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[SYCL]. Add implementation for loss-guided policy (#10681)

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Co-authored-by: Dmitry Razdoburdin <>
This commit is contained in:
Dmitry Razdoburdin
2024-08-09 03:04:46 +02:00
committed by GitHub
parent cc3b56fc37
commit e555a238bc
3 changed files with 169 additions and 7 deletions
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73
tests/cpp/plugin/test_sycl_hist_updater.cc
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@@ -51,9 +51,8 @@ class TestHistUpdater : public HistUpdater<GradientSumT> {
auto TestInitNewNode(int nid,
const common::GHistIndexMatrix& gmat,
const USMVector<GradientPair, MemoryType::on_device> &gpair,
const DMatrix& fmat,
const RegTree& tree) {
HistUpdater<GradientSumT>::InitNewNode(nid, gmat, gpair, fmat, tree);
HistUpdater<GradientSumT>::InitNewNode(nid, gmat, gpair, tree);
return HistUpdater<GradientSumT>::snode_host_[nid];
}
@@ -69,6 +68,13 @@ class TestHistUpdater : public HistUpdater<GradientSumT> {
RegTree* p_tree) {
HistUpdater<GradientSumT>::ApplySplit(nodes, gmat, p_tree);
}
auto TestExpandWithLossGuide(const common::GHistIndexMatrix& gmat,
DMatrix *p_fmat,
RegTree* p_tree,
const USMVector<GradientPair, MemoryType::on_device> &gpair) {
HistUpdater<GradientSumT>::ExpandWithLossGuide(gmat, p_tree, gpair);
}
};
void GenerateRandomGPairs(::sycl::queue* qu, GradientPair* gpair_ptr, size_t num_rows, bool has_neg_hess) {
@@ -300,7 +306,7 @@ void TestHistUpdaterInitNewNode(const xgboost::tree::TrainParam& param, float sp
auto& row_idxs = row_set_collection->Data();
const size_t* row_idxs_ptr = row_idxs.DataConst();
updater.TestBuildHistogramsLossGuide(node, gmat, &tree, gpair);
const auto snode = updater.TestInitNewNode(ExpandEntry::kRootNid, gmat, gpair, *p_fmat, tree);
const auto snode = updater.TestInitNewNode(ExpandEntry::kRootNid, gmat, gpair, tree);
GradStats<GradientSumT> grad_stat;
{
@@ -360,7 +366,7 @@ void TestHistUpdaterEvaluateSplits(const xgboost::tree::TrainParam& param) {
auto& row_idxs = row_set_collection->Data();
const size_t* row_idxs_ptr = row_idxs.DataConst();
const auto* hist = updater.TestBuildHistogramsLossGuide(node, gmat, &tree, gpair);
const auto snode_init = updater.TestInitNewNode(ExpandEntry::kRootNid, gmat, gpair, *p_fmat, tree);
const auto snode_init = updater.TestInitNewNode(ExpandEntry::kRootNid, gmat, gpair, tree);
const auto snode_updated = updater.TestEvaluateSplits({node}, gmat, tree);
auto best_loss_chg = snode_updated[0].best.loss_chg;
@@ -488,6 +494,56 @@ void TestHistUpdaterApplySplit(const xgboost::tree::TrainParam& param, float spa
}
}
template <typename GradientSumT>
void TestHistUpdaterExpandWithLossGuide(const xgboost::tree::TrainParam& param) {
const size_t num_rows = 3;
const size_t num_columns = 1;
const size_t n_bins = 16;
Context ctx;
ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
DeviceManager device_manager;
auto qu = device_manager.GetQueue(ctx.Device());
std::vector<float> data = {7, 3, 15};
auto p_fmat = GetDMatrixFromData(data, num_rows, num_columns);
DeviceMatrix dmat;
dmat.Init(qu, p_fmat.get());
common::GHistIndexMatrix gmat;
gmat.Init(qu, &ctx, dmat, n_bins);
std::vector<GradientPair> gpair_host = {{1, 2}, {3, 1}, {1, 1}};
USMVector<GradientPair, MemoryType::on_device> gpair(&qu, gpair_host);
RegTree tree;
FeatureInteractionConstraintHost int_constraints;
ObjInfo task{ObjInfo::kRegression};
std::unique_ptr<TreeUpdater> pruner{TreeUpdater::Create("prune", &ctx, &task)};
TestHistUpdater<GradientSumT> updater(&ctx, qu, param, std::move(pruner), int_constraints, p_fmat.get());
updater.SetHistSynchronizer(new BatchHistSynchronizer<GradientSumT>());
updater.SetHistRowsAdder(new BatchHistRowsAdder<GradientSumT>());
auto* row_set_collection = updater.TestInitData(gmat, gpair, *p_fmat, tree);
updater.TestExpandWithLossGuide(gmat, p_fmat.get(), &tree, gpair);
const auto& nodes = tree.GetNodes();
std::vector<float> ans(data.size());
for (size_t data_idx = 0; data_idx < data.size(); ++data_idx) {
size_t node_idx = 0;
while (!nodes[node_idx].IsLeaf()) {
node_idx = data[data_idx] < nodes[node_idx].SplitCond() ? nodes[node_idx].LeftChild() : nodes[node_idx].RightChild();
}
ans[data_idx] = nodes[node_idx].LeafValue();
}
ASSERT_NEAR(ans[0], -0.15, 1e-6);
ASSERT_NEAR(ans[1], -0.45, 1e-6);
ASSERT_NEAR(ans[2], -0.15, 1e-6);
}
TEST(SyclHistUpdater, Sampling) {
xgboost::tree::TrainParam param;
param.UpdateAllowUnknown(Args{{"subsample", "0.7"}});
@@ -555,4 +611,13 @@ TEST(SyclHistUpdater, ApplySplitDence) {
TestHistUpdaterApplySplit<double>(param, 0.0, (1u << 16) + 1);
}
TEST(SyclHistUpdater, ExpandWithLossGuide) {
xgboost::tree::TrainParam param;
param.UpdateAllowUnknown(Args{{"max_depth", "2"},
{"grow_policy", "lossguide"}});
TestHistUpdaterExpandWithLossGuide<float>(param);
TestHistUpdaterExpandWithLossGuide<double>(param);
}
} // namespace xgboost::sycl::tree