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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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53
tests/cpp/data/test_gradient_index.cc
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@@ -2,20 +2,38 @@
* Copyright 2021-2023 by XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/data.h>
#include <xgboost/data.h> // for BatchIterator, BatchSet, DMatrix, BatchParam
#include "../../../src/common/column_matrix.h"
#include "../../../src/common/io.h" // MemoryBufferStream
#include "../../../src/data/gradient_index.h"
#include "../helpers.h"
#include <algorithm> // for sort, unique
#include <cmath> // for isnan
#include <cstddef> // for size_t
#include <limits> // for numeric_limits
#include <memory> // for shared_ptr, __shared_ptr_access, unique_ptr
#include <string> // for string
#include <tuple> // for make_tuple, tie, tuple
#include <utility> // for move
#include <vector> // for vector
#include "../../../src/common/categorical.h" // for AsCat
#include "../../../src/common/column_matrix.h" // for ColumnMatrix
#include "../../../src/common/hist_util.h" // for Index, HistogramCuts, SketchOnDMatrix
#include "../../../src/common/io.h" // for MemoryBufferStream
#include "../../../src/data/adapter.h" // for SparsePageAdapterBatch
#include "../../../src/data/gradient_index.h" // for GHistIndexMatrix
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h" // for CreateEmptyGenericParam, GenerateRandomCa...
#include "xgboost/base.h" // for bst_bin_t
#include "xgboost/context.h" // for Context
#include "xgboost/host_device_vector.h" // for HostDeviceVector
namespace xgboost {
namespace data {
TEST(GradientIndex, ExternalMemory) {
auto ctx = CreateEmptyGenericParam(Context::kCpuId);
std::unique_ptr<DMatrix> dmat = CreateSparsePageDMatrix(10000);
std::vector<size_t> base_rowids;
std::vector<float> hessian(dmat->Info().num_row_, 1);
for (auto const &page : dmat->GetBatches<GHistIndexMatrix>({64, hessian, true})) {
for (auto const &page : dmat->GetBatches<GHistIndexMatrix>(&ctx, {64, hessian, true})) {
base_rowids.push_back(page.base_rowid);
}
size_t i = 0;
@@ -24,9 +42,8 @@ TEST(GradientIndex, ExternalMemory) {
++i;
}
base_rowids.clear();
for (auto const &page : dmat->GetBatches<GHistIndexMatrix>({64, hessian, false})) {
for (auto const &page : dmat->GetBatches<GHistIndexMatrix>(&ctx, {64, hessian, false})) {
base_rowids.push_back(page.base_rowid);
}
i = 0;
@@ -41,12 +58,13 @@ TEST(GradientIndex, FromCategoricalBasic) {
size_t max_bins = 8;
auto x = GenerateRandomCategoricalSingleColumn(kRows, kCats);
auto m = GetDMatrixFromData(x, kRows, 1);
auto ctx = CreateEmptyGenericParam(Context::kCpuId);
auto &h_ft = m->Info().feature_types.HostVector();
h_ft.resize(kCols, FeatureType::kCategorical);
BatchParam p(max_bins, 0.8);
GHistIndexMatrix gidx(m.get(), max_bins, p.sparse_thresh, false, AllThreadsForTest(), {});
GHistIndexMatrix gidx(&ctx, m.get(), max_bins, p.sparse_thresh, false, {});
auto x_copy = x;
std::sort(x_copy.begin(), x_copy.end());
@@ -80,11 +98,11 @@ TEST(GradientIndex, FromCategoricalLarge) {
BatchParam p{max_bins, 0.8};
{
GHistIndexMatrix gidx(m.get(), max_bins, p.sparse_thresh, false, AllThreadsForTest(), {});
GHistIndexMatrix gidx{&ctx, m.get(), max_bins, p.sparse_thresh, false, {}};
ASSERT_TRUE(gidx.index.GetBinTypeSize() == common::kUint16BinsTypeSize);
}
{
for (auto const &page : m->GetBatches<GHistIndexMatrix>(p)) {
for (auto const &page : m->GetBatches<GHistIndexMatrix>(&ctx, p)) {
common::HistogramCuts cut = page.cut;
GHistIndexMatrix gidx{m->Info(), std::move(cut), max_bins};
ASSERT_EQ(gidx.MaxNumBinPerFeat(), kCats);
@@ -96,10 +114,11 @@ TEST(GradientIndex, PushBatch) {
size_t constexpr kRows = 64, kCols = 4;
bst_bin_t max_bins = 64;
float st = 0.5;
Context ctx;
auto test = [&](float sparisty) {
auto m = RandomDataGenerator{kRows, kCols, sparisty}.GenerateDMatrix(true);
auto cuts = common::SketchOnDMatrix(m.get(), max_bins, AllThreadsForTest(), false, {});
auto cuts = common::SketchOnDMatrix(&ctx, m.get(), max_bins, false, {});
common::HistogramCuts copy_cuts = cuts;
ASSERT_EQ(m->Info().num_row_, kRows);
@@ -112,7 +131,7 @@ TEST(GradientIndex, PushBatch) {
m->Info().num_row_);
gmat.PushAdapterBatchColumns(m->Ctx(), batch, std::numeric_limits<float>::quiet_NaN(), 0);
}
for (auto const &page : m->GetBatches<GHistIndexMatrix>(BatchParam{max_bins, st})) {
for (auto const &page : m->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{max_bins, st})) {
for (size_t i = 0; i < kRows; ++i) {
for (size_t j = 0; j < kCols; ++j) {
auto v0 = gmat.GetFvalue(i, j, false);
@@ -143,17 +162,19 @@ class GHistIndexMatrixTest : public testing::TestWithParam<std::tuple<float, flo
// device.
size_t n_samples{128}, n_features{13};
Context ctx;
ctx.gpu_id = 0;
auto Xy = RandomDataGenerator{n_samples, n_features, 1 - density}.GenerateDMatrix(true);
std::unique_ptr<GHistIndexMatrix> from_ellpack;
ASSERT_TRUE(Xy->SingleColBlock());
bst_bin_t constexpr kBins{17};
auto p = BatchParam{kBins, threshold};
for (auto const &page : Xy->GetBatches<EllpackPage>(BatchParam{0, kBins})) {
Context gpu_ctx;
gpu_ctx.gpu_id = 0;
for (auto const &page : Xy->GetBatches<EllpackPage>(
&gpu_ctx, BatchParam{kBins, tree::TrainParam::DftSparseThreshold()})) {
from_ellpack.reset(new GHistIndexMatrix{&ctx, Xy->Info(), page, p});
}
for (auto const &from_sparse_page : Xy->GetBatches<GHistIndexMatrix>(p)) {
for (auto const &from_sparse_page : Xy->GetBatches<GHistIndexMatrix>(&ctx, p)) {
ASSERT_EQ(from_sparse_page.IsDense(), from_ellpack->IsDense());
ASSERT_EQ(from_sparse_page.base_rowid, 0);
ASSERT_EQ(from_sparse_page.base_rowid, from_ellpack->base_rowid);