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/**
* Copyright 2024, XGBoost Contributors
*/
#include "test_column_split.h"
#include <gtest/gtest.h>
#include <xgboost/tree_model.h> // for RegTree
#include <xgboost/tree_updater.h> // for TreeUpdater
#include <vector> // for vector
#include "../../../src/tree/param.h" // for TrainParam
#include "../collective/test_worker.h" // for TestDistributedGlobal
namespace xgboost::tree {
void TestColumnSplit(bst_target_t n_targets, bool categorical, std::string name, float sparsity) {
auto constexpr kRows = 32;
auto constexpr kCols = 16;
RegTree expected_tree{n_targets, static_cast<bst_feature_t>(kCols)};
ObjInfo task{ObjInfo::kRegression};
Context ctx;
{
auto p_dmat = GenerateCatDMatrix(kRows, kCols, sparsity, categorical);
auto gpair = GenerateRandomGradients(&ctx, kRows, n_targets);
std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create(name, &ctx, &task)};
std::vector<HostDeviceVector<bst_node_t>> position(1);
TrainParam param;
param.Init(Args{});
updater->Configure(Args{});
updater->Update(¶m, &gpair, p_dmat.get(), position, {&expected_tree});
}
auto constexpr kWorldSize = 2;
auto verify = [&] {
Context ctx;
collective::GetWorkerLocalThreads(kWorldSize, &ctx);
auto p_dmat = GenerateCatDMatrix(kRows, kCols, sparsity, categorical);
auto gpair = GenerateRandomGradients(&ctx, kRows, n_targets);
ObjInfo task{ObjInfo::kRegression};
std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create(name, &ctx, &task)};
std::vector<HostDeviceVector<bst_node_t>> position(1);
std::unique_ptr<DMatrix> sliced{
p_dmat->SliceCol(collective::GetWorldSize(), collective::GetRank())};
RegTree tree{n_targets, static_cast<bst_feature_t>(kCols)};
TrainParam param;
param.Init(Args{});
updater->Configure(Args{});
updater->Update(¶m, &gpair, sliced.get(), position, {&tree});
Json json{Object{}};
tree.SaveModel(&json);
Json expected_json{Object{}};
expected_tree.SaveModel(&expected_json);
ASSERT_EQ(json, expected_json);
};
collective::TestDistributedGlobal(kWorldSize, [&] { verify(); });
}
} // namespace xgboost::tree
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