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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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32
tests/cpp/helpers.h
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@@ -387,23 +387,6 @@ std::unique_ptr<GradientBooster> CreateTrainedGBM(std::string name, Args kwargs,
LearnerModelParam const* learner_model_param,
Context const* generic_param);
inline std::unique_ptr<HostDeviceVector<GradientPair>> GenerateGradients(
std::size_t rows, bst_target_t n_targets = 1) {
auto p_gradients = std::make_unique<HostDeviceVector<GradientPair>>(rows * n_targets);
auto& h_gradients = p_gradients->HostVector();
xgboost::SimpleLCG gen;
xgboost::SimpleRealUniformDistribution<bst_float> dist(0.0f, 1.0f);
for (std::size_t i = 0; i < rows * n_targets; ++i) {
auto grad = dist(&gen);
auto hess = dist(&gen);
h_gradients[i] = GradientPair{grad, hess};
}
return p_gradients;
}
/**
* \brief Make a context that uses CUDA if device >= 0.
*/
@@ -415,11 +398,12 @@ inline Context MakeCUDACtx(std::int32_t device) {
}
inline HostDeviceVector<GradientPair> GenerateRandomGradients(const size_t n_rows,
float lower= 0.0f, float upper = 1.0f) {
float lower = 0.0f,
float upper = 1.0f) {
xgboost::SimpleLCG gen;
xgboost::SimpleRealUniformDistribution<bst_float> dist(lower, upper);
std::vector<GradientPair> h_gpair(n_rows);
for (auto &gpair : h_gpair) {
for (auto& gpair : h_gpair) {
bst_float grad = dist(&gen);
bst_float hess = dist(&gen);
gpair = GradientPair(grad, hess);
@@ -428,6 +412,16 @@ inline HostDeviceVector<GradientPair> GenerateRandomGradients(const size_t n_row
return gpair;
}
inline linalg::Matrix<GradientPair> GenerateRandomGradients(Context const* ctx, bst_row_t n_rows,
bst_target_t n_targets,
float lower = 0.0f,
float upper = 1.0f) {
auto g = GenerateRandomGradients(n_rows * n_targets, lower, upper);
linalg::Matrix<GradientPair> gpair({n_rows, static_cast<bst_row_t>(n_targets)}, ctx->Device());
gpair.Data()->Copy(g);
return gpair;
}
typedef void *DMatrixHandle; // NOLINT(*);
class ArrayIterForTest {