size();
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto device_cuts = DeviceSketch(0, dmat.get(), num_bins);
@@ -47,26 +48,6 @@ TEST(HistUtil, DeviceSketch) {
EXPECT_EQ(device_cuts.MinValues(), host_cuts.MinValues());
}
// Duplicate this function from hist_util.cu so we don't have to expose it in
// header
size_t RequiredSampleCutsTest(int max_bins, size_t num_rows) {
double eps = 1.0 / (SketchContainer::kFactor * max_bins);
size_t dummy_nlevel;
size_t num_cuts;
WQuantileSketch<bst_float, bst_float>::LimitSizeLevel(
num_rows, eps, &dummy_nlevel, &num_cuts);
return std::min(num_cuts, num_rows);
}
size_t BytesRequiredForTest(size_t num_rows, size_t num_columns, size_t num_bins,
bool with_weights) {
size_t bytes_num_elements = BytesPerElement(with_weights) * num_rows * num_columns;
size_t bytes_cuts = RequiredSampleCutsTest(num_bins, num_rows) * num_columns *
sizeof(DenseCuts::WQSketch::Entry);
// divide by 2 is because the memory quota used in sorting is reused for storing cuts.
return bytes_num_elements / 2 + bytes_cuts;
}
TEST(HistUtil, DeviceSketchMemory) {
int num_columns = 100;
int num_rows = 1000;
@@ -77,15 +58,15 @@ TEST(HistUtil, DeviceSketchMemory) {
dh::GlobalMemoryLogger().Clear();
ConsoleLogger::Configure({{"verbosity", "3"}});
auto device_cuts = DeviceSketch(0, dmat.get(), num_bins);
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_required = BytesRequiredForTest(num_rows, num_columns, num_bins, false);
size_t bytes_constant = 1000;
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required + bytes_constant);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required);
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, false);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 1.05);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 0.95);
ConsoleLogger::Configure({{"verbosity", "0"}});
}
TEST(HistUtil, DeviceSketchMemoryWeights) {
TEST(HistUtil, DeviceSketchWeightsMemory) {
int num_columns = 100;
int num_rows = 1000;
int num_bins = 256;
@@ -98,7 +79,8 @@ TEST(HistUtil, DeviceSketchMemoryWeights) {
auto device_cuts = DeviceSketch(0, dmat.get(), num_bins);
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_required = BytesRequiredForTest(num_rows, num_columns, num_bins, true);
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, true);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 1.05);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required);
}
@@ -118,7 +100,7 @@ TEST(HistUtil, DeviceSketchDeterminism) {
}
}
TEST(HistUtil, DeviceSketchCategorical) {
TEST(HistUtil, DeviceSketchCategorical) {
int categorical_sizes[] = {2, 6, 8, 12};
int num_bins = 256;
int sizes[] = {25, 100, 1000};
@@ -231,11 +213,10 @@ template <typename Adapter>
void ValidateBatchedCuts(Adapter adapter, int num_bins, int num_columns, int num_rows,
DMatrix* dmat) {
common::HistogramCuts batched_cuts;
SketchContainer sketch_container(num_bins, num_columns, num_rows);
SketchContainer sketch_container(num_bins, num_columns, num_rows, 0);
AdapterDeviceSketch(adapter.Value(), num_bins, std::numeric_limits<float>::quiet_NaN(),
0, &sketch_container);
common::DenseCuts dense_cuts(&batched_cuts);
dense_cuts.Init(&sketch_container.sketches_, num_bins, num_rows);
&sketch_container);
sketch_container.MakeCuts(&batched_cuts);
ValidateCuts(batched_cuts, dmat, num_bins);
}
@@ -275,12 +256,13 @@ TEST(HistUtil, AdapterDeviceSketchMemory) {
std::numeric_limits<float>::quiet_NaN());
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_constant = 1000;
size_t bytes_required = BytesRequiredForTest(num_rows, num_columns, num_bins, false);
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, false);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required + bytes_constant);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 0.95);
}
TEST(HistUtil, AdapterSketchBatchMemory) {
TEST(HistUtil, AdapterSketchSlidingWindowMemory) {
int num_columns = 100;
int num_rows = 1000;
int num_bins = 256;
@@ -291,17 +273,19 @@ TEST(HistUtil, AdapterSketchBatchMemory) {
dh::GlobalMemoryLogger().Clear();
ConsoleLogger::Configure({{"verbosity", "3"}});
common::HistogramCuts batched_cuts;
SketchContainer sketch_container(num_bins, num_columns, num_rows);
SketchContainer sketch_container(num_bins, num_columns, num_rows, 0);
AdapterDeviceSketch(adapter.Value(), num_bins, std::numeric_limits<float>::quiet_NaN(),
0, &sketch_container);
&sketch_container);
HistogramCuts cuts;
sketch_container.MakeCuts(&cuts);
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, false);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 1.05);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 0.95);
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_constant = 1000;
size_t bytes_required = BytesRequiredForTest(num_rows, num_columns, num_bins, false);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required + bytes_constant);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required);
}
TEST(HistUtil, AdapterSketchBatchWeightedMemory) {
TEST(HistUtil, AdapterSketchSlidingWindowWeightedMemory) {
int num_columns = 100;
int num_rows = 1000;
int num_bins = 256;
@@ -316,12 +300,15 @@ TEST(HistUtil, AdapterSketchBatchWeightedMemory) {
dh::GlobalMemoryLogger().Clear();
ConsoleLogger::Configure({{"verbosity", "3"}});
common::HistogramCuts batched_cuts;
SketchContainer sketch_container(num_bins, num_columns, num_rows);
SketchContainer sketch_container(num_bins, num_columns, num_rows, 0);
AdapterDeviceSketchWeighted(adapter.Value(), num_bins, info,
std::numeric_limits<float>::quiet_NaN(), 0,
std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
HistogramCuts cuts;
sketch_container.MakeCuts(&cuts);
ConsoleLogger::Configure({{"verbosity", "0"}});
size_t bytes_required = BytesRequiredForTest(num_rows, num_columns, num_bins, true);
size_t bytes_required = detail::RequiredMemory(
num_rows, num_columns, num_rows * num_columns, num_bins, true);
EXPECT_LE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required * 1.05);
EXPECT_GE(dh::GlobalMemoryLogger().PeakMemory(), bytes_required);
}
@@ -462,13 +449,11 @@ void TestAdapterSketchFromWeights(bool with_group) {
data::CupyAdapter adapter(m);
auto const& batch = adapter.Value();
SketchContainer sketch_container(kBins, kCols, kRows);
SketchContainer sketch_container(kBins, kCols, kRows, 0);
AdapterDeviceSketchWeighted(adapter.Value(), kBins, info, std::numeric_limits<float>::quiet_NaN(),
0,
&sketch_container);
common::HistogramCuts cuts;
common::DenseCuts dense_cuts(&cuts);
dense_cuts.Init(&sketch_container.sketches_, kBins, kRows);
sketch_container.MakeCuts(&cuts);
auto dmat = GetDMatrixFromData(storage.HostVector(), kRows, kCols);
if (with_group) {