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

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* 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
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64
tests/cpp/tree/test_gpu_hist.cu
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@@ -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