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.
dmlc · Jun 6, 2022 · 1a33b50 · 1a33b50
1 parent d48123d
commit 1a33b50
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Showing 46 changed files with 149 additions and 189 deletions.
diff --git 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>)

diff --git 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.";
   }
   /*!

diff --git 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);
   }

diff --git 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;
   }

diff --git 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 */

diff --git 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.

diff --git 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);
     }

diff --git 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;
   }

diff --git 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__);

diff --git 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__);

diff --git 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

diff --git 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 {

diff --git 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));
   }

diff --git 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);
@@ -490,15 +488,14 @@ 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) {
-        auto new_tree =
-            std::make_unique<RegTree>(*this->model_.trees.at(in_it));
-        bst_group_t group = this->model_.tree_info[in_it];
-        out_trees.at(out_it) = std::move(new_tree);
-        out_trees_info.at(out_it) = group;
-      });
+  *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];
+                                       out_trees.at(out_it) = std::move(new_tree);
+                                       out_trees_info.at(out_it) = group;
+                                     });
 }
 
 void GBTree::PredictBatch(DMatrix* p_fmat,
@@ -674,11 +671,10 @@ 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(),
-        [&](auto const& in_it, auto const&) {
-          p_dart->weight_drop_.push_back(this->weight_drop_.at(in_it));
-        });
+    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));
+                       });
   }
 
   void SaveModel(Json *p_out) const override {
@@ -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();

diff --git a/src/gbm/gbtree.h b/src/gbm/gbtree.h
@@ -162,9 +162,8 @@ 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,
-                       uint32_t layer_trees, Func fn) {
+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);
   if (tree_end > model.trees.size()) {
@@ -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_;

diff --git 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());
       }
     }
 

diff --git 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);

diff --git 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>