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Use matrix for gradient. (#9508)

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- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)
This commit is contained in:
Jiaming Yuan
2023-08-24 05:29:52 +08:00
committed by GitHub
parent 6103dca0bb
commit 972730cde0
77 changed files with 1052 additions and 651 deletions
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21
tests/cpp/helpers.cc
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@@ -96,9 +96,9 @@ void CheckObjFunctionImpl(std::unique_ptr<xgboost::ObjFunction> const& obj,
std::vector<xgboost::bst_float> out_grad,
std::vector<xgboost::bst_float> out_hess) {
xgboost::HostDeviceVector<xgboost::bst_float> in_preds(preds);
xgboost::HostDeviceVector<xgboost::GradientPair> out_gpair;
obj->GetGradient(in_preds, info, 1, &out_gpair);
std::vector<xgboost::GradientPair>& gpair = out_gpair.HostVector();
xgboost::linalg::Matrix<xgboost::GradientPair> out_gpair;
obj->GetGradient(in_preds, info, 0, &out_gpair);
std::vector<xgboost::GradientPair>& gpair = out_gpair.Data()->HostVector();
ASSERT_EQ(gpair.size(), in_preds.Size());
for (int i = 0; i < static_cast<int>(gpair.size()); ++i) {
@@ -119,8 +119,8 @@ void CheckObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
std::vector<xgboost::bst_float> out_hess) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels =
xgboost::linalg::Tensor<float, 2>{labels.cbegin(), labels.cend(), {labels.size()}, -1};
info.labels = xgboost::linalg::Tensor<float, 2>{
labels.cbegin(), labels.cend(), {labels.size(), static_cast<std::size_t>(1)}, -1};
info.weights_.HostVector() = weights;
CheckObjFunctionImpl(obj, preds, labels, weights, info, out_grad, out_hess);
@@ -155,8 +155,8 @@ void CheckRankingObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
std::vector<xgboost::bst_float> out_hess) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels = xgboost::linalg::Tensor<float, 2>{
labels.cbegin(), labels.cend(), {labels.size(), static_cast<size_t>(1)}, -1};
info.labels = xgboost::linalg::Matrix<float>{
labels.cbegin(), labels.cend(), {labels.size(), static_cast<std::size_t>(1)}, -1};
info.weights_.HostVector() = weights;
info.group_ptr_ = groups;
@@ -645,11 +645,10 @@ std::unique_ptr<GradientBooster> CreateTrainedGBM(std::string name, Args kwargs,
}
p_dmat->Info().labels =
linalg::Tensor<float, 2>{labels.cbegin(), labels.cend(), {labels.size()}, -1};
HostDeviceVector<GradientPair> gpair;
auto& h_gpair = gpair.HostVector();
h_gpair.resize(kRows);
linalg::Matrix<GradientPair> gpair({kRows}, ctx->Ordinal());
auto h_gpair = gpair.HostView();
for (size_t i = 0; i < kRows; ++i) {
h_gpair[i] = GradientPair{static_cast<float>(i), 1};
h_gpair(i) = GradientPair{static_cast<float>(i), 1};
}
PredictionCacheEntry predts;