Fix compiler warnings. (#7974)

- Remove unused parameters. There are still many warnings that are not yet addressed. Currently, the warnings in dmlc-core dominate the error log. - Remove `distributed` parameter from metric. - Fixes some warnings about signed comparison.
2022-06-06 22:56:25 +08:00 · 2022-06-06 22:56:25 +08:00 · 1a33b50a0d
commit 1a33b50a0d
parent d48123d23b
46 changed files with 149 additions and 189 deletions
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@ -144,6 +144,15 @@ function(xgboost_set_cuda_flags target)
    set_property(TARGET ${target} PROPERTY CUDA_ARCHITECTURES ${CMAKE_CUDA_ARCHITECTURES})
  endif (CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")

+  if (FORCE_COLORED_OUTPUT)
+    if (FORCE_COLORED_OUTPUT AND (CMAKE_GENERATOR STREQUAL "Ninja") AND
+        ((CMAKE_CXX_COMPILER_ID STREQUAL "GNU") OR
+          (CMAKE_CXX_COMPILER_ID STREQUAL "Clang")))
+      target_compile_options(${target} PRIVATE
+        $<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=-fdiagnostics-color=always>)
+    endif()
+  endif (FORCE_COLORED_OUTPUT)
+
  if (USE_DEVICE_DEBUG)
    target_compile_options(${target} PRIVATE
      $<$<AND:$<CONFIG:DEBUG>,$<COMPILE_LANGUAGE:CUDA>>:-G;-src-in-ptx>)
--- a/include/xgboost/gbm.h
+++ b/include/xgboost/gbm.h
@ -68,8 +68,8 @@ class GradientBooster : public Model, public Configurable {
   * \param layer_end   End of booster layer. 0 means do not limit trees.
   * \param out         Output gradient booster
   */
-  virtual void Slice(int32_t layer_begin, int32_t layer_end, int32_t step,
-                     GradientBooster *out, bool* out_of_bound) const {
+  virtual void Slice(int32_t /*layer_begin*/, int32_t /*layer_end*/, int32_t /*step*/,
+                     GradientBooster* /*out*/, bool* /*out_of_bound*/) const {
    LOG(FATAL) << "Slice is not supported by current booster.";
  }
  /*!
--- a/include/xgboost/json_io.h
+++ b/include/xgboost/json_io.h
@ -89,7 +89,7 @@ class JsonReader {
    } else if (got == 0) {
      msg += "\\0\"";
    } else {
-      msg += (got <= 127 ? std::string{got} : std::to_string(got)) + " \"";  // NOLINT
+      msg += (got <= static_cast<char>(127) ? std::string{got} : std::to_string(got)) + " \"";
    }
    Error(msg);
  }
--- a/include/xgboost/linalg.h
+++ b/include/xgboost/linalg.h
@ -317,7 +317,8 @@ class TensorView {
  }

  template <size_t old_dim, size_t new_dim, int32_t D, typename Index>
-  LINALG_HD size_t MakeSliceDim(size_t new_shape[D], size_t new_stride[D], Index i) const {
+  LINALG_HD size_t MakeSliceDim(DMLC_ATTRIBUTE_UNUSED size_t new_shape[D],
+                                DMLC_ATTRIBUTE_UNUSED size_t new_stride[D], Index i) const {
    static_assert(old_dim < kDim, "");
    return stride_[old_dim] * i;
  }
--- a/include/xgboost/metric.h
+++ b/include/xgboost/metric.h
@ -57,12 +57,8 @@ class Metric : public Configurable {
   * \brief evaluate a specific metric
   * \param preds prediction
   * \param info information, including label etc.
-   * \param distributed whether a call to Allreduce is needed to gather
-   *        the average statistics across all the node,
-   *        this is only supported by some metrics
   */
-  virtual double Eval(const HostDeviceVector<bst_float> &preds,
-                      const MetaInfo &info, bool distributed) = 0;
+  virtual double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info) = 0;
  /*! \return name of metric */
  virtual const char* Name() const = 0;
  /*! \brief virtual destructor */
--- a/include/xgboost/objective.h
+++ b/include/xgboost/objective.h
@ -103,8 +103,10 @@ class ObjFunction : public Configurable {
   * \param prediction Model prediction after transformation.
   * \param p_tree Tree that needs to be updated.
   */
-  virtual void UpdateTreeLeaf(HostDeviceVector<bst_node_t> const& position, MetaInfo const& info,
-                              HostDeviceVector<float> const& prediction, RegTree* p_tree) const {}
+  virtual void UpdateTreeLeaf(HostDeviceVector<bst_node_t> const& /*position*/,
+                              MetaInfo const& /*info*/,
+                              HostDeviceVector<float> const& /*prediction*/,
+                              RegTree* /*p_tree*/) const {}

  /*!
   * \brief Create an objective function according to name.
--- a/src/common/hist_util.h
+++ b/src/common/hist_util.h
@ -171,14 +171,14 @@ inline HistogramCuts SketchOnDMatrix(DMatrix* m, int32_t max_bins, int32_t n_thr

  if (!use_sorted) {
    HostSketchContainer container(max_bins, m->Info(), reduced, HostSketchContainer::UseGroup(info),
-                                  hessian, n_threads);
+                                  n_threads);
    for (auto const& page : m->GetBatches<SparsePage>()) {
      container.PushRowPage(page, info, hessian);
    }
    container.MakeCuts(&out);
  } else {
-    SortedSketchContainer container{
-        max_bins, m->Info(), reduced, HostSketchContainer::UseGroup(info), hessian, n_threads};
+    SortedSketchContainer container{max_bins, m->Info(), reduced,
+                                    HostSketchContainer::UseGroup(info), n_threads};
    for (auto const& page : m->GetBatches<SortedCSCPage>()) {
      container.PushColPage(page, info, hessian);
    }
--- a/src/common/partition_builder.h
+++ b/src/common/partition_builder.h
@ -168,8 +168,8 @@ class PartitionBuilder {
    const size_t n_left  = child_nodes_sizes.first;
    const size_t n_right = child_nodes_sizes.second;

-    SetNLeftElems(node_in_set, range.begin(), range.end(), n_left);
-    SetNRightElems(node_in_set, range.begin(), range.end(), n_right);
+    SetNLeftElems(node_in_set, range.begin(), n_left);
+    SetNRightElems(node_in_set, range.begin(), n_right);
  }

  /**
@ -188,8 +188,7 @@ class PartitionBuilder {
   */
  template <typename Pred>
  void PartitionRange(const size_t node_in_set, const size_t nid, common::Range1d range,
-                      bst_feature_t fidx, common::RowSetCollection* p_row_set_collection,
-                      Pred pred) {
+                      common::RowSetCollection* p_row_set_collection, Pred pred) {
    auto& row_set_collection = *p_row_set_collection;
    const size_t* p_ridx = row_set_collection[nid].begin;
    common::Span<const size_t> ridx(p_ridx + range.begin(), p_ridx + range.end());
@ -200,8 +199,8 @@ class PartitionBuilder {
    const size_t n_left = child_nodes_sizes.first;
    const size_t n_right = child_nodes_sizes.second;

-    this->SetNLeftElems(node_in_set, range.begin(), range.end(), n_left);
-    this->SetNRightElems(node_in_set, range.begin(), range.end(), n_right);
+    this->SetNLeftElems(node_in_set, range.begin(), n_left);
+    this->SetNRightElems(node_in_set, range.begin(), n_right);
  }

  // allocate thread local memory, should be called for each specific task
@ -223,12 +222,12 @@ class PartitionBuilder {
    return { mem_blocks_.at(task_idx)->Right(), end - begin };
  }

-  void SetNLeftElems(int nid, size_t begin, size_t end, size_t n_left) {
+  void SetNLeftElems(int nid, size_t begin, size_t n_left) {
    size_t task_idx = GetTaskIdx(nid, begin);
    mem_blocks_.at(task_idx)->n_left = n_left;
  }

-  void SetNRightElems(int nid, size_t begin, size_t end, size_t n_right) {
+  void SetNRightElems(int nid, size_t begin, size_t n_right) {
    size_t task_idx = GetTaskIdx(nid, begin);
    mem_blocks_.at(task_idx)->n_right = n_right;
  }
--- a/src/common/quantile.cc
+++ b/src/common/quantile.cc
@ -543,7 +543,7 @@ template class SketchContainerImpl<WXQuantileSketch<float, float>>;

 HostSketchContainer::HostSketchContainer(int32_t max_bins, MetaInfo const &info,
                                         std::vector<size_t> columns_size, bool use_group,
-                                         Span<float const> hessian, int32_t n_threads)
+                                         int32_t n_threads)
    : SketchContainerImpl{columns_size, max_bins, info.feature_types.ConstHostSpan(), use_group,
                          n_threads} {
  monitor_.Init(__func__);
--- a/src/common/quantile.h
+++ b/src/common/quantile.h
@ -774,7 +774,7 @@ class HostSketchContainer : public SketchContainerImpl<WQuantileSketch<float, fl

 public:
  HostSketchContainer(int32_t max_bins, MetaInfo const &info, std::vector<size_t> columns_size,
-                      bool use_group, Span<float const> hessian, int32_t n_threads);
+                      bool use_group, int32_t n_threads);
 };

 /**
@ -868,7 +868,7 @@ class SortedSketchContainer : public SketchContainerImpl<WXQuantileSketch<float,
 public:
  explicit SortedSketchContainer(int32_t max_bins, MetaInfo const &info,
                                 std::vector<size_t> columns_size, bool use_group,
-                                 Span<float const> hessian, int32_t n_threads)
+                                 int32_t n_threads)
      : SketchContainerImpl{columns_size, max_bins, info.feature_types.ConstHostSpan(), use_group,
                            n_threads} {
    monitor_.Init(__func__);
--- a/src/data/iterative_device_dmatrix.cu
+++ b/src/data/iterative_device_dmatrix.cu
@ -163,8 +163,7 @@ void IterativeDeviceDMatrix::Initialize(DataIterHandle iter_handle, float missin

 BatchSet<EllpackPage> IterativeDeviceDMatrix::GetEllpackBatches(const BatchParam& param) {
  CHECK(page_);
-  auto begin_iter =
-      BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(page_));
+  auto begin_iter = BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(page_));
  return BatchSet<EllpackPage>(begin_iter);
 }
 }  // namespace data
--- a/src/data/iterative_device_dmatrix.h
+++ b/src/data/iterative_device_dmatrix.h
@ -45,8 +45,8 @@ class IterativeDeviceDMatrix : public DMatrix {

  bool EllpackExists() const override { return true; }
  bool SparsePageExists() const override { return false; }
-  DMatrix *Slice(common::Span<int32_t const> ridxs) override {
-    LOG(FATAL) << "Slicing DMatrix is not supported for Device DMatrix.";
+  DMatrix *Slice(common::Span<int32_t const>) override {
+    LOG(FATAL) << "Slicing DMatrix is not supported for Quantile DMatrix.";
    return nullptr;
  }
  BatchSet<SparsePage> GetRowBatches() override {
--- a/src/data/proxy_dmatrix.h
+++ b/src/data/proxy_dmatrix.h
@ -84,7 +84,7 @@ class DMatrixProxy : public DMatrix {
  bool SingleColBlock() const override { return true; }
  bool EllpackExists() const override { return true; }
  bool SparsePageExists() const override { return false; }
-  DMatrix *Slice(common::Span<int32_t const> ridxs) override {
+  DMatrix* Slice(common::Span<int32_t const> /*ridxs*/) override {
    LOG(FATAL) << "Slicing DMatrix is not supported for Proxy DMatrix.";
    return nullptr;
  }
@ -100,7 +100,7 @@ class DMatrixProxy : public DMatrix {
    LOG(FATAL) << "Not implemented.";
    return BatchSet<SortedCSCPage>(BatchIterator<SortedCSCPage>(nullptr));
  }
-  BatchSet<EllpackPage> GetEllpackBatches(const BatchParam& param) override {
+  BatchSet<EllpackPage> GetEllpackBatches(const BatchParam&) override {
    LOG(FATAL) << "Not implemented.";
    return BatchSet<EllpackPage>(BatchIterator<EllpackPage>(nullptr));
  }
--- a/src/gbm/gbtree.cc
+++ b/src/gbm/gbtree.cc
@ -218,7 +218,7 @@ void CopyGradient(HostDeviceVector<GradientPair> const* in_gpair, int32_t n_thre
 }

 void GBTree::UpdateTreeLeaf(DMatrix const* p_fmat, HostDeviceVector<float> const& predictions,
-                            ObjFunction const* obj, size_t gidx,
+                            ObjFunction const* obj,
                            std::vector<std::unique_ptr<RegTree>>* p_trees) {
  CHECK(!updaters_.empty());
  if (!updaters_.back()->HasNodePosition()) {
@ -257,7 +257,7 @@ void GBTree::DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
  if (ngroup == 1) {
    std::vector<std::unique_ptr<RegTree>> ret;
    BoostNewTrees(in_gpair, p_fmat, 0, &ret);
-    UpdateTreeLeaf(p_fmat, predt->predictions, obj, 0, &ret);
+    UpdateTreeLeaf(p_fmat, predt->predictions, obj, &ret);
    const size_t num_new_trees = ret.size();
    new_trees.push_back(std::move(ret));
    auto v_predt = out.Slice(linalg::All(), 0);
@ -274,7 +274,7 @@ void GBTree::DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
      CopyGradient(in_gpair, ctx_->Threads(), ngroup, gid, &tmp);
      std::vector<std::unique_ptr<RegTree>> ret;
      BoostNewTrees(&tmp, p_fmat, gid, &ret);
-      UpdateTreeLeaf(p_fmat, predt->predictions, obj, gid, &ret);
+      UpdateTreeLeaf(p_fmat, predt->predictions, obj, &ret);
      const size_t num_new_trees = ret.size();
      new_trees.push_back(std::move(ret));
      auto v_predt = out.Slice(linalg::All(), gid);
@ -289,7 +289,7 @@ void GBTree::DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
  }

  monitor_.Stop("BoostNewTrees");
-  this->CommitModel(std::move(new_trees), p_fmat, predt);
+  this->CommitModel(std::move(new_trees));
 }

 void GBTree::InitUpdater(Args const& cfg) {
@ -378,9 +378,7 @@ void GBTree::BoostNewTrees(HostDeviceVector<GradientPair>* gpair, DMatrix* p_fma
  }
 }

-void GBTree::CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
-                         DMatrix* m,
-                         PredictionCacheEntry* predts) {
+void GBTree::CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees) {
  monitor_.Start("CommitModel");
  for (uint32_t gid = 0; gid < model_.learner_model_param->num_output_group; ++gid) {
    model_.CommitModel(std::move(new_trees[gid]), gid);
@ -490,9 +488,8 @@ void GBTree::Slice(int32_t layer_begin, int32_t layer_end, int32_t step,
           "want to update a portion of trees.";
  }

-  *out_of_bound = detail::SliceTrees(
-      layer_begin, layer_end, step, this->model_, tparam_, layer_trees,
-      [&](auto const &in_it, auto const &out_it) {
+  *out_of_bound = detail::SliceTrees(layer_begin, layer_end, step, this->model_, layer_trees,
+                                     [&](auto const& in_it, auto const& out_it) {
                                       auto new_tree =
                                           std::make_unique<RegTree>(*this->model_.trees.at(in_it));
                                       bst_group_t group = this->model_.tree_info[in_it];
@ -674,8 +671,7 @@ class Dart : public GBTree {
    auto p_dart = dynamic_cast<Dart*>(out);
    CHECK(p_dart);
    CHECK(p_dart->weight_drop_.empty());
-    detail::SliceTrees(
-        layer_begin, layer_end, step, model_, tparam_, this->LayerTrees(),
+    detail::SliceTrees(layer_begin, layer_end, step, model_, this->LayerTrees(),
                       [&](auto const& in_it, auto const&) {
                         p_dart->weight_drop_.push_back(this->weight_drop_.at(in_it));
                       });
@ -901,9 +897,7 @@ class Dart : public GBTree {

 protected:
  // commit new trees all at once
-  void
-  CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
-              DMatrix*, PredictionCacheEntry*) override {
+  void CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees) override {
    int num_new_trees = 0;
    for (uint32_t gid = 0; gid < model_.learner_model_param->num_output_group; ++gid) {
      num_new_trees += new_trees[gid].size();
--- a/src/gbm/gbtree.h
+++ b/src/gbm/gbtree.h
@ -162,8 +162,7 @@ inline std::pair<uint32_t, uint32_t> LayerToTree(gbm::GBTreeModel const &model,

 // Call fn for each pair of input output tree.  Return true if index is out of bound.
 template <typename Func>
-inline bool SliceTrees(int32_t layer_begin, int32_t layer_end, int32_t step,
-                       GBTreeModel const &model, GBTreeTrainParam const &tparam,
+bool SliceTrees(int32_t layer_begin, int32_t layer_end, int32_t step, GBTreeModel const& model,
                uint32_t layer_trees, Func fn) {
  uint32_t tree_begin, tree_end;
  std::tie(tree_begin, tree_end) = detail::LayerToTree(model, layer_begin, layer_end);
@ -206,8 +205,7 @@ class GBTree : public GradientBooster {
   * \brief Optionally update the leaf value.
   */
  void UpdateTreeLeaf(DMatrix const* p_fmat, HostDeviceVector<float> const& predictions,
-                      ObjFunction const* obj, size_t gidx,
-                      std::vector<std::unique_ptr<RegTree>>* p_trees);
+                      ObjFunction const* obj, std::vector<std::unique_ptr<RegTree>>* p_trees);

  /*! \brief Carry out one iteration of boosting */
  void DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
@ -325,7 +323,7 @@ class GBTree : public GradientBooster {
    };

    if (importance_type == "weight") {
-      add_score([&](auto const &p_tree, bst_node_t, bst_feature_t split) {
+      add_score([&](auto const&, bst_node_t, bst_feature_t split) {
        gain_map[split] = split_counts[split];
      });
    } else if (importance_type == "gain" || importance_type == "total_gain") {
@ -423,9 +421,7 @@ class GBTree : public GradientBooster {
                                                 DMatrix* f_dmat = nullptr) const;

  // commit new trees all at once
-  virtual void CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
-                           DMatrix* m,
-                           PredictionCacheEntry* predts);
+  virtual void CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees);

  // --- data structure ---
  GBTreeModel model_;
--- a/src/learner.cc
+++ b/src/learner.cc
@ -1234,8 +1234,7 @@ class LearnerImpl : public LearnerIO {

      obj_->EvalTransform(&out);
      for (auto& ev : metrics_) {
-        os << '\t' << data_names[i] << '-' << ev->Name() << ':'
-           << ev->Eval(out, m->Info(), tparam_.dsplit == DataSplitMode::kRow);
+        os << '\t' << data_names[i] << '-' << ev->Name() << ':' << ev->Eval(out, m->Info());
      }
    }

--- a/src/metric/auc.cc
+++ b/src/metric/auc.cc
@ -254,8 +254,7 @@ std::pair<double, uint32_t> RankingAUC(std::vector<float> const &predts,

 template <typename Curve>
 class EvalAUC : public Metric {
-  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
-              bool distributed) override {
+  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info) override {
    double auc {0};
    if (tparam_->gpu_id != GenericParameter::kCpuId) {
      preds.SetDevice(tparam_->gpu_id);
--- a/src/metric/auc.cu
+++ b/src/metric/auc.cu
@ -312,10 +312,8 @@ void SegmentedReduceAUC(common::Span<size_t const> d_unique_idx,
 * up each class in all kernels.
 */
 template <bool scale, typename Fn>
-double GPUMultiClassAUCOVR(common::Span<float const> predts,
-                           MetaInfo const &info, int32_t device,
-                           common::Span<uint32_t> d_class_ptr, size_t n_classes,
-                           std::shared_ptr<DeviceAUCCache> cache, Fn area_fn) {
+double GPUMultiClassAUCOVR(MetaInfo const &info, int32_t device, common::Span<uint32_t> d_class_ptr,
+                           size_t n_classes, std::shared_ptr<DeviceAUCCache> cache, Fn area_fn) {
  dh::safe_cuda(cudaSetDevice(device));
  /**
   * Sorted idx
@ -478,8 +476,7 @@ double GPUMultiClassROCAUC(common::Span<float const> predts,
                              double tp, size_t /*class_id*/) {
    return TrapezoidArea(fp_prev, fp, tp_prev, tp);
  };
-  return GPUMultiClassAUCOVR<true>(predts, info, device, dh::ToSpan(class_ptr),
-                                   n_classes, cache, fn);
+  return GPUMultiClassAUCOVR<true>(info, device, dh::ToSpan(class_ptr), n_classes, cache, fn);
 }

 namespace {
@ -704,8 +701,7 @@ double GPUMultiClassPRAUC(common::Span<float const> predts,
    return detail::CalcDeltaPRAUC(fp_prev, fp, tp_prev, tp,
                                  d_totals[class_id].first);
  };
-  return GPUMultiClassAUCOVR<false>(predts, info, device, d_class_ptr,
-                                    n_classes, cache, fn);
+  return GPUMultiClassAUCOVR<false>(info, device, d_class_ptr, n_classes, cache, fn);
 }

 template <typename Fn>
--- a/src/metric/elementwise_metric.cu
+++ b/src/metric/elementwise_metric.cu
@ -178,8 +178,7 @@ class PseudoErrorLoss : public Metric {
    out["pseudo_huber_param"] = ToJson(param_);
  }

-  double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info,
-              bool distributed) override {
+  double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info) override {
    CHECK_EQ(info.labels.Shape(0), info.num_row_);
    auto labels = info.labels.View(tparam_->gpu_id);
    preds.SetDevice(tparam_->gpu_id);
@ -197,7 +196,7 @@ class PseudoErrorLoss : public Metric {
          return std::make_tuple(v, wt);
        });
    double dat[2]{result.Residue(), result.Weights()};
-    if (distributed) {
+    if (rabit::IsDistributed()) {
      rabit::Allreduce<rabit::op::Sum>(dat, 2);
    }
    return EvalRowMAPE::GetFinal(dat[0], dat[1]);
@ -342,8 +341,7 @@ struct EvalEWiseBase : public Metric {
  EvalEWiseBase() = default;
  explicit EvalEWiseBase(char const* policy_param) : policy_{policy_param} {}

-  double Eval(HostDeviceVector<bst_float> const& preds, const MetaInfo& info,
-              bool distributed) override {
+  double Eval(HostDeviceVector<bst_float> const& preds, const MetaInfo& info) override {
    CHECK_EQ(preds.Size(), info.labels.Size())
        << "label and prediction size not match, "
        << "hint: use merror or mlogloss for multi-class classification";
@ -367,10 +365,7 @@ struct EvalEWiseBase : public Metric {
        });

    double dat[2]{result.Residue(), result.Weights()};
-
-    if (distributed) {
    rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
    return Policy::GetFinal(dat[0], dat[1]);
  }

--- a/src/metric/multiclass_metric.cu
+++ b/src/metric/multiclass_metric.cu
@ -167,8 +167,7 @@ class MultiClassMetricsReduction {
 */
 template<typename Derived>
 struct EvalMClassBase : public Metric {
-  double Eval(const HostDeviceVector<float> &preds, const MetaInfo &info,
-              bool distributed) override {
+  double Eval(const HostDeviceVector<float> &preds, const MetaInfo &info) override {
    if (info.labels.Size() == 0) {
      CHECK_EQ(preds.Size(), 0);
    } else {
@ -186,9 +185,7 @@ struct EvalMClassBase : public Metric {
      dat[0] = result.Residue();
      dat[1] = result.Weights();
    }
-    if (distributed) {
    rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
    return Derived::GetFinal(dat[0], dat[1]);
  }
  /*!
--- a/src/metric/rank_metric.cc
+++ b/src/metric/rank_metric.cc
@ -102,9 +102,8 @@ struct EvalAMS : public Metric {
    name_ = os.str();
  }

-  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
-              bool distributed) override {
-    CHECK(!distributed) << "metric AMS do not support distributed evaluation";
+  double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info) override {
+    CHECK(!rabit::IsDistributed()) << "metric AMS do not support distributed evaluation";
    using namespace std;  // NOLINT(*)

    const auto ndata = static_cast<bst_omp_uint>(info.labels.Size());
@ -161,8 +160,7 @@ struct EvalRank : public Metric, public EvalRankConfig {
  std::unique_ptr<xgboost::Metric> rank_gpu_;

 public:
-  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
-              bool distributed) override {
+  double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info) override {
    CHECK_EQ(preds.Size(), info.labels.Size())
        << "label size predict size not match";

@ -185,7 +183,7 @@ struct EvalRank : public Metric, public EvalRankConfig {
        rank_gpu_.reset(GPUMetric::CreateGPUMetric(this->Name(), tparam_));
      }
      if (rank_gpu_) {
-        sum_metric = rank_gpu_->Eval(preds, info, distributed);
+        sum_metric = rank_gpu_->Eval(preds, info);
      }
    }

@ -218,7 +216,7 @@ struct EvalRank : public Metric, public EvalRankConfig {
      exc.Rethrow();
    }

-    if (distributed) {
+    if (rabit::IsDistributed()) {
      double dat[2]{sum_metric, static_cast<double>(ngroups)};
      // approximately estimate the metric using mean
      rabit::Allreduce<rabit::op::Sum>(dat, 2);
@ -342,9 +340,8 @@ struct EvalMAP : public EvalRank {
 struct EvalCox : public Metric {
 public:
  EvalCox() = default;
-  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
-              bool distributed) override {
-    CHECK(!distributed) << "Cox metric does not support distributed evaluation";
+  double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info) override {
+    CHECK(!rabit::IsDistributed()) << "Cox metric does not support distributed evaluation";
    using namespace std;  // NOLINT(*)

    const auto ndata = static_cast<bst_omp_uint>(info.labels.Size());
--- a/src/metric/rank_metric.cu
+++ b/src/metric/rank_metric.cu
@ -29,8 +29,7 @@ DMLC_REGISTRY_FILE_TAG(rank_metric_gpu);
 template <typename EvalMetricT>
 struct EvalRankGpu : public GPUMetric, public EvalRankConfig {
 public:
-  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
-              bool distributed) override {
+  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info) override {
    // Sanity check is done by the caller
    std::vector<unsigned> tgptr(2, 0);
    tgptr[1] = static_cast<unsigned>(preds.Size());
--- a/src/metric/survival_metric.cu
+++ b/src/metric/survival_metric.cu
@ -206,20 +206,15 @@ template <typename Policy> struct EvalEWiseSurvivalBase : public Metric {
    CHECK(tparam_);
  }

-  double Eval(const HostDeviceVector<float> &preds, const MetaInfo &info,
-              bool distributed) override {
+  double Eval(const HostDeviceVector<float>& preds, const MetaInfo& info) override {
    CHECK_EQ(preds.Size(), info.labels_lower_bound_.Size());
    CHECK_EQ(preds.Size(), info.labels_upper_bound_.Size());
    CHECK(tparam_);
-    auto result =
-        reducer_.Reduce(*tparam_, info.weights_, info.labels_lower_bound_,
+    auto result = reducer_.Reduce(*tparam_, info.weights_, info.labels_lower_bound_,
                                  info.labels_upper_bound_, preds);

-    double dat[2] {result.Residue(), result.Weights()};
-
-    if (distributed) {
+    double dat[2]{result.Residue(), result.Weights()};
    rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
    return Policy::GetFinal(dat[0], dat[1]);
  }

@ -240,10 +235,9 @@ struct AFTNLogLikDispatcher : public Metric {
    return "aft-nloglik";
  }

-  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
-              bool distributed) override {
+  double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info) override {
    CHECK(metric_) << "AFT metric must be configured first, with distribution type and scale";
-    return metric_->Eval(preds, info, distributed);
+    return metric_->Eval(preds, info);
  }

  void Configure(const Args& args) override {
--- a/src/tree/gpu_hist/row_partitioner.cuh
+++ b/src/tree/gpu_hist/row_partitioner.cuh
@ -116,7 +116,7 @@ class RowPartitioner {
    Segment segment = ridx_segments_.at(nidx);  // rows belongs to node nidx
    auto d_ridx = ridx_.CurrentSpan();
    auto d_position = position_.CurrentSpan();
-    if (left_counts_.size() <= nidx) {
+    if (left_counts_.size() <= static_cast<size_t>(nidx)) {
      left_counts_.resize((nidx * 2) + 1);
      thrust::fill(left_counts_.begin(), left_counts_.end(), 0);
    }
--- a/src/tree/hist/evaluate_splits.h
+++ b/src/tree/hist/evaluate_splits.h
@ -203,8 +203,8 @@ class HistEvaluator {
  // Returns the sum of gradients corresponding to the data points that contains
  // a non-missing value for the particular feature fid.
  template <int d_step>
-  GradStats EnumerateSplit(common::HistogramCuts const &cut, common::Span<size_t const> sorted_idx,
-                           const common::GHistRow &hist, bst_feature_t fidx, bst_node_t nidx,
+  GradStats EnumerateSplit(common::HistogramCuts const &cut, const common::GHistRow &hist,
+                           bst_feature_t fidx, bst_node_t nidx,
                           TreeEvaluator::SplitEvaluator<TrainParam> const &evaluator,
                           SplitEntry *p_best) const {
    static_assert(d_step == +1 || d_step == -1, "Invalid step.");
@ -333,9 +333,9 @@ class HistEvaluator {
            EnumeratePart<-1>(cut, sorted_idx, histogram, fidx, nidx, evaluator, best);
          }
        } else {
-          auto grad_stats = EnumerateSplit<+1>(cut, {}, histogram, fidx, nidx, evaluator, best);
+          auto grad_stats = EnumerateSplit<+1>(cut, histogram, fidx, nidx, evaluator, best);
          if (SplitContainsMissingValues(grad_stats, snode_[nidx])) {
-            EnumerateSplit<-1>(cut, {}, histogram, fidx, nidx, evaluator, best);
+            EnumerateSplit<-1>(cut, histogram, fidx, nidx, evaluator, best);
          }
        }
      }
@ -440,7 +440,7 @@ template <typename Partitioner, typename ExpandEntry>
 void UpdatePredictionCacheImpl(GenericParameter const *ctx, RegTree const *p_last_tree,
                               std::vector<Partitioner> const &partitioner,
                               HistEvaluator<ExpandEntry> const &hist_evaluator,
-                               TrainParam const &param, linalg::VectorView<float> out_preds) {
+                               linalg::VectorView<float> out_preds) {
  CHECK_GT(out_preds.Size(), 0U);

  CHECK(p_last_tree);
--- a/src/tree/updater_approx.cc
+++ b/src/tree/updater_approx.cc
@ -116,7 +116,7 @@ class GloablApproxBuilder {
    // Caching prediction seems redundant for approx tree method, as sketching takes up
    // majority of training time.
    CHECK_EQ(out_preds.Size(), data->Info().num_row_);
-    UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, evaluator_, param_, out_preds);
+    UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, evaluator_, out_preds);
    monitor_->Stop(__func__);
  }

--- a/src/tree/updater_approx.h
+++ b/src/tree/updater_approx.h
@ -83,7 +83,7 @@ class ApproxRowPartitioner {
      const size_t task_id = partition_builder_.GetTaskIdx(node_in_set, r.begin());
      partition_builder_.AllocateForTask(task_id);
      partition_builder_.PartitionRange(
-          node_in_set, nid, r, fidx, &row_set_collection_, [&](size_t row_id) {
+          node_in_set, nid, r, &row_set_collection_, [&](size_t row_id) {
            auto cut_value = SearchCutValue(row_id, fidx, index, cut_ptrs, cut_values);
            if (std::isnan(cut_value)) {
              return candidate.split.DefaultLeft();
--- a/src/tree/updater_gpu_hist.cu
+++ b/src/tree/updater_gpu_hist.cu
@ -563,7 +563,7 @@ struct GPUHistMakerDevice {
    // when processing a large batch
    this->AllReduceHist(hist_nidx.at(0), reducer, hist_nidx.size());

-    for (int i = 0; i < subtraction_nidx.size(); i++) {
+    for (size_t i = 0; i < subtraction_nidx.size(); i++) {
      auto build_hist_nidx = hist_nidx.at(i);
      auto subtraction_trick_nidx = subtraction_nidx.at(i);
      auto parent_nidx = candidates.at(i).nid;
--- a/src/tree/updater_quantile_hist.cc
+++ b/src/tree/updater_quantile_hist.cc
@ -257,7 +257,7 @@ bool QuantileHistMaker::Builder::UpdatePredictionCache(DMatrix const *data,
  }
  monitor_->Start(__func__);
  CHECK_EQ(out_preds.Size(), data->Info().num_row_);
-  UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, *evaluator_, param_, out_preds);
+  UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, *evaluator_, out_preds);
  monitor_->Stop(__func__);
  return true;
 }
--- a/tests/cpp/common/test_device_helpers.cu
+++ b/tests/cpp/common/test_device_helpers.cu
@ -67,7 +67,7 @@ TEST(SegmentedUnique, Basic) {
  CHECK_EQ(n_uniques, 5);

  std::vector<float> values_sol{0.1f, 0.2f, 0.3f, 0.62448811531066895f, 0.4f};
-  for (auto i = 0 ; i < values_sol.size(); i ++) {
+  for (size_t i = 0 ; i < values_sol.size(); i ++) {
    ASSERT_EQ(d_vals_out[i], values_sol[i]);
  }

@ -84,7 +84,7 @@ TEST(SegmentedUnique, Basic) {
      d_segs_out.data().get(), d_vals_out.data().get(),
      thrust::equal_to<float>{});
  ASSERT_EQ(n_uniques, values.size());
-  for (auto i = 0 ; i < values.size(); i ++) {
+  for (size_t i = 0 ; i < values.size(); i ++) {
    ASSERT_EQ(d_vals_out[i], values[i]);
  }
 }
--- a/tests/cpp/common/test_linalg.cc
+++ b/tests/cpp/common/test_linalg.cc
@ -315,10 +315,10 @@ TEST(Linalg, Popc) {
 TEST(Linalg, Stack) {
  Tensor<float, 3> l{{2, 3, 4}, kCpuId};
  ElementWiseTransformHost(l.View(kCpuId), omp_get_max_threads(),
-                           [=](size_t i, float v) { return i; });
+                           [=](size_t i, float) { return i; });
  Tensor<float, 3> r_0{{2, 3, 4}, kCpuId};
  ElementWiseTransformHost(r_0.View(kCpuId), omp_get_max_threads(),
-                           [=](size_t i, float v) { return i; });
+                           [=](size_t i, float) { return i; });

  Stack(&l, r_0);

--- a/tests/cpp/common/test_partition_builder.cc
+++ b/tests/cpp/common/test_partition_builder.cc
@ -50,8 +50,8 @@ TEST(PartitionBuilder, BasicTest) {
        right[i] = left_total + value_right++;
      }

-      builder.SetNLeftElems(nid, begin, end, n_left);
-      builder.SetNRightElems(nid, begin, end, n_right);
+      builder.SetNLeftElems(nid, begin, n_left);
+      builder.SetNRightElems(nid, begin, n_right);
    }
  }
  builder.CalculateRowOffsets();
--- a/tests/cpp/common/test_quantile.cc
+++ b/tests/cpp/common/test_quantile.cc
@ -77,7 +77,7 @@ void TestDistributedQuantile(size_t rows, size_t cols) {
  std::vector<float> hessian(rows, 1.0);
  auto hess = Span<float const>{hessian};

-  ContainerType<use_column> sketch_distributed(n_bins, m->Info(), column_size, false, hess,
+  ContainerType<use_column> sketch_distributed(n_bins, m->Info(), column_size, false,
                                               OmpGetNumThreads(0));

  if (use_column) {
@ -98,7 +98,7 @@ void TestDistributedQuantile(size_t rows, size_t cols) {
  CHECK_EQ(rabit::GetWorldSize(), 1);
  std::for_each(column_size.begin(), column_size.end(), [=](auto& size) { size *= world; });
  m->Info().num_row_ = world * rows;
-  ContainerType<use_column> sketch_on_single_node(n_bins, m->Info(), column_size, false, hess,
+  ContainerType<use_column> sketch_on_single_node(n_bins, m->Info(), column_size, false,
                                                  OmpGetNumThreads(0));
  m->Info().num_row_ = rows;

@ -190,7 +190,7 @@ TEST(Quantile, SameOnAllWorkers) {

  constexpr size_t kRows = 1000, kCols = 100;
  RunWithSeedsAndBins(
-      kRows, [=](int32_t seed, size_t n_bins, MetaInfo const &info) {
+      kRows, [=](int32_t seed, size_t n_bins, MetaInfo const&) {
        auto rank = rabit::GetRank();
        HostDeviceVector<float> storage;
        std::vector<FeatureType> ft(kCols);
--- a/tests/cpp/common/test_span.h
+++ b/tests/cpp/common/test_span.h
@ -36,7 +36,7 @@ struct TestTestStatus {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    SPAN_ASSERT_TRUE(false, status_);
  }
 };
@ -49,7 +49,7 @@ struct TestAssignment {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    Span<float> s1;

    float arr[] = {3, 4, 5};
@ -71,7 +71,7 @@ struct TestBeginEnd {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    float arr[16];
    InitializeRange(arr, arr + 16);

@ -93,7 +93,7 @@ struct TestRBeginREnd {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    float arr[16];
    InitializeRange(arr, arr + 16);

@ -121,7 +121,7 @@ struct TestObservers {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    // empty
    {
      float *arr = nullptr;
@ -148,7 +148,7 @@ struct TestCompare {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    float lhs_arr[16], rhs_arr[16];
    InitializeRange(lhs_arr, lhs_arr + 16);
    InitializeRange(rhs_arr, rhs_arr + 16);
@ -178,7 +178,7 @@ struct TestIterConstruct {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index.
    Span<float>::iterator it1;
    Span<float>::iterator it2;
    SPAN_ASSERT_TRUE(it1 == it2, status_);
@ -197,7 +197,7 @@ struct TestIterRef {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    float arr[16];
    InitializeRange(arr, arr + 16);

@ -215,7 +215,7 @@ struct TestIterCalculate {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    float arr[16];
    InitializeRange(arr, arr + 16);

@ -278,7 +278,7 @@ struct TestAsBytes {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    float arr[16];
    InitializeRange(arr, arr + 16);

@ -313,7 +313,7 @@ struct TestAsWritableBytes {
  XGBOOST_DEVICE void operator()() {
    this->operator()(0);
  }
-  XGBOOST_DEVICE void operator()(int _idx) {
+  XGBOOST_DEVICE void operator()(size_t) {  // size_t for CUDA index
    float arr[16];
    InitializeRange(arr, arr + 16);

--- a/tests/cpp/common/test_threading_utils.cc
+++ b/tests/cpp/common/test_threading_utils.cc
@ -34,9 +34,8 @@ TEST(ParallelFor2d, Test) {

  // working space is matrix of size (kDim1 x kDim2)
  std::vector<int> matrix(kDim1 * kDim2, 0);
-  BlockedSpace2d space(kDim1, [&](size_t i) {
-      return kDim2;
-  }, kGrainSize);
+  BlockedSpace2d space(
+      kDim1, [&](size_t) { return kDim2; }, kGrainSize);

  auto old = omp_get_max_threads();
  omp_set_num_threads(4);
--- a/tests/cpp/helpers.cc
+++ b/tests/cpp/helpers.cc
@ -167,7 +167,7 @@ double GetMultiMetricEval(xgboost::Metric* metric,
  info.weights_.HostVector() = weights;
  info.group_ptr_ = groups;

-  return metric->Eval(preds, info, false);
+  return metric->Eval(preds, info);
 }

 namespace xgboost {
@ -653,8 +653,6 @@ class RMMAllocator {};

 void DeleteRMMResource(RMMAllocator* r) {}

-RMMAllocatorPtr SetUpRMMResourceForCppTests(int argc, char** argv) {
-  return {nullptr, DeleteRMMResource};
-}
+RMMAllocatorPtr SetUpRMMResourceForCppTests(int, char**) { return {nullptr, DeleteRMMResource}; }
 #endif  // !defined(XGBOOST_USE_RMM) || XGBOOST_USE_RMM != 1
 }  // namespace xgboost
--- a/tests/cpp/helpers.cu
+++ b/tests/cpp/helpers.cu
@ -29,9 +29,7 @@ int CudaArrayIterForTest::Next() {
 }


-std::shared_ptr<DMatrix> RandomDataGenerator::GenerateDeviceDMatrix(bool with_label,
-                                                                    bool float_label,
-                                                                    size_t classes) {
+std::shared_ptr<DMatrix> RandomDataGenerator::GenerateDeviceDMatrix() {
  CudaArrayIterForTest iter{this->sparsity_, this->rows_, this->cols_, 1};
  auto m = std::make_shared<data::IterativeDeviceDMatrix>(
      &iter, iter.Proxy(), Reset, Next, std::numeric_limits<float>::quiet_NaN(),
--- a/tests/cpp/helpers.h
+++ b/tests/cpp/helpers.h
@ -296,9 +296,7 @@ class RandomDataGenerator {
                                           bool float_label = true,
                                           size_t classes = 1) const;
 #if defined(XGBOOST_USE_CUDA)
-  std::shared_ptr<DMatrix> GenerateDeviceDMatrix(bool with_label = false,
-                                                 bool float_label = true,
-                                                 size_t classes = 1);
+  std::shared_ptr<DMatrix> GenerateDeviceDMatrix();
 #endif
 };

--- a/tests/cpp/metric/test_auc.cc
+++ b/tests/cpp/metric/test_auc.cc
@ -22,10 +22,10 @@ TEST(Metric, DeclareUnifiedTest(BinaryAUC)) {
  // Invalid dataset
  MetaInfo info;
  info.labels = linalg::Tensor<float, 2>{{0.0f, 0.0f}, {2}, -1};
-  float auc = metric->Eval({1, 1}, info, false);
+  float auc = metric->Eval({1, 1}, info);
  ASSERT_TRUE(std::isnan(auc));
  *info.labels.Data() = HostDeviceVector<float>{};
-  auc = metric->Eval(HostDeviceVector<float>{}, info, false);
+  auc = metric->Eval(HostDeviceVector<float>{}, info);
  ASSERT_TRUE(std::isnan(auc));

  EXPECT_NEAR(GetMetricEval(metric, {0, 1, 0, 1}, {0, 1, 0, 1}), 1.0f, 1e-10);
--- a/tests/cpp/metric/test_elementwise_metric.cc
+++ b/tests/cpp/metric/test_elementwise_metric.cc
@ -36,9 +36,9 @@ inline void CheckDeterministicMetricElementWise(StringView name, int32_t device)
    h_labels[i] = dist(&lcg);
  }

-  auto result = metric->Eval(predts, info, false);
+  auto result = metric->Eval(predts, info);
  for (size_t i = 0; i < 8; ++i) {
-    ASSERT_EQ(metric->Eval(predts, info, false), result);
+    ASSERT_EQ(metric->Eval(predts, info), result);
  }
 }
 }  // anonymous namespace
--- a/tests/cpp/metric/test_multiclass_metric.cc
+++ b/tests/cpp/metric/test_multiclass_metric.cc
@ -35,9 +35,9 @@ inline void CheckDeterministicMetricMultiClass(StringView name, int32_t device)
    }
  }

-  auto result = metric->Eval(predts, info, false);
+  auto result = metric->Eval(predts, info);
  for (size_t i = 0; i < 8; ++i) {
-    ASSERT_EQ(metric->Eval(predts, info, false), result);
+    ASSERT_EQ(metric->Eval(predts, info), result);
  }
 }
 }  // namespace xgboost
--- a/tests/cpp/metric/test_survival_metric.cu
+++ b/tests/cpp/metric/test_survival_metric.cu
@ -40,9 +40,9 @@ inline void CheckDeterministicMetricElementWise(StringView name, int32_t device)
    h_upper[i] = 10;
  }

-  auto result = metric->Eval(predts, info, false);
+  auto result = metric->Eval(predts, info);
  for (size_t i = 0; i < 8; ++i) {
-    ASSERT_EQ(metric->Eval(predts, info, false), result);
+    ASSERT_EQ(metric->Eval(predts, info), result);
  }
 }
 }  // anonymous namespace
@ -72,7 +72,7 @@ TEST(Metric, DeclareUnifiedTest(AFTNegLogLik)) {
    std::unique_ptr<Metric> metric(Metric::Create("aft-nloglik", &lparam));
    metric->Configure({ {"aft_loss_distribution", test_case.dist_type},
                        {"aft_loss_distribution_scale", "1.0"} });
-    EXPECT_NEAR(metric->Eval(preds, info, false), test_case.reference_value, 1e-4);
+    EXPECT_NEAR(metric->Eval(preds, info), test_case.reference_value, 1e-4);
  }
 }

@ -87,15 +87,15 @@ TEST(Metric, DeclareUnifiedTest(IntervalRegressionAccuracy)) {
  HostDeviceVector<bst_float> preds(4, std::log(60.0f));

  std::unique_ptr<Metric> metric(Metric::Create("interval-regression-accuracy", &lparam));
-  EXPECT_FLOAT_EQ(metric->Eval(preds, info, false), 0.75f);
+  EXPECT_FLOAT_EQ(metric->Eval(preds, info), 0.75f);
  info.labels_lower_bound_.HostVector()[2] = 70.0f;
-  EXPECT_FLOAT_EQ(metric->Eval(preds, info, false), 0.50f);
+  EXPECT_FLOAT_EQ(metric->Eval(preds, info), 0.50f);
  info.labels_upper_bound_.HostVector()[2] = std::numeric_limits<bst_float>::infinity();
-  EXPECT_FLOAT_EQ(metric->Eval(preds, info, false), 0.50f);
+  EXPECT_FLOAT_EQ(metric->Eval(preds, info), 0.50f);
  info.labels_upper_bound_.HostVector()[3] = std::numeric_limits<bst_float>::infinity();
-  EXPECT_FLOAT_EQ(metric->Eval(preds, info, false), 0.50f);
+  EXPECT_FLOAT_EQ(metric->Eval(preds, info), 0.50f);
  info.labels_lower_bound_.HostVector()[0] = 70.0f;
-  EXPECT_FLOAT_EQ(metric->Eval(preds, info, false), 0.25f);
+  EXPECT_FLOAT_EQ(metric->Eval(preds, info), 0.25f);

  CheckDeterministicMetricElementWise(StringView{"interval-regression-accuracy"}, GPUIDX);
 }
--- a/tests/cpp/objective/test_ranking_obj_gpu.cu
+++ b/tests/cpp/objective/test_ranking_obj_gpu.cu
@ -170,7 +170,7 @@ TEST(Objective, NDCGLambdaWeightComputerTest) {
  EXPECT_EQ(hgroup_dcgs.size(), segment_label_sorter->GetNumGroups());
  std::vector<float> hsorted_labels(segment_label_sorter->GetNumItems());
  dh::CopyDeviceSpanToVector(&hsorted_labels, segment_label_sorter->GetItemsSpan());
-  for (auto i = 0; i < hgroup_dcgs.size(); ++i) {
+  for (size_t i = 0; i < hgroup_dcgs.size(); ++i) {
    // Compute group DCG value on CPU and compare
    auto gbegin = hgroups[i];
    auto gend = hgroups[i + 1];
@ -244,7 +244,7 @@ TEST(Objective, ComputeAndCompareMAPStatsTest) {
  std::vector<uint32_t> hgroups(segment_label_sorter->GetNumGroups() + 1);
  dh::CopyDeviceSpanToVector(&hgroups, segment_label_sorter->GetGroupsSpan());

-  for (auto i = 0; i < hgroups.size() - 1; ++i) {
+  for (size_t i = 0; i < hgroups.size() - 1; ++i) {
    auto gbegin = hgroups[i];
    auto gend = hgroups[i + 1];
    std::vector<xgboost::obj::ListEntry> lst_entry;
--- a/tests/cpp/predictor/test_gpu_predictor.cu
+++ b/tests/cpp/predictor/test_gpu_predictor.cu
@ -66,10 +66,7 @@ TEST(GPUPredictor, EllpackBasic) {
  size_t constexpr kCols {8};
  for (size_t bins = 2; bins < 258; bins += 16) {
    size_t rows = bins * 16;
-    auto p_m = RandomDataGenerator{rows, kCols, 0.0}
-         .Bins(bins)
-         .Device(0)
-         .GenerateDeviceDMatrix(true);
+    auto p_m = RandomDataGenerator{rows, kCols, 0.0}.Bins(bins).Device(0).GenerateDeviceDMatrix();
    ASSERT_FALSE(p_m->PageExists<SparsePage>());
    TestPredictionFromGradientIndex<EllpackPage>("gpu_predictor", rows, kCols, p_m);
    TestPredictionFromGradientIndex<EllpackPage>("gpu_predictor", bins, kCols, p_m);
@ -78,10 +75,8 @@ TEST(GPUPredictor, EllpackBasic) {

 TEST(GPUPredictor, EllpackTraining) {
  size_t constexpr kRows { 128 }, kCols { 16 }, kBins { 64 };
-  auto p_ellpack = RandomDataGenerator{kRows, kCols, 0.0}
-       .Bins(kBins)
-       .Device(0)
-       .GenerateDeviceDMatrix(true);
+  auto p_ellpack =
+      RandomDataGenerator{kRows, kCols, 0.0}.Bins(kBins).Device(0).GenerateDeviceDMatrix();
  HostDeviceVector<float> storage(kRows * kCols);
  auto columnar = RandomDataGenerator{kRows, kCols, 0.0}
       .Device(0)
--- a/tests/cpp/tree/test_constraints.cu
+++ b/tests/cpp/tree/test_constraints.cu
@ -94,8 +94,8 @@ TEST(GPUFeatureInteractionConstraint, Init) {
    tree::TrainParam param = GetParameter();
    param.interaction_constraints = R"([[0, 1, 3], [3, 5, 6]])";
    FConstraintWrapper constraints(param, kFeatures);
-    std::vector<int32_t> h_sets {0, 0, 0, 1, 1, 1};
-    std::vector<int32_t> h_sets_ptr {0, 1, 2, 2, 4, 4, 5, 6};
+    std::vector<bst_feature_t> h_sets {0, 0, 0, 1, 1, 1};
+    std::vector<size_t> h_sets_ptr {0, 1, 2, 2, 4, 4, 5, 6};
    auto d_sets = constraints.GetDSets();
    ASSERT_EQ(h_sets.size(), d_sets.size());
    auto d_sets_ptr = constraints.GetDSetsPtr();
--- a/tests/cpp/tree/test_gpu_hist.cu
+++ b/tests/cpp/tree/test_gpu_hist.cu
@ -346,7 +346,7 @@ TEST(GpuHist, UniformSampling) {
  // Make sure the predictions are the same.
  auto preds_h = preds.ConstHostVector();
  auto preds_sampling_h = preds_sampling.ConstHostVector();
-  for (int i = 0; i < kRows; i++) {
+  for (size_t i = 0; i < kRows; i++) {
    EXPECT_NEAR(preds_h[i], preds_sampling_h[i], 1e-8);
  }
 }
@ -376,7 +376,7 @@ TEST(GpuHist, GradientBasedSampling) {
  // Make sure the predictions are the same.
  auto preds_h = preds.ConstHostVector();
  auto preds_sampling_h = preds_sampling.ConstHostVector();
-  for (int i = 0; i < kRows; i++) {
+  for (size_t i = 0; i < kRows; i++) {
    EXPECT_NEAR(preds_h[i], preds_sampling_h[i], 1e-3);
  }
 }
@ -409,7 +409,7 @@ TEST(GpuHist, ExternalMemory) {
  // Make sure the predictions are the same.
  auto preds_h = preds.ConstHostVector();
  auto preds_ext_h = preds_ext.ConstHostVector();
-  for (int i = 0; i < kRows; i++) {
+  for (size_t i = 0; i < kRows; i++) {
    EXPECT_NEAR(preds_h[i], preds_ext_h[i], 1e-6);
  }
 }
@ -451,7 +451,7 @@ TEST(GpuHist, ExternalMemoryWithSampling) {
  // Make sure the predictions are the same.
  auto preds_h = preds.ConstHostVector();
  auto preds_ext_h = preds_ext.ConstHostVector();
-  for (int i = 0; i < kRows; i++) {
+  for (size_t i = 0; i < kRows; i++) {
    ASSERT_NEAR(preds_h[i], preds_ext_h[i], 1e-3);
  }
 }