More tests for column split and vertical federated learning (#8985)

Added some more tests for the learner and fit_stump, for both column-wise distributed learning and vertical federated learning.

Also moved the `IsRowSplit` and `IsColumnSplit` methods from the `DMatrix` to the `MetaInfo` since in some places we only have access to the `MetaInfo`. Added a new convenience method `IsVerticalFederatedLearning`.

Some refactoring of the testing fixtures.

tests/cpp/plugin/test_federated_learner.cc

@@ -0,0 +1,78 @@
+/*!
+ * Copyright 2023 XGBoost contributors
+ */
+#include <dmlc/parameter.h>
+#include <gtest/gtest.h>
+#include <xgboost/data.h>
+#include <xgboost/objective.h>
+
+#include "../../../plugin/federated/federated_server.h"
+#include "../../../src/collective/communicator-inl.h"
+#include "../helpers.h"
+#include "helpers.h"
+
+namespace xgboost {
+
+class FederatedLearnerTest : public BaseFederatedTest {
+ protected:
+  static auto constexpr kRows{16};
+  static auto constexpr kCols{16};
+};
+
+void VerifyBaseScore(size_t rows, size_t cols, float expected_base_score) {
+  auto const world_size = collective::GetWorldSize();
+  auto const rank = collective::GetRank();
+  std::shared_ptr<DMatrix> Xy_{RandomDataGenerator{rows, cols, 0}.GenerateDMatrix(rank == 0)};
+  std::shared_ptr<DMatrix> sliced{Xy_->SliceCol(world_size, rank)};
+  std::unique_ptr<Learner> learner{Learner::Create({sliced})};
+  learner->SetParam("tree_method", "approx");
+  learner->SetParam("objective", "binary:logistic");
+  learner->UpdateOneIter(0, sliced);
+  Json config{Object{}};
+  learner->SaveConfig(&config);
+  auto base_score = GetBaseScore(config);
+  ASSERT_EQ(base_score, expected_base_score);
+}
+
+void VerifyModel(size_t rows, size_t cols, Json const& expected_model) {
+  auto const world_size = collective::GetWorldSize();
+  auto const rank = collective::GetRank();
+  std::shared_ptr<DMatrix> Xy_{RandomDataGenerator{rows, cols, 0}.GenerateDMatrix(rank == 0)};
+  std::shared_ptr<DMatrix> sliced{Xy_->SliceCol(world_size, rank)};
+  std::unique_ptr<Learner> learner{Learner::Create({sliced})};
+  learner->SetParam("tree_method", "approx");
+  learner->SetParam("objective", "binary:logistic");
+  learner->UpdateOneIter(0, sliced);
+  Json model{Object{}};
+  learner->SaveModel(&model);
+  ASSERT_EQ(model, expected_model);
+}
+
+TEST_F(FederatedLearnerTest, BaseScore) {
+  std::shared_ptr<DMatrix> Xy_{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true)};
+  std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
+  learner->SetParam("tree_method", "approx");
+  learner->SetParam("objective", "binary:logistic");
+  learner->UpdateOneIter(0, Xy_);
+  Json config{Object{}};
+  learner->SaveConfig(&config);
+  auto base_score = GetBaseScore(config);
+  ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());
+
+  RunWithFederatedCommunicator(kWorldSize, server_address_, &VerifyBaseScore, kRows, kCols,
+                               base_score);
+}
+
+TEST_F(FederatedLearnerTest, Model) {
+  std::shared_ptr<DMatrix> Xy_{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true)};
+  std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
+  learner->SetParam("tree_method", "approx");
+  learner->SetParam("objective", "binary:logistic");
+  learner->UpdateOneIter(0, Xy_);
+  Json model{Object{}};
+  learner->SaveModel(&model);
+
+  RunWithFederatedCommunicator(kWorldSize, server_address_, &VerifyModel, kRows, kCols,
+                               std::cref(model));
+}
+}  // namespace xgboost