Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix CPU hist init for sparse dataset. (#4625)

Browse Source 
* Fix CPU hist init for sparse dataset.

* Implement sparse histogram cut.
* Allow empty features.

* Fix windows build, don't use sparse in distributed environment.

* Comments.

* Smaller threshold.

* Fix windows omp.

* Fix msvc lambda capture.

* Fix MSVC macro.

* Fix MSVC initialization list.

* Fix MSVC initialization list x2.

* Preserve categorical feature behavior.

* Rename matrix to sparse cuts.
* Reuse UseGroup.
* Check for categorical data when adding cut.

Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>

* Sanity check.

* Fix comments.

* Fix comment.
This commit is contained in:
Jiaming Yuan
2019-07-04 19:27:03 -04:00
committed by Philip Hyunsu Cho
parent b7a1f22d24
commit d9a47794a5
33 changed files with 681 additions and 299 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tests/cpp/tree/gpu_hist/test_row_partitioner.cu
View File

@@ -94,7 +94,7 @@ void TestUpdatePosition() {
}
TEST(RowPartitioner, Basic) { TestUpdatePosition(); }
void TestFinalise() {
const int kNumRows = 10;
RowPartitioner rp(0, kNumRows);

64
tests/cpp/tree/test_gpu_hist.cu
View File

@@ -53,27 +53,43 @@ TEST(GpuHist, DeviceHistogram) {
}
}
};
}
namespace {
class HistogramCutsWrapper : public common::HistogramCuts {
public:
using SuperT = common::HistogramCuts;
void SetValues(std::vector<float> cuts) {
SuperT::cut_values_ = cuts;
}
void SetPtrs(std::vector<uint32_t> ptrs) {
SuperT::cut_ptrs_ = ptrs;
}
void SetMins(std::vector<float> mins) {
SuperT::min_vals_ = mins;
}
};
} // anonymous namespace
template <typename GradientSumT>
void BuildGidx(DeviceShard<GradientSumT>* shard, int n_rows, int n_cols,
bst_float sparsity=0) {
auto dmat = CreateDMatrix(n_rows, n_cols, sparsity, 3);
const SparsePage& batch = *(*dmat)->GetRowBatches().begin();
common::HistCutMatrix cmat;
cmat.row_ptr = {0, 3, 6, 9, 12, 15, 18, 21, 24};
cmat.min_val = {0.1f, 0.2f, 0.3f, 0.1f, 0.2f, 0.3f, 0.2f, 0.2f};
HistogramCutsWrapper cmat;
cmat.SetPtrs({0, 3, 6, 9, 12, 15, 18, 21, 24});
// 24 cut fields, 3 cut fields for each feature (column).
cmat.cut = {0.30f, 0.67f, 1.64f,
0.32f, 0.77f, 1.95f,
0.29f, 0.70f, 1.80f,
0.32f, 0.75f, 1.85f,
0.18f, 0.59f, 1.69f,
0.25f, 0.74f, 2.00f,
0.26f, 0.74f, 1.98f,
0.26f, 0.71f, 1.83f};
cmat.SetValues({0.30f, 0.67f, 1.64f,
0.32f, 0.77f, 1.95f,
0.29f, 0.70f, 1.80f,
0.32f, 0.75f, 1.85f,
0.18f, 0.59f, 1.69f,
0.25f, 0.74f, 2.00f,
0.26f, 0.74f, 1.98f,
0.26f, 0.71f, 1.83f});
cmat.SetMins({0.1f, 0.2f, 0.3f, 0.1f, 0.2f, 0.3f, 0.2f, 0.2f});
auto is_dense = (*dmat)->Info().num_nonzero_ ==
(*dmat)->Info().num_row_ * (*dmat)->Info().num_col_;
@@ -241,20 +257,20 @@ TEST(GpuHist, BuildHistSharedMem) {
TestBuildHist<GradientPair>(true);
}
common::HistCutMatrix GetHostCutMatrix () {
common::HistCutMatrix cmat;
cmat.row_ptr = {0, 3, 6, 9, 12, 15, 18, 21, 24};
cmat.min_val = {0.1f, 0.2f, 0.3f, 0.1f, 0.2f, 0.3f, 0.2f, 0.2f};
HistogramCutsWrapper GetHostCutMatrix () {
HistogramCutsWrapper cmat;
cmat.SetPtrs({0, 3, 6, 9, 12, 15, 18, 21, 24});
cmat.SetMins({0.1f, 0.2f, 0.3f, 0.1f, 0.2f, 0.3f, 0.2f, 0.2f});
// 24 cut fields, 3 cut fields for each feature (column).
// Each row of the cut represents the cuts for a data column.
cmat.cut = {0.30f, 0.67f, 1.64f,
cmat.SetValues({0.30f, 0.67f, 1.64f,
0.32f, 0.77f, 1.95f,
0.29f, 0.70f, 1.80f,
0.32f, 0.75f, 1.85f,
0.18f, 0.59f, 1.69f,
0.25f, 0.74f, 2.00f,
0.26f, 0.74f, 1.98f,
0.26f, 0.71f, 1.83f};
0.26f, 0.71f, 1.83f});
return cmat;
}
@@ -293,21 +309,21 @@ TEST(GpuHist, EvaluateSplits) {
shard->node_sum_gradients = {{6.4f, 12.8f}};
// Initialize DeviceShard::cut
common::HistCutMatrix cmat = GetHostCutMatrix();
auto cmat = GetHostCutMatrix();
// Copy cut matrix to device.
shard->ba.Allocate(0,
&(shard->feature_segments), cmat.row_ptr.size(),
&(shard->min_fvalue), cmat.min_val.size(),
&(shard->feature_segments), cmat.Ptrs().size(),
&(shard->min_fvalue), cmat.MinValues().size(),
&(shard->gidx_fvalue_map), 24,
&(shard->monotone_constraints), kNCols);
dh::CopyVectorToDeviceSpan(shard->feature_segments, cmat.row_ptr);
dh::CopyVectorToDeviceSpan(shard->gidx_fvalue_map, cmat.cut);
dh::CopyVectorToDeviceSpan(shard->feature_segments, cmat.Ptrs());
dh::CopyVectorToDeviceSpan(shard->gidx_fvalue_map, cmat.Values());
dh::CopyVectorToDeviceSpan(shard->monotone_constraints,
param.monotone_constraints);
shard->ellpack_matrix.feature_segments = shard->feature_segments;
shard->ellpack_matrix.gidx_fvalue_map = shard->gidx_fvalue_map;
dh::CopyVectorToDeviceSpan(shard->min_fvalue, cmat.min_val);
dh::CopyVectorToDeviceSpan(shard->min_fvalue, cmat.MinValues());
shard->ellpack_matrix.min_fvalue = shard->min_fvalue;
// Initialize DeviceShard::hist

2
tests/cpp/tree/test_prune.cc
View File

@@ -13,7 +13,7 @@ namespace xgboost {
namespace tree {
TEST(Updater, Prune) {
int constexpr kNRows = 32, kNCols = 16;
int constexpr kNCols = 16;
std::vector<std::pair<std::string, std::string>> cfg;
cfg.emplace_back(std::pair<std::string, std::string>(

50
tests/cpp/tree/test_quantile_hist.cc
View File

@@ -1,5 +1,5 @@
/*!
* Copyright 2018 by Contributors
* Copyright 2018-2019 by Contributors
*/
#include "../helpers.h"
#include "../../../src/tree/param.h"
@@ -46,23 +46,25 @@ class QuantileHistMock : public QuantileHistMaker {
const size_t num_row = p_fmat->Info().num_row_;
const size_t num_col = p_fmat->Info().num_col_;
/* Validate HistCutMatrix */
ASSERT_EQ(gmat.cut.row_ptr.size(), num_col + 1);
ASSERT_EQ(gmat.cut.Ptrs().size(), num_col + 1);
for (size_t fid = 0; fid < num_col; ++fid) {
// Each feature must have at least one quantile point (cut)
const size_t ibegin = gmat.cut.row_ptr[fid];
const size_t iend = gmat.cut.row_ptr[fid + 1];
ASSERT_LT(ibegin, iend);
const size_t ibegin = gmat.cut.Ptrs()[fid];
const size_t iend = gmat.cut.Ptrs()[fid + 1];
// Ordered, but empty feature is allowed.
ASSERT_LE(ibegin, iend);
for (size_t i = ibegin; i < iend - 1; ++i) {
// Quantile points must be sorted in ascending order
// No duplicates allowed
ASSERT_LT(gmat.cut.cut[i], gmat.cut.cut[i + 1]);
ASSERT_LT(gmat.cut.Values()[i], gmat.cut.Values()[i + 1])
<< "ibegin: " << ibegin << ", "
<< "iend: " << iend;
}
}
/* Validate GHistIndexMatrix */
ASSERT_EQ(gmat.row_ptr.size(), num_row + 1);
ASSERT_LT(*std::max_element(gmat.index.begin(), gmat.index.end()),
gmat.cut.row_ptr.back());
gmat.cut.Ptrs().back());
for (const auto& batch : p_fmat->GetRowBatches()) {
for (size_t i = 0; i < batch.Size(); ++i) {
const size_t rid = batch.base_rowid + i;
@@ -71,20 +73,20 @@ class QuantileHistMock : public QuantileHistMaker {
ASSERT_LT(gmat_row_offset, gmat.index.size());
SparsePage::Inst inst = batch[i];
ASSERT_EQ(gmat.row_ptr[rid] + inst.size(), gmat.row_ptr[rid + 1]);
for (size_t j = 0; j < inst.size(); ++j) {
for (int64_t j = 0; j < inst.size(); ++j) {
// Each entry of GHistIndexMatrix represents a bin ID
const size_t bin_id = gmat.index[gmat_row_offset + j];
const size_t fid = inst[j].index;
// The bin ID must correspond to correct feature
ASSERT_GE(bin_id, gmat.cut.row_ptr[fid]);
ASSERT_LT(bin_id, gmat.cut.row_ptr[fid + 1]);
ASSERT_GE(bin_id, gmat.cut.Ptrs()[fid]);
ASSERT_LT(bin_id, gmat.cut.Ptrs()[fid + 1]);
// The bin ID must correspond to a region between two
// suitable quantile points
ASSERT_LT(inst[j].fvalue, gmat.cut.cut[bin_id]);
if (bin_id > gmat.cut.row_ptr[fid]) {
ASSERT_GE(inst[j].fvalue, gmat.cut.cut[bin_id - 1]);
ASSERT_LT(inst[j].fvalue, gmat.cut.Values()[bin_id]);
if (bin_id > gmat.cut.Ptrs()[fid]) {
ASSERT_GE(inst[j].fvalue, gmat.cut.Values()[bin_id - 1]);
} else {
ASSERT_GE(inst[j].fvalue, gmat.cut.min_val[fid]);
ASSERT_GE(inst[j].fvalue, gmat.cut.MinValues()[fid]);
}
}
}
@@ -106,11 +108,12 @@ class QuantileHistMock : public QuantileHistMaker {
std::vector<std::vector<uint8_t>> hist_is_init;
std::vector<ExpandEntry> nodes = {ExpandEntry(nid, -1, -1, tree.GetDepth(0), 0.0, 0)};
BuildHistsBatch(nodes, const_cast<RegTree*>(&tree), gmat, gpair, &hist_buffers, &hist_is_init);
RealImpl::InitNewNode(nid, gmat, gpair, fmat, const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
RealImpl::InitNewNode(nid, gmat, gpair, fmat,
const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
EvaluateSplitsBatch(nodes, gmat, fmat, hist_is_init, hist_buffers);
// Check if number of histogram bins is correct
ASSERT_EQ(hist_[nid].size(), gmat.cut.row_ptr.back());
ASSERT_EQ(hist_[nid].size(), gmat.cut.Ptrs().back());
std::vector<GradientPairPrecise> histogram_expected(hist_[nid].size());
// Compute the correct histogram (histogram_expected)
@@ -126,7 +129,7 @@ class QuantileHistMock : public QuantileHistMaker {
}
// Now validate the computed histogram returned by BuildHist
for (size_t i = 0; i < hist_[nid].size(); ++i) {
for (int64_t i = 0; i < hist_[nid].size(); ++i) {
GradientPairPrecise sol = histogram_expected[i];
ASSERT_NEAR(sol.GetGrad(), hist_[nid][i].GetGrad(), kEps);
ASSERT_NEAR(sol.GetHess(), hist_[nid][i].GetHess(), kEps);
@@ -140,7 +143,7 @@ class QuantileHistMock : public QuantileHistMaker {
{0.27f, 0.29f}, {0.37f, 0.39f}, {-0.47f, 0.49f}, {0.57f, 0.59f} };
size_t constexpr kMaxBins = 4;
auto dmat = CreateDMatrix(kNRows, kNCols, 0, 3);
// dense, no missing values
// dense, no missing values
common::GHistIndexMatrix gmat;
gmat.Init((*dmat).get(), kMaxBins);
@@ -152,7 +155,8 @@ class QuantileHistMock : public QuantileHistMaker {
std::vector<std::vector<float*>> hist_buffers;
std::vector<std::vector<uint8_t>> hist_is_init;
BuildHistsBatch(nodes, const_cast<RegTree*>(&tree), gmat, row_gpairs, &hist_buffers, &hist_is_init);
RealImpl::InitNewNode(0, gmat, row_gpairs, *(*dmat), const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
RealImpl::InitNewNode(0, gmat, row_gpairs, *(*dmat),
const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
EvaluateSplitsBatch(nodes, gmat, **dmat, hist_is_init, hist_buffers);
/* Compute correct split (best_split) using the computed histogram */
@@ -178,8 +182,8 @@ class QuantileHistMock : public QuantileHistMaker {
size_t best_split_feature = std::numeric_limits<size_t>::max();
// Enumerate all features
for (size_t fid = 0; fid < num_feature; ++fid) {
const size_t bin_id_min = gmat.cut.row_ptr[fid];
const size_t bin_id_max = gmat.cut.row_ptr[fid + 1];
const size_t bin_id_min = gmat.cut.Ptrs()[fid];
const size_t bin_id_max = gmat.cut.Ptrs()[fid + 1];
// Enumerate all bin ID in [bin_id_min, bin_id_max), i.e. every possible
// choice of thresholds for feature fid
for (size_t split_thresh = bin_id_min;
@@ -217,7 +221,7 @@ class QuantileHistMock : public QuantileHistMaker {
EvaluateSplitsBatch(nodes, gmat, **dmat, hist_is_init, hist_buffers);
ASSERT_EQ(snode_[0].best.SplitIndex(), best_split_feature);
ASSERT_EQ(snode_[0].best.split_value, gmat.cut.cut[best_split_threshold]);
ASSERT_EQ(snode_[0].best.split_value, gmat.cut.Values()[best_split_threshold]);
delete dmat;
}