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Support learning rate for zero-hessian objectives. (#8866)

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This commit is contained in:
Jiaming Yuan
2023-03-06 20:33:28 +08:00
committed by GitHub
parent 173096a6a7
commit 228a46e8ad
34 changed files with 464 additions and 434 deletions
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38
tests/cpp/tree/test_gpu_hist.cu
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@@ -1,5 +1,5 @@
/*!
* Copyright 2017-2022 XGBoost contributors
/**
* Copyright 2017-2023 by XGBoost contributors
*/
#include <gtest/gtest.h>
#include <thrust/device_vector.h>
@@ -13,6 +13,7 @@
#include "../../../src/common/common.h"
#include "../../../src/data/sparse_page_source.h"
#include "../../../src/tree/constraints.cuh"
#include "../../../src/tree/param.h" // for TrainParam
#include "../../../src/tree/updater_gpu_common.cuh"
#include "../../../src/tree/updater_gpu_hist.cu"
#include "../filesystem.h" // dmlc::TemporaryDirectory
@@ -21,8 +22,7 @@
#include "xgboost/context.h"
#include "xgboost/json.h"
namespace xgboost {
namespace tree {
namespace xgboost::tree {
TEST(GpuHist, DeviceHistogram) {
// Ensures that node allocates correctly after reaching `kStopGrowingSize`.
dh::safe_cuda(cudaSetDevice(0));
@@ -83,11 +83,12 @@ void TestBuildHist(bool use_shared_memory_histograms) {
int const kNRows = 16, kNCols = 8;
TrainParam param;
std::vector<std::pair<std::string, std::string>> args {
{"max_depth", "6"},
{"max_leaves", "0"},
Args args{
{"max_depth", "6"},
{"max_leaves", "0"},
};
param.Init(args);
auto page = BuildEllpackPage(kNRows, kNCols);
BatchParam batch_param{};
Context ctx{CreateEmptyGenericParam(0)};
@@ -168,7 +169,6 @@ void TestHistogramIndexImpl() {
int constexpr kNRows = 1000, kNCols = 10;
// Build 2 matrices and build a histogram maker with that
Context ctx(CreateEmptyGenericParam(0));
tree::GPUHistMaker hist_maker{&ctx, ObjInfo{ObjInfo::kRegression}},
hist_maker_ext{&ctx, ObjInfo{ObjInfo::kRegression}};
@@ -179,15 +179,14 @@ void TestHistogramIndexImpl() {
std::unique_ptr<DMatrix> hist_maker_ext_dmat(
CreateSparsePageDMatrixWithRC(kNRows, kNCols, 128UL, true, tempdir));
std::vector<std::pair<std::string, std::string>> training_params = {
{"max_depth", "10"},
{"max_leaves", "0"}
};
Args training_params = {{"max_depth", "10"}, {"max_leaves", "0"}};
TrainParam param;
param.UpdateAllowUnknown(training_params);
hist_maker.Configure(training_params);
hist_maker.InitDataOnce(hist_maker_dmat.get());
hist_maker.InitDataOnce(&param, hist_maker_dmat.get());
hist_maker_ext.Configure(training_params);
hist_maker_ext.InitDataOnce(hist_maker_ext_dmat.get());
hist_maker_ext.InitDataOnce(&param, hist_maker_ext_dmat.get());
// Extract the device maker from the histogram makers and from that its compressed
// histogram index
@@ -237,13 +236,15 @@ void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
{"subsample", std::to_string(subsample)},
{"sampling_method", sampling_method},
};
TrainParam param;
param.UpdateAllowUnknown(args);
Context ctx(CreateEmptyGenericParam(0));
tree::GPUHistMaker hist_maker{&ctx,ObjInfo{ObjInfo::kRegression}};
hist_maker.Configure(args);
std::vector<HostDeviceVector<bst_node_t>> position(1);
hist_maker.Update(gpair, dmat, common::Span<HostDeviceVector<bst_node_t>>{position}, {tree});
hist_maker.Update(&param, gpair, dmat, common::Span<HostDeviceVector<bst_node_t>>{position},
{tree});
auto cache = linalg::VectorView<float>{preds->DeviceSpan(), {preds->Size()}, 0};
hist_maker.UpdatePredictionCache(dmat, cache);
}
@@ -391,13 +392,11 @@ TEST(GpuHist, ConfigIO) {
Json j_updater { Object() };
updater->SaveConfig(&j_updater);
ASSERT_TRUE(IsA<Object>(j_updater["gpu_hist_train_param"]));
ASSERT_TRUE(IsA<Object>(j_updater["train_param"]));
updater->LoadConfig(j_updater);
Json j_updater_roundtrip { Object() };
updater->SaveConfig(&j_updater_roundtrip);
ASSERT_TRUE(IsA<Object>(j_updater_roundtrip["gpu_hist_train_param"]));
ASSERT_TRUE(IsA<Object>(j_updater_roundtrip["train_param"]));
ASSERT_EQ(j_updater, j_updater_roundtrip);
}
@@ -414,5 +413,4 @@ TEST(GpuHist, MaxDepth) {
ASSERT_THROW({learner->UpdateOneIter(0, p_mat);}, dmlc::Error);
}
} // namespace tree
} // namespace xgboost
} // namespace xgboost::tree