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

More refactoring to take advantage of collective aggregators (#9081)

Browse Source
This commit is contained in:
Rong Ou
2023-04-25 12:36:09 -07:00
committed by GitHub
parent 49ccae7fb9
commit a320b402a5
10 changed files with 81 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files

66
src/objective/adaptive.cc
View File

@@ -99,44 +99,40 @@ void UpdateTreeLeafHost(Context const* ctx, std::vector<bst_node_t> const& posit
auto h_predt = linalg::MakeTensorView(ctx, predt.ConstHostSpan(), info.num_row_,
predt.Size() / info.num_row_);
if (!info.IsVerticalFederated() || collective::GetRank() == 0) {
// loop over each leaf
common::ParallelFor(quantiles.size(), ctx->Threads(), [&](size_t k) {
auto nidx = h_node_idx[k];
CHECK(tree[nidx].IsLeaf());
CHECK_LT(k + 1, h_node_ptr.size());
size_t n = h_node_ptr[k + 1] - h_node_ptr[k];
auto h_row_set = common::Span<size_t const>{ridx}.subspan(h_node_ptr[k], n);
collective::ApplyWithLabels(
info, static_cast<void*>(quantiles.data()), quantiles.size() * sizeof(float), [&] {
// loop over each leaf
common::ParallelFor(quantiles.size(), ctx->Threads(), [&](size_t k) {
auto nidx = h_node_idx[k];
CHECK(tree[nidx].IsLeaf());
CHECK_LT(k + 1, h_node_ptr.size());
size_t n = h_node_ptr[k + 1] - h_node_ptr[k];
auto h_row_set = common::Span<size_t const>{ridx}.subspan(h_node_ptr[k], n);
auto h_labels = info.labels.HostView().Slice(linalg::All(), IdxY(info, group_idx));
auto h_weights = linalg::MakeVec(&info.weights_);
auto h_labels = info.labels.HostView().Slice(linalg::All(), IdxY(info, group_idx));
auto h_weights = linalg::MakeVec(&info.weights_);
auto iter = common::MakeIndexTransformIter([&](size_t i) -> float {
auto row_idx = h_row_set[i];
return h_labels(row_idx) - h_predt(row_idx, group_idx);
auto iter = common::MakeIndexTransformIter([&](size_t i) -> float {
auto row_idx = h_row_set[i];
return h_labels(row_idx) - h_predt(row_idx, group_idx);
});
auto w_it = common::MakeIndexTransformIter([&](size_t i) -> float {
auto row_idx = h_row_set[i];
return h_weights(row_idx);
});
float q{0};
if (info.weights_.Empty()) {
q = common::Quantile(ctx, alpha, iter, iter + h_row_set.size());
} else {
q = common::WeightedQuantile(ctx, alpha, iter, iter + h_row_set.size(), w_it);
}
if (std::isnan(q)) {
CHECK(h_row_set.empty());
}
quantiles.at(k) = q;
});
});
auto w_it = common::MakeIndexTransformIter([&](size_t i) -> float {
auto row_idx = h_row_set[i];
return h_weights(row_idx);
});
float q{0};
if (info.weights_.Empty()) {
q = common::Quantile(ctx, alpha, iter, iter + h_row_set.size());
} else {
q = common::WeightedQuantile(ctx, alpha, iter, iter + h_row_set.size(), w_it);
}
if (std::isnan(q)) {
CHECK(h_row_set.empty());
}
quantiles.at(k) = q;
});
}
if (info.IsVerticalFederated()) {
collective::Broadcast(static_cast<void*>(quantiles.data()), quantiles.size() * sizeof(float),
0);
}
UpdateLeafValues(&quantiles, nidx, info, learning_rate, p_tree);
}

2
src/objective/quantile_obj.cu
View File

@@ -36,7 +36,7 @@ class QuantileRegression : public ObjFunction {
bst_target_t Targets(MetaInfo const& info) const override {
auto const& alpha = param_.quantile_alpha.Get();
CHECK_EQ(alpha.size(), alpha_.Size()) << "The objective is not yet configured.";
if (!info.IsVerticalFederated() || collective::GetRank() == 0) {
if (info.ShouldHaveLabels()) {
CHECK_EQ(info.labels.Shape(1), 1)
<< "Multi-target is not yet supported by the quantile loss.";
}