Fix clang-tidy warnings. (#4149)

* Upgrade gtest for clang-tidy.
* Use CMake to install GTest instead of mv.
* Don't enforce clang-tidy to return 0 due to errors in thrust.
* Add a small test for tidy itself.

* Reformat.

src/c_api/c_api.cc

@@ -87,7 +87,10 @@ class Booster {
     initialized_ = true;
   }
 
- public:
+  bool IsInitialized() const { return initialized_; }
+  void Intialize() { initialized_ = true; }
+
+ private:
   bool configured_;
   bool initialized_;
   std::unique_ptr<Learner> learner_;
@@ -1153,7 +1156,7 @@ XGB_DLL int XGBoosterLoadRabitCheckpoint(BoosterHandle handle,
   auto* bst = static_cast<Booster*>(handle);
   *version = rabit::LoadCheckPoint(bst->learner());
   if (*version != 0) {
-    bst->initialized_ = true;
+    bst->Intialize();
   }
   API_END();
 }

src/common/column_matrix.h

@@ -42,7 +42,9 @@ class Column {
   uint32_t GetBaseIdx() const { return index_base_; }
   ColumnType GetType() const { return type_; }
   size_t GetRowIdx(size_t idx) const {
-    return type_ == ColumnType::kDenseColumn ? idx : row_ind_[idx];
+    // clang-tidy worries that row_ind_ might be a nullptr, which is possible,
+    // but low level structure is not safe anyway.
+    return type_ == ColumnType::kDenseColumn ? idx : row_ind_[idx];  // NOLINT
   }
   bool IsMissing(size_t idx) const {
     return index_[idx] == std::numeric_limits<uint32_t>::max();

src/common/device_helpers.cuh

@@ -772,7 +772,7 @@ template <typename T>
 typename std::iterator_traits<T>::value_type SumReduction(
     dh::CubMemory &tmp_mem, T in, int nVals) {
   using ValueT = typename std::iterator_traits<T>::value_type;
-  size_t tmpSize;
+  size_t tmpSize {0};
   ValueT *dummy_out = nullptr;
   dh::safe_cuda(cub::DeviceReduce::Sum(nullptr, tmpSize, in, dummy_out, nVals));
   // Allocate small extra memory for the return value

src/common/hist_util.cc

@@ -548,7 +548,7 @@ void GHistBuilder::BuildBlockHist(const std::vector<GradientPair>& gpair,
   const size_t rest = nrows % kUnroll;
 
 #if defined(_OPENMP)
-  const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
+  const auto nthread = static_cast<bst_omp_uint>(this->nthread_);  // NOLINT
 #endif  // defined(_OPENMP)
   tree::GradStats* p_hist = hist.data();
 
@@ -594,7 +594,7 @@ void GHistBuilder::SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow pa
   const uint32_t rest = nbins % kUnroll;
 
 #if defined(_OPENMP)
-  const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
+  const auto nthread = static_cast<bst_omp_uint>(this->nthread_);  // NOLINT
 #endif  // defined(_OPENMP)
   tree::GradStats* p_self = self.data();
   tree::GradStats* p_sibling = sibling.data();

src/common/hist_util.cu

@@ -24,13 +24,14 @@ namespace common {
 
 using WXQSketch = HistCutMatrix::WXQSketch;
 
-__global__ void find_cuts_k
+__global__ void FindCutsK
 (WXQSketch::Entry* __restrict__ cuts, const bst_float* __restrict__ data,
  const float* __restrict__ cum_weights, int nsamples, int ncuts) {
   // ncuts < nsamples
   int icut = threadIdx.x + blockIdx.x * blockDim.x;
-  if (icut >= ncuts)
+  if (icut >= ncuts) {
     return;
+  }
   WXQSketch::Entry v;
   int isample = 0;
   if (icut == 0) {
@@ -55,7 +56,7 @@ struct IsNotNaN {
   __device__ bool operator()(float a) const { return !isnan(a); }
 };
 
-__global__ void unpack_features_k
+__global__ void UnpackFeaturesK
 (float* __restrict__ fvalues, float* __restrict__ feature_weights,
  const size_t* __restrict__ row_ptrs, const float* __restrict__ weights,
  Entry* entries, size_t nrows_array, int ncols, size_t row_begin_ptr,
@@ -75,7 +76,7 @@ __global__ void unpack_features_k
   // if and only if it is also written to features
   if (!isnan(entry.fvalue) && (weights == nullptr || !isnan(weights[irow]))) {
     fvalues[ind] = entry.fvalue;
-    if (feature_weights != nullptr) {
+    if (feature_weights != nullptr && weights != nullptr) {
       feature_weights[ind] = weights[irow];
     }
   }
@@ -84,7 +85,7 @@ __global__ void unpack_features_k
 // finds quantiles on the GPU
 struct GPUSketcher {
   // manage memory for a single GPU
-  struct DeviceShard {
+  class DeviceShard {
     int device_;
     bst_uint row_begin_;  // The row offset for this shard
     bst_uint row_end_;
@@ -110,6 +111,7 @@ struct GPUSketcher {
     thrust::device_vector<size_t> num_elements_;
     thrust::device_vector<char> tmp_storage_;
 
+   public:
     DeviceShard(int device, bst_uint row_begin, bst_uint row_end,
                 tree::TrainParam param) :
       device_(device), row_begin_(row_begin), row_end_(row_end),
@@ -268,7 +270,7 @@ struct GPUSketcher {
       } else if (n_cuts_cur_[icol] > 0) {
         // if more elements than cuts: use binary search on cumulative weights
         int block = 256;
-        find_cuts_k<<<dh::DivRoundUp(n_cuts_cur_[icol], block), block>>>
+        FindCutsK<<<dh::DivRoundUp(n_cuts_cur_[icol], block), block>>>
           (cuts_d_.data().get() + icol * n_cuts_, fvalues_cur_.data().get(),
            weights2_.data().get(), n_unique, n_cuts_cur_[icol]);
         dh::safe_cuda(cudaGetLastError());  // NOLINT
@@ -309,7 +311,7 @@ struct GPUSketcher {
       dim3 block3(64, 4, 1);
       dim3 grid3(dh::DivRoundUp(batch_nrows, block3.x),
                  dh::DivRoundUp(num_cols_, block3.y), 1);
-      unpack_features_k<<<grid3, block3>>>
+      UnpackFeaturesK<<<grid3, block3>>>
         (fvalues_.data().get(), has_weights_ ? feature_weights_.data().get() : nullptr,
          row_ptrs_.data().get() + batch_row_begin,
          has_weights_ ? weights_.data().get() : nullptr, entries_.data().get(),
@@ -340,6 +342,10 @@ struct GPUSketcher {
         SketchBatch(row_batch, info, gpu_batch);
       }
     }
+
+    void GetSummary(WXQSketch::SummaryContainer *summary, size_t const icol) {
+      sketches_[icol].GetSummary(summary);
+    }
   };
 
   void Sketch(const SparsePage& batch, const MetaInfo& info,
@@ -368,8 +374,8 @@ struct GPUSketcher {
     WXQSketch::SummaryContainer summary;
     for (int icol = 0; icol < num_cols; ++icol) {
       sketches[icol].Init(batch.Size(), 1.0 / (8 * param_.max_bin));
-      for (int shard = 0; shard < shards_.size(); ++shard) {
+      for (auto &shard : shards_) {
-        shards_[shard]->sketches_[icol].GetSummary(&summary);
+        shard->GetSummary(&summary, icol);
         sketches[icol].PushSummary(summary);
       }
     }
@@ -381,6 +387,7 @@ struct GPUSketcher {
     dist_ = GPUDistribution::Block(GPUSet::All(param_.gpu_id, param_.n_gpus, n_rows));
   }
 
+ private:
   std::vector<std::unique_ptr<DeviceShard>> shards_;
   tree::TrainParam param_;
   GPUDistribution dist_;

src/common/hist_util.h

@@ -38,6 +38,7 @@ struct HistCutMatrix {
   void Init(std::vector<WXQSketch>* sketchs, uint32_t max_num_bins);
 
   HistCutMatrix();
+  size_t NumBins() const { return row_ptr.back(); }
 
  protected:
   virtual size_t SearchGroupIndFromBaseRow(

src/common/host_device_vector.cc

@@ -18,6 +18,11 @@ struct HostDeviceVectorImpl {
   explicit HostDeviceVectorImpl(size_t size, T v) : data_h_(size, v), distribution_() {}
   HostDeviceVectorImpl(std::initializer_list<T> init) : data_h_(init), distribution_() {}
   explicit HostDeviceVectorImpl(std::vector<T>  init) : data_h_(std::move(init)), distribution_() {}
+
+  std::vector<T>& Vec() { return data_h_; }
+  GPUDistribution& Dist() { return distribution_; }
+
+ private:
   std::vector<T> data_h_;
   GPUDistribution distribution_;
 };
@@ -64,14 +69,14 @@ HostDeviceVector<T>& HostDeviceVector<T>::operator=(const HostDeviceVector<T>& o
 }
 
 template <typename T>
-size_t HostDeviceVector<T>::Size() const { return impl_->data_h_.size(); }
+size_t HostDeviceVector<T>::Size() const { return impl_->Vec().size(); }
 
 template <typename T>
 GPUSet HostDeviceVector<T>::Devices() const { return GPUSet::Empty(); }
 
 template <typename T>
 const GPUDistribution& HostDeviceVector<T>::Distribution() const {
-  return impl_->distribution_;
+  return impl_->Dist();
 }
 
 template <typename T>
@@ -93,16 +98,16 @@ common::Span<const T> HostDeviceVector<T>::ConstDeviceSpan(int device) const {
 }
 
 template <typename T>
-std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->data_h_; }
+std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->Vec(); }
 
 template <typename T>
 const std::vector<T>& HostDeviceVector<T>::ConstHostVector() const {
-  return impl_->data_h_;
+  return impl_->Vec();
 }
 
 template <typename T>
 void HostDeviceVector<T>::Resize(size_t new_size, T v) {
-  impl_->data_h_.resize(new_size, v);
+  impl_->Vec().resize(new_size, v);
 }
 
 template <typename T>

src/common/host_device_vector.cu

@@ -23,10 +23,10 @@ void SetCudaSetDeviceHandler(void (*handler)(int)) {
 // wrapper over access with useful methods
 class Permissions {
   GPUAccess access_;
-  explicit Permissions(GPUAccess access) : access_(access) {}
+  explicit Permissions(GPUAccess access) : access_{access} {}
 
  public:
-  Permissions() : access_(GPUAccess::kNone) {}
+  Permissions() : access_{GPUAccess::kNone} {}
   explicit Permissions(bool perm)
     : access_(perm ? GPUAccess::kWrite : GPUAccess::kNone) {}
 
@@ -46,8 +46,8 @@ template <typename T>
 struct HostDeviceVectorImpl {
   struct DeviceShard {
     DeviceShard()
-        : proper_size_(0), device_(-1), start_(0), perm_d_(false),
+      : proper_size_{0}, device_{-1}, start_{0}, perm_d_{false},
-        cached_size_(~0), vec_(nullptr) {}
+        cached_size_{static_cast<size_t>(~0)}, vec_{nullptr} {}
 
     void Init(HostDeviceVectorImpl<T>* vec, int device) {
       if (vec_ == nullptr) { vec_ = vec; }
@@ -154,6 +154,13 @@ struct HostDeviceVectorImpl {
       }
     }
 
+    T*     Raw()                    { return data_.data().get(); }
+    size_t Start()            const { return start_; }
+    size_t DataSize()         const { return data_.size(); }
+    Permissions& Perm()             { return perm_d_; }
+    Permissions const& Perm() const { return perm_d_; }
+
+   private:
     int device_;
     thrust::device_vector<T> data_;
     // cached vector size
@@ -216,20 +223,20 @@ struct HostDeviceVectorImpl {
   T* DevicePointer(int device) {
     CHECK(distribution_.devices_.Contains(device));
     LazySyncDevice(device, GPUAccess::kWrite);
-    return shards_.at(distribution_.devices_.Index(device)).data_.data().get();
+    return shards_.at(distribution_.devices_.Index(device)).Raw();
   }
 
   const T* ConstDevicePointer(int device) {
     CHECK(distribution_.devices_.Contains(device));
     LazySyncDevice(device, GPUAccess::kRead);
-    return shards_.at(distribution_.devices_.Index(device)).data_.data().get();
+    return shards_.at(distribution_.devices_.Index(device)).Raw();
   }
 
   common::Span<T> DeviceSpan(int device) {
     GPUSet devices = distribution_.devices_;
     CHECK(devices.Contains(device));
     LazySyncDevice(device, GPUAccess::kWrite);
-    return {shards_.at(devices.Index(device)).data_.data().get(),
+    return {shards_.at(devices.Index(device)).Raw(),
           static_cast<typename common::Span<T>::index_type>(DeviceSize(device))};
   }
 
@@ -237,20 +244,21 @@ struct HostDeviceVectorImpl {
     GPUSet devices = distribution_.devices_;
     CHECK(devices.Contains(device));
     LazySyncDevice(device, GPUAccess::kRead);
-    return {shards_.at(devices.Index(device)).data_.data().get(),
+    using SpanInd = typename common::Span<const T>::index_type;
-        static_cast<typename common::Span<const T>::index_type>(DeviceSize(device))};
+    return {shards_.at(devices.Index(device)).Raw(),
+          static_cast<SpanInd>(DeviceSize(device))};
   }
 
   size_t DeviceSize(int device) {
     CHECK(distribution_.devices_.Contains(device));
     LazySyncDevice(device, GPUAccess::kRead);
-    return shards_.at(distribution_.devices_.Index(device)).data_.size();
+    return shards_.at(distribution_.devices_.Index(device)).DataSize();
   }
 
   size_t DeviceStart(int device) {
     CHECK(distribution_.devices_.Contains(device));
     LazySyncDevice(device, GPUAccess::kRead);
-    return shards_.at(distribution_.devices_.Index(device)).start_;
+    return shards_.at(distribution_.devices_.Index(device)).Start();
   }
 
   thrust::device_ptr<T> tbegin(int device) {  // NOLINT
@@ -293,7 +301,7 @@ struct HostDeviceVectorImpl {
     }
   }
 
-  void Fill(T v) {
+  void Fill(T v) {  // NOLINT
     if (perm_h_.CanWrite()) {
       std::fill(data_h_.begin(), data_h_.end(), v);
     } else {
@@ -389,7 +397,7 @@ struct HostDeviceVectorImpl {
     if (perm_h_.CanRead()) {
       // data is present, just need to deny access to the device
       dh::ExecuteIndexShards(&shards_, [&](int idx, DeviceShard& shard) {
-          shard.perm_d_.DenyComplementary(access);
+          shard.Perm().DenyComplementary(access);
         });
       perm_h_.Grant(access);
       return;
@@ -412,9 +420,10 @@ struct HostDeviceVectorImpl {
   bool DeviceCanAccess(int device, GPUAccess access) {
     GPUSet devices = distribution_.Devices();
     if (!devices.Contains(device)) { return false; }
-    return shards_.at(devices.Index(device)).perm_d_.CanAccess(access);
+    return shards_.at(devices.Index(device)).Perm().CanAccess(access);
   }
 
+ private:
   std::vector<T> data_h_;
   Permissions perm_h_;
   // the total size of the data stored on the devices

src/common/math.h

@@ -7,6 +7,8 @@
 #ifndef XGBOOST_COMMON_MATH_H_
 #define XGBOOST_COMMON_MATH_H_
 
+#include <xgboost/base.h>
+
 #include <utility>
 #include <vector>
 #include <cmath>

src/common/span.h

@@ -622,8 +622,8 @@ XGBOOST_DEVICE auto as_writable_bytes(Span<T, E> s) __span_noexcept ->  // NOLIN
   return {reinterpret_cast<byte*>(s.data()), s.size_bytes()};
 }
 
-}  // namespace common NOLINT
+}  // namespace common
-}  // namespace xgboost NOLINT
+}  // namespace xgboost
 
 #if defined(_MSC_VER) &&_MSC_VER < 1910
 #undef constexpr

src/linear/updater_coordinate.cc

@@ -30,8 +30,8 @@ class CoordinateUpdater : public LinearUpdater {
       tparam_.InitAllowUnknown(args)
     };
     cparam_.InitAllowUnknown(rest);
-    selector.reset(FeatureSelector::Create(tparam_.feature_selector));
+    selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
-    monitor.Init("CoordinateUpdater");
+    monitor_.Init("CoordinateUpdater");
   }
   void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
               gbm::GBLinearModel *model, double sum_instance_weight) override {
@@ -48,20 +48,20 @@ class CoordinateUpdater : public LinearUpdater {
                                  dbias, &in_gpair->HostVector(), p_fmat);
     }
     // prepare for updating the weights
-    selector->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
+    selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
                     tparam_.reg_alpha_denorm,
                     tparam_.reg_lambda_denorm, cparam_.top_k);
     // update weights
     for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
       for (unsigned i = 0U; i < model->param.num_feature; i++) {
-        int fidx = selector->NextFeature
+        int fidx = selector_->NextFeature
           (i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
            tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
         if (fidx < 0) break;
         this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), p_fmat, model);
       }
     }
-    monitor.Stop("UpdateFeature");
+    monitor_.Stop("UpdateFeature");
   }
 
   inline void UpdateFeature(int fidx, int group_idx, std::vector<GradientPair> *in_gpair,
@@ -78,11 +78,12 @@ class CoordinateUpdater : public LinearUpdater {
     UpdateResidualParallel(fidx, group_idx, ngroup, dw, in_gpair, p_fmat);
   }
 
+ private:
   CoordinateParam cparam_;
   // training parameter
   LinearTrainParam tparam_;
-  std::unique_ptr<FeatureSelector> selector;
+  std::unique_ptr<FeatureSelector> selector_;
-  common::Monitor monitor;
+  common::Monitor monitor_;
 };
 
 XGBOOST_REGISTER_LINEAR_UPDATER(CoordinateUpdater, "coord_descent")

src/linear/updater_gpu_coordinate.cu

@@ -62,7 +62,7 @@ class DeviceShard {
       auto column_end =
           std::lower_bound(col.cbegin(), col.cend(),
                            xgboost::Entry(row_end, 0.0f), cmp);
-      column_segments.push_back(
+      column_segments.emplace_back(
           std::make_pair(column_begin - col.cbegin(), column_end - col.cbegin()));
       row_ptr_.push_back(row_ptr_.back() + (column_end - column_begin));
     }
@@ -154,13 +154,13 @@ class GPUCoordinateUpdater : public LinearUpdater {
   void Init(
       const std::vector<std::pair<std::string, std::string>> &args) override {
     tparam_.InitAllowUnknown(args);
-    selector.reset(FeatureSelector::Create(tparam_.feature_selector));
+    selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
-    monitor.Init("GPUCoordinateUpdater");
+    monitor_.Init("GPUCoordinateUpdater");
   }
 
   void LazyInitShards(DMatrix *p_fmat,
                       const gbm::GBLinearModelParam &model_param) {
-    if (!shards.empty()) return;
+    if (!shards_.empty()) return;
 
     dist_ = GPUDistribution::Block(GPUSet::All(tparam_.gpu_id, tparam_.n_gpus,
                                                p_fmat->Info().num_row_));
@@ -183,9 +183,9 @@ class GPUCoordinateUpdater : public LinearUpdater {
     CHECK(p_fmat->SingleColBlock());
     SparsePage const& batch = *(p_fmat->GetColumnBatches().begin());
 
-    shards.resize(n_devices);
+    shards_.resize(n_devices);
     // Create device shards
-    dh::ExecuteIndexShards(&shards,
+    dh::ExecuteIndexShards(&shards_,
                            [&](int i, std::unique_ptr<DeviceShard>& shard) {
         shard = std::unique_ptr<DeviceShard>(
             new DeviceShard(devices.DeviceId(i), batch, row_segments[i],
@@ -196,38 +196,38 @@ class GPUCoordinateUpdater : public LinearUpdater {
   void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
               gbm::GBLinearModel *model, double sum_instance_weight) override {
     tparam_.DenormalizePenalties(sum_instance_weight);
-    monitor.Start("LazyInitShards");
+    monitor_.Start("LazyInitShards");
     this->LazyInitShards(p_fmat, model->param);
-    monitor.Stop("LazyInitShards");
+    monitor_.Stop("LazyInitShards");
 
-    monitor.Start("UpdateGpair");
+    monitor_.Start("UpdateGpair");
     // Update gpair
-    dh::ExecuteIndexShards(&shards, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
+    dh::ExecuteIndexShards(&shards_, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
       if (!shard->IsEmpty()) {
         shard->UpdateGpair(in_gpair->ConstHostVector(), model->param);
       }
     });
-    monitor.Stop("UpdateGpair");
+    monitor_.Stop("UpdateGpair");
 
-    monitor.Start("UpdateBias");
+    monitor_.Start("UpdateBias");
     this->UpdateBias(p_fmat, model);
-    monitor.Stop("UpdateBias");
+    monitor_.Stop("UpdateBias");
     // prepare for updating the weights
-    selector->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
+    selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
                      tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm,
                      coord_param_.top_k);
-    monitor.Start("UpdateFeature");
+    monitor_.Start("UpdateFeature");
     for (auto group_idx = 0; group_idx < model->param.num_output_group;
          ++group_idx) {
       for (auto i = 0U; i < model->param.num_feature; i++) {
-        auto fidx = selector->NextFeature(
+        auto fidx = selector_->NextFeature(
             i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
             tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
         if (fidx < 0) break;
         this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), model);
       }
     }
-    monitor.Stop("UpdateFeature");
+    monitor_.Stop("UpdateFeature");
   }
 
   void UpdateBias(DMatrix *p_fmat, gbm::GBLinearModel *model) {
@@ -235,7 +235,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
          ++group_idx) {
       // Get gradient
       auto grad = dh::ReduceShards<GradientPair>(
-          &shards, [&](std::unique_ptr<DeviceShard> &shard) {
+          &shards_, [&](std::unique_ptr<DeviceShard> &shard) {
             if (!shard->IsEmpty()) {
               GradientPair result =
                   shard->GetBiasGradient(group_idx,
@@ -251,7 +251,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
       model->bias()[group_idx] += dbias;
 
       // Update residual
-    dh::ExecuteIndexShards(&shards, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
+    dh::ExecuteIndexShards(&shards_, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
         if (!shard->IsEmpty()) {
           shard->UpdateBiasResidual(dbias, group_idx,
                                     model->param.num_output_group);
@@ -266,7 +266,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
     bst_float &w = (*model)[fidx][group_idx];
     // Get gradient
     auto grad = dh::ReduceShards<GradientPair>(
-        &shards, [&](std::unique_ptr<DeviceShard> &shard) {
+        &shards_, [&](std::unique_ptr<DeviceShard> &shard) {
           if (!shard->IsEmpty()) {
             return shard->GetGradient(group_idx, model->param.num_output_group,
                                       fidx);
@@ -280,7 +280,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
                                                  tparam_.reg_lambda_denorm));
     w += dw;
 
-    dh::ExecuteIndexShards(&shards, [&](int idx,
+    dh::ExecuteIndexShards(&shards_, [&](int idx,
                                         std::unique_ptr<DeviceShard> &shard) {
       if (!shard->IsEmpty()) {
         shard->UpdateResidual(dw, group_idx, model->param.num_output_group, fidx);
@@ -288,14 +288,15 @@ class GPUCoordinateUpdater : public LinearUpdater {
     });
   }
 
+ private:
   // training parameter
   LinearTrainParam tparam_;
   CoordinateParam coord_param_;
   GPUDistribution dist_;
-  std::unique_ptr<FeatureSelector> selector;
+  std::unique_ptr<FeatureSelector> selector_;
-  common::Monitor monitor;
+  common::Monitor monitor_;
 
-  std::vector<std::unique_ptr<DeviceShard>> shards;
+  std::vector<std::unique_ptr<DeviceShard>> shards_;
 };
 
 XGBOOST_REGISTER_LINEAR_UPDATER(GPUCoordinateUpdater, "gpu_coord_descent")

src/metric/elementwise_metric.cu

@@ -27,10 +27,15 @@ namespace metric {
 // tag the this file, used by force static link later.
 DMLC_REGISTRY_FILE_TAG(elementwise_metric);
 
-struct PackedReduceResult {
+template <typename EvalRow>
+class MetricsReduction {
+ public:
+  class PackedReduceResult {
     double residue_sum_;
     double weights_sum_;
+    friend MetricsReduction;
 
+   public:
     XGBOOST_DEVICE PackedReduceResult() : residue_sum_{0}, weights_sum_{0} {}
     XGBOOST_DEVICE PackedReduceResult(double residue, double weight) :
         residue_sum_{residue}, weights_sum_{weight} {}
@@ -40,10 +45,10 @@ struct PackedReduceResult {
       return PackedReduceResult { residue_sum_ + other.residue_sum_,
             weights_sum_ + other.weights_sum_ };
     }
+    double Residue() const { return residue_sum_; }
+    double Weights() const { return weights_sum_; }
   };
 
-template <typename EvalRow>
-class MetricsReduction {
  public:
   explicit MetricsReduction(EvalRow policy) :
     policy_(std::move(policy)) {}
@@ -346,10 +351,10 @@ struct EvalEWiseBase : public Metric {
     // Dealing with ndata < n_gpus.
     GPUSet devices = GPUSet::All(param_.gpu_id, param_.n_gpus, ndata);
 
-    PackedReduceResult result =
+    auto result =
         reducer_.Reduce(devices, info.weights_, info.labels_, preds);
 
-    double dat[2] { result.residue_sum_, result.weights_sum_ };
+    double dat[2] { result.Residue(), result.Weights() };
     if (distributed) {
       rabit::Allreduce<rabit::op::Sum>(dat, 2);
     }

src/metric/multiclass_metric.cc

@@ -79,6 +79,8 @@ struct EvalMClassBase : public Metric {
   inline static bst_float GetFinal(bst_float esum, bst_float wsum) {
     return esum / wsum;
   }
+
+ private:
   // used to store error message
   const char *error_msg_;
 };

src/predictor/gpu_predictor.cu

@@ -48,7 +48,7 @@ void IncrementOffset(IterT begin_itr, IterT end_itr, size_t amount) {
  */
 struct DevicePredictionNode {
   XGBOOST_DEVICE DevicePredictionNode()
-      : fidx(-1), left_child_idx(-1), right_child_idx(-1) {}
+      : fidx{-1}, left_child_idx{-1}, right_child_idx{-1} {}
 
   union NodeValue {
     float leaf_weight;
@@ -238,10 +238,10 @@ class GPUPredictor : public xgboost::Predictor {
   }
 
   struct DeviceShard {
-    DeviceShard() : device_(-1) {}
+    DeviceShard() : device_{-1} {}
     void Init(int device) {
       this->device_ = device;
-      max_shared_memory_bytes = dh::MaxSharedMemory(this->device_);
+      max_shared_memory_bytes_ = dh::MaxSharedMemory(this->device_);
      }
     void PredictInternal
     (const SparsePage& batch, const MetaInfo& info,
@@ -251,18 +251,18 @@ class GPUPredictor : public xgboost::Predictor {
      const thrust::host_vector<DevicePredictionNode>& h_nodes,
      size_t tree_begin, size_t tree_end) {
       dh::safe_cuda(cudaSetDevice(device_));
-      nodes.resize(h_nodes.size());
+      nodes_.resize(h_nodes.size());
-      dh::safe_cuda(cudaMemcpyAsync(dh::Raw(nodes), h_nodes.data(),
+      dh::safe_cuda(cudaMemcpyAsync(dh::Raw(nodes_), h_nodes.data(),
                                     sizeof(DevicePredictionNode) * h_nodes.size(),
                                     cudaMemcpyHostToDevice));
-      tree_segments.resize(h_tree_segments.size());
+      tree_segments_.resize(h_tree_segments.size());
 
-      dh::safe_cuda(cudaMemcpyAsync(dh::Raw(tree_segments), h_tree_segments.data(),
+      dh::safe_cuda(cudaMemcpyAsync(dh::Raw(tree_segments_), h_tree_segments.data(),
                                     sizeof(size_t) * h_tree_segments.size(),
                                     cudaMemcpyHostToDevice));
-      tree_group.resize(model.tree_info.size());
+      tree_group_.resize(model.tree_info.size());
 
-      dh::safe_cuda(cudaMemcpyAsync(dh::Raw(tree_group), model.tree_info.data(),
+      dh::safe_cuda(cudaMemcpyAsync(dh::Raw(tree_group_), model.tree_info.data(),
                                     sizeof(int) * model.tree_info.size(),
                                     cudaMemcpyHostToDevice));
 
@@ -275,7 +275,7 @@ class GPUPredictor : public xgboost::Predictor {
       int shared_memory_bytes = static_cast<int>
         (sizeof(float) * info.num_col_ * BLOCK_THREADS);
       bool use_shared = true;
-      if (shared_memory_bytes > max_shared_memory_bytes) {
+      if (shared_memory_bytes > max_shared_memory_bytes_) {
         shared_memory_bytes = 0;
         use_shared = false;
       }
@@ -284,17 +284,18 @@ class GPUPredictor : public xgboost::Predictor {
                                                  data_distr.Devices().Index(device_));
 
       PredictKernel<BLOCK_THREADS><<<GRID_SIZE, BLOCK_THREADS, shared_memory_bytes>>>
-        (dh::ToSpan(nodes), predictions->DeviceSpan(device_), dh::ToSpan(tree_segments),
+        (dh::ToSpan(nodes_), predictions->DeviceSpan(device_), dh::ToSpan(tree_segments_),
-         dh::ToSpan(tree_group), batch.offset.DeviceSpan(device_),
+         dh::ToSpan(tree_group_), batch.offset.DeviceSpan(device_),
          batch.data.DeviceSpan(device_), tree_begin, tree_end, info.num_col_,
          num_rows, entry_start, use_shared, model.param.num_output_group);
     }
 
+   private:
     int device_;
-    thrust::device_vector<DevicePredictionNode> nodes;
+    thrust::device_vector<DevicePredictionNode> nodes_;
-    thrust::device_vector<size_t> tree_segments;
+    thrust::device_vector<size_t> tree_segments_;
-    thrust::device_vector<int> tree_group;
+    thrust::device_vector<int> tree_group_;
-    size_t max_shared_memory_bytes;
+    size_t max_shared_memory_bytes_;
   };
 
   void DevicePredictInternal(DMatrix* dmat,
@@ -325,13 +326,12 @@ class GPUPredictor : public xgboost::Predictor {
 
     for (const auto &batch : dmat->GetRowBatches()) {
       CHECK_EQ(i_batch, 0) << "External memory not supported";
-      size_t n_rows = batch.offset.Size() - 1;
       // out_preds have been resharded and resized in InitOutPredictions()
       batch.offset.Reshard(GPUDistribution::Overlap(devices_, 1));
       std::vector<size_t> device_offsets;
       DeviceOffsets(batch.offset, &device_offsets);
       batch.data.Reshard(GPUDistribution::Explicit(devices_, device_offsets));
-      dh::ExecuteIndexShards(&shards, [&](int idx, DeviceShard& shard) {
+      dh::ExecuteIndexShards(&shards_, [&](int idx, DeviceShard& shard) {
         shard.PredictInternal(batch, dmat->Info(), out_preds, model,
                               h_tree_segments, h_nodes, tree_begin, tree_end);
       });
@@ -340,13 +340,13 @@ class GPUPredictor : public xgboost::Predictor {
   }
 
  public:
-  GPUPredictor() : cpu_predictor(Predictor::Create("cpu_predictor")) {}
+  GPUPredictor() : cpu_predictor_(Predictor::Create("cpu_predictor")) {}
 
   void PredictBatch(DMatrix* dmat, HostDeviceVector<bst_float>* out_preds,
                     const gbm::GBTreeModel& model, int tree_begin,
                     unsigned ntree_limit = 0) override {
     GPUSet devices = GPUSet::All(
-        param.gpu_id, param.n_gpus, dmat->Info().num_row_);
+        param_.gpu_id, param_.n_gpus, dmat->Info().num_row_);
     ConfigureShards(devices);
 
     if (this->PredictFromCache(dmat, out_preds, model, ntree_limit)) {
@@ -427,12 +427,12 @@ class GPUPredictor : public xgboost::Predictor {
                        std::vector<bst_float>* out_preds,
                        const gbm::GBTreeModel& model, unsigned ntree_limit,
                        unsigned root_index) override {
-    cpu_predictor->PredictInstance(inst, out_preds, model, root_index);
+    cpu_predictor_->PredictInstance(inst, out_preds, model, root_index);
   }
   void PredictLeaf(DMatrix* p_fmat, std::vector<bst_float>* out_preds,
                    const gbm::GBTreeModel& model,
                    unsigned ntree_limit) override {
-    cpu_predictor->PredictLeaf(p_fmat, out_preds, model, ntree_limit);
+    cpu_predictor_->PredictLeaf(p_fmat, out_preds, model, ntree_limit);
   }
 
   void PredictContribution(DMatrix* p_fmat,
@@ -440,7 +440,7 @@ class GPUPredictor : public xgboost::Predictor {
                            const gbm::GBTreeModel& model, unsigned ntree_limit,
                            bool approximate, int condition,
                            unsigned condition_feature) override {
-    cpu_predictor->PredictContribution(p_fmat, out_contribs, model, ntree_limit,
+    cpu_predictor_->PredictContribution(p_fmat, out_contribs, model, ntree_limit,
                                        approximate, condition,
                                        condition_feature);
   }
@@ -450,17 +450,17 @@ class GPUPredictor : public xgboost::Predictor {
                                        const gbm::GBTreeModel& model,
                                        unsigned ntree_limit,
                                        bool approximate) override {
-    cpu_predictor->PredictInteractionContributions(p_fmat, out_contribs, model,
+    cpu_predictor_->PredictInteractionContributions(p_fmat, out_contribs, model,
                                                    ntree_limit, approximate);
   }
 
   void Init(const std::vector<std::pair<std::string, std::string>>& cfg,
             const std::vector<std::shared_ptr<DMatrix>>& cache) override {
     Predictor::Init(cfg, cache);
-    cpu_predictor->Init(cfg, cache);
+    cpu_predictor_->Init(cfg, cache);
-    param.InitAllowUnknown(cfg);
+    param_.InitAllowUnknown(cfg);
 
-    GPUSet devices = GPUSet::All(param.gpu_id, param.n_gpus);
+    GPUSet devices = GPUSet::All(param_.gpu_id, param_.n_gpus);
     ConfigureShards(devices);
   }
 
@@ -470,16 +470,16 @@ class GPUPredictor : public xgboost::Predictor {
     if (devices_ == devices) return;
 
     devices_ = devices;
-    shards.clear();
+    shards_.clear();
-    shards.resize(devices_.Size());
+    shards_.resize(devices_.Size());
-    dh::ExecuteIndexShards(&shards, [=](size_t i, DeviceShard& shard){
+    dh::ExecuteIndexShards(&shards_, [=](size_t i, DeviceShard& shard){
         shard.Init(devices_.DeviceId(i));
       });
   }
 
-  GPUPredictionParam param;
+  GPUPredictionParam param_;
-  std::unique_ptr<Predictor> cpu_predictor;
+  std::unique_ptr<Predictor> cpu_predictor_;
-  std::vector<DeviceShard> shards;
+  std::vector<DeviceShard> shards_;
   GPUSet devices_;
 };
 

src/tree/updater_colmaker.cc

@@ -77,7 +77,7 @@ class ColMaker: public TreeUpdater {
     /*! \brief current best solution */
     SplitEntry best;
     // constructor
-    NodeEntry() : root_gain(0.0f), weight(0.0f) {}
+    NodeEntry() : root_gain{0.0f}, weight{0.0f} {}
   };
   // actual builder that runs the algorithm
   class Builder {
@@ -596,7 +596,8 @@ class ColMaker: public TreeUpdater {
       // start enumeration
       const auto num_features = static_cast<bst_omp_uint>(feat_set.size());
 #if defined(_OPENMP)
-      const int batch_size = std::max(static_cast<int>(num_features / this->nthread_ / 32), 1);
+      const int batch_size =  // NOLINT
+          std::max(static_cast<int>(num_features / this->nthread_ / 32), 1);
 #endif  // defined(_OPENMP)
       int poption = param_.parallel_option;
       if (poption == 2) {

src/tree/updater_gpu.cu

@@ -102,7 +102,7 @@ struct AddByKey {
  * @param instIds instance index buffer
  * @return the expected gradient value
  */
-HOST_DEV_INLINE GradientPair get(int id,
+HOST_DEV_INLINE GradientPair Get(int id,
                                  common::Span<const GradientPair> vals,
                                  common::Span<const int> instIds) {
   id = instIds[id];
@@ -123,13 +123,13 @@ __global__ void CubScanByKeyL1(
   Pair rootPair = {kNoneKey, GradientPair(0.f, 0.f)};
   int myKey;
   GradientPair myValue;
-  typedef cub::BlockScan<Pair, BLKDIM_L1L3> BlockScan;
+  using BlockScan = cub::BlockScan<Pair, BLKDIM_L1L3>;
   __shared__ typename BlockScan::TempStorage temp_storage;
   Pair threadData;
   int tid = blockIdx.x * BLKDIM_L1L3 + threadIdx.x;
   if (tid < size) {
     myKey = Abs2UniqueKey(tid, keys, colIds, nodeStart, nUniqKeys);
-    myValue = get(tid, vals, instIds);
+    myValue = Get(tid, vals, instIds);
   } else {
     myKey = kNoneKey;
     myValue = {};
@@ -164,7 +164,7 @@ __global__ void CubScanByKeyL1(
 template <int BLKSIZE>
 __global__ void CubScanByKeyL2(common::Span<GradientPair> mScans,
                                common::Span<int> mKeys, int mLength) {
-  typedef cub::BlockScan<Pair, BLKSIZE, cub::BLOCK_SCAN_WARP_SCANS> BlockScan;
+  using BlockScan = cub::BlockScan<Pair, BLKSIZE, cub::BLOCK_SCAN_WARP_SCANS>;
   Pair threadData;
   __shared__ typename BlockScan::TempStorage temp_storage;
   for (int i = threadIdx.x; i < mLength; i += BLKSIZE - 1) {
@@ -205,19 +205,19 @@ __global__ void CubScanByKeyL3(common::Span<GradientPair> sums,
   int previousKey =
       tid == 0 ? kNoneKey
                : Abs2UniqueKey(tid - 1, keys, colIds, nodeStart, nUniqKeys);
-  GradientPair myValue = scans[tid];
+  GradientPair my_value = scans[tid];
   __syncthreads();
   if (blockIdx.x > 0 && s_mKeys == previousKey) {
-    myValue += s_mScans[0];
+    my_value += s_mScans[0];
   }
   if (tid == size - 1) {
-    sums[previousKey] = myValue + get(tid, vals, instIds);
+    sums[previousKey] = my_value + Get(tid, vals, instIds);
   }
   if ((previousKey != myKey) && (previousKey >= 0)) {
-    sums[previousKey] = myValue;
+    sums[previousKey] = my_value;
-    myValue = GradientPair(0.0f, 0.0f);
+    my_value = GradientPair(0.0f, 0.0f);
   }
-  scans[tid] = myValue;
+  scans[tid] = my_value;
 }
 
 /**
@@ -271,14 +271,14 @@ struct ExactSplitCandidate {
   /** index where to split in the DMatrix */
   int index;
 
-  HOST_DEV_INLINE ExactSplitCandidate() : score(-FLT_MAX), index(INT_MAX) {}
+  HOST_DEV_INLINE ExactSplitCandidate() : score{-FLT_MAX}, index{INT_MAX} {}
 
   /**
    * @brief Whether the split info is valid to be used to create a new child
    * @param minSplitLoss minimum score above which decision to split is made
    * @return true if splittable, else false
    */
-  HOST_DEV_INLINE bool isSplittable(float minSplitLoss) const {
+  HOST_DEV_INLINE bool IsSplittable(float minSplitLoss) const {
     return ((score >= minSplitLoss) && (index != INT_MAX));
   }
 };
@@ -297,7 +297,7 @@ enum ArgMaxByKeyAlgo {
 /** max depth until which to use shared mem based atomics for argmax */
 static const int kMaxAbkLevels = 3;
 
-HOST_DEV_INLINE ExactSplitCandidate maxSplit(ExactSplitCandidate a,
+HOST_DEV_INLINE ExactSplitCandidate MaxSplit(ExactSplitCandidate a,
                                              ExactSplitCandidate b) {
   ExactSplitCandidate out;
   if (a.score < b.score) {
@@ -315,13 +315,13 @@ HOST_DEV_INLINE ExactSplitCandidate maxSplit(ExactSplitCandidate a,
 
 DEV_INLINE void AtomicArgMax(ExactSplitCandidate* address,
                              ExactSplitCandidate val) {
-  unsigned long long* intAddress = (unsigned long long*)address;  // NOLINT
+  unsigned long long* intAddress = reinterpret_cast<unsigned long long*>(address);  // NOLINT
   unsigned long long old = *intAddress;                           // NOLINT
-  unsigned long long assumed;                                     // NOLINT
+  unsigned long long assumed = old;                               // NOLINT
   do {
     assumed = old;
     ExactSplitCandidate res =
-        maxSplit(val, *reinterpret_cast<ExactSplitCandidate*>(&assumed));
+        MaxSplit(val, *reinterpret_cast<ExactSplitCandidate*>(&assumed));
     old = atomicCAS(intAddress, assumed, *reinterpret_cast<uint64_t*>(&res));
   } while (assumed != old);
 }
@@ -399,7 +399,7 @@ __global__ void AtomicArgMaxByKeySmem(
               nUniqKeys * sizeof(ExactSplitCandidate)));
   int tid = threadIdx.x;
   ExactSplitCandidate defVal;
-#pragma unroll 1
+
   for (int i = tid; i < nUniqKeys; i += blockDim.x) {
     sNodeSplits[i] = defVal;
   }
@@ -465,7 +465,7 @@ void ArgMaxByKey(common::Span<ExactSplitCandidate> nodeSplits,
   }
 }
 
-__global__ void assignColIds(int* colIds, const int* colOffsets) {
+__global__ void AssignColIds(int* colIds, const int* colOffsets) {
   int myId = blockIdx.x;
   int start = colOffsets[myId];
   int end = colOffsets[myId + 1];
@@ -474,10 +474,10 @@ __global__ void assignColIds(int* colIds, const int* colOffsets) {
   }
 }
 
-__global__ void fillDefaultNodeIds(NodeIdT* nodeIdsPerInst,
+__global__ void FillDefaultNodeIds(NodeIdT* nodeIdsPerInst,
-                                   const DeviceNodeStats* nodes, int nRows) {
+                                   const DeviceNodeStats* nodes, int n_rows) {
   int id = threadIdx.x + (blockIdx.x * blockDim.x);
-  if (id >= nRows) {
+  if (id >= n_rows) {
     return;
   }
   // if this element belongs to none of the currently active node-id's
@@ -497,7 +497,7 @@ __global__ void fillDefaultNodeIds(NodeIdT* nodeIdsPerInst,
   nodeIdsPerInst[id] = result;
 }
 
-__global__ void assignNodeIds(NodeIdT* nodeIdsPerInst, int* nodeLocations,
+__global__ void AssignNodeIds(NodeIdT* nodeIdsPerInst, int* nodeLocations,
                               const NodeIdT* nodeIds, const int* instId,
                               const DeviceNodeStats* nodes,
                               const int* colOffsets, const float* vals,
@@ -526,7 +526,7 @@ __global__ void assignNodeIds(NodeIdT* nodeIdsPerInst, int* nodeLocations,
   }
 }
 
-__global__ void markLeavesKernel(DeviceNodeStats* nodes, int len) {
+__global__ void MarkLeavesKernel(DeviceNodeStats* nodes, int len) {
   int id = (blockIdx.x * blockDim.x) + threadIdx.x;
   if ((id < len) && !nodes[id].IsUnused()) {
     int lid = (id << 1) + 1;
@@ -541,118 +541,117 @@ __global__ void markLeavesKernel(DeviceNodeStats* nodes, int len) {
 
 class GPUMaker : public TreeUpdater {
  protected:
-  TrainParam param;
+  TrainParam param_;
   /** whether we have initialized memory already (so as not to repeat!) */
-  bool allocated;
+  bool allocated_;
   /** feature values stored in column-major compressed format */
-  dh::DVec2<float> vals;
+  dh::DVec2<float> vals_;
-  dh::DVec<float> vals_cached;
+  dh::DVec<float> vals_cached_;
   /** corresponding instance id's of these featutre values */
-  dh::DVec2<int> instIds;
+  dh::DVec2<int> instIds_;
-  dh::DVec<int> instIds_cached;
+  dh::DVec<int> inst_ids_cached_;
   /** column offsets for these feature values */
-  dh::DVec<int> colOffsets;
+  dh::DVec<int> colOffsets_;
-  dh::DVec<GradientPair> gradsInst;
+  dh::DVec<GradientPair> gradsInst_;
-  dh::DVec2<NodeIdT> nodeAssigns;
+  dh::DVec2<NodeIdT> nodeAssigns_;
-  dh::DVec2<int> nodeLocations;
+  dh::DVec2<int> nodeLocations_;
-  dh::DVec<DeviceNodeStats> nodes;
+  dh::DVec<DeviceNodeStats> nodes_;
-  dh::DVec<NodeIdT> nodeAssignsPerInst;
+  dh::DVec<NodeIdT> node_assigns_per_inst_;
-  dh::DVec<GradientPair> gradSums;
+  dh::DVec<GradientPair> gradsums_;
-  dh::DVec<GradientPair> gradScans;
+  dh::DVec<GradientPair> gradscans_;
-  dh::DVec<ExactSplitCandidate> nodeSplits;
+  dh::DVec<ExactSplitCandidate> nodeSplits_;
-  int nVals;
+  int n_vals_;
-  int nRows;
+  int n_rows_;
-  int nCols;
+  int n_cols_;
-  int maxNodes;
+  int maxNodes_;
-  int maxLeaves;
+  int maxLeaves_;
 
   // devices are only used for resharding the HostDeviceVector passed as a parameter;
   // the algorithm works with a single GPU only
   GPUSet devices_;
 
-  dh::CubMemory tmp_mem;
+  dh::CubMemory tmp_mem_;
-  dh::DVec<GradientPair> tmpScanGradBuff;
+  dh::DVec<GradientPair> tmpScanGradBuff_;
-  dh::DVec<int> tmpScanKeyBuff;
+  dh::DVec<int> tmp_scan_key_buff_;
-  dh::DVec<int> colIds;
+  dh::DVec<int> colIds_;
-  dh::BulkAllocator<dh::MemoryType::kDevice> ba;
+  dh::BulkAllocator<dh::MemoryType::kDevice> ba_;
 
  public:
-  GPUMaker() : allocated(false) {}
+  GPUMaker() : allocated_{false} {}
-  ~GPUMaker() {}
+  ~GPUMaker() override = default;
 
-  void Init(
+  void Init(const std::vector<std::pair<std::string, std::string>> &args) override {
-      const std::vector<std::pair<std::string, std::string>>& args) {
+     param_.InitAllowUnknown(args);
-    param.InitAllowUnknown(args);
+     maxNodes_ = (1 << (param_.max_depth + 1)) - 1;
-    maxNodes = (1 << (param.max_depth + 1)) - 1;
+     maxLeaves_ = 1 << param_.max_depth;
-    maxLeaves = 1 << param.max_depth;
 
-    devices_ = GPUSet::All(param.gpu_id, param.n_gpus);
+     devices_ = GPUSet::All(param_.gpu_id, param_.n_gpus);
   }
 
   void Update(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
-              const std::vector<RegTree*>& trees) {
+              const std::vector<RegTree*>& trees) override {
     // rescale learning rate according to size of trees
-    float lr = param.learning_rate;
+    float lr = param_.learning_rate;
-    param.learning_rate = lr / trees.size();
+    param_.learning_rate = lr / trees.size();
 
     gpair->Reshard(devices_);
 
     try {
       // build tree
-      for (size_t i = 0; i < trees.size(); ++i) {
+      for (auto tree : trees) {
-        UpdateTree(gpair, dmat, trees[i]);
+        UpdateTree(gpair, dmat, tree);
       }
     } catch (const std::exception& e) {
       LOG(FATAL) << "grow_gpu exception: " << e.what() << std::endl;
     }
-    param.learning_rate = lr;
+    param_.learning_rate = lr;
   }
   /// @note: Update should be only after Init!!
   void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
                   RegTree* hTree) {
-    if (!allocated) {
+    if (!allocated_) {
       SetupOneTimeData(dmat);
     }
-    for (int i = 0; i < param.max_depth; ++i) {
+    for (int i = 0; i < param_.max_depth; ++i) {
       if (i == 0) {
         // make sure to start on a fresh tree with sorted values!
-        vals.CurrentDVec() = vals_cached;
+        vals_.CurrentDVec() = vals_cached_;
-        instIds.CurrentDVec() = instIds_cached;
+        instIds_.CurrentDVec() = inst_ids_cached_;
-        transferGrads(gpair);
+        TransferGrads(gpair);
       }
       int nNodes = 1 << i;
       NodeIdT nodeStart = nNodes - 1;
-      initNodeData(i, nodeStart, nNodes);
+      InitNodeData(i, nodeStart, nNodes);
-      findSplit(i, nodeStart, nNodes);
+      FindSplit(i, nodeStart, nNodes);
     }
     // mark all the used nodes with unused children as leaf nodes
-    markLeaves();
+    MarkLeaves();
-    Dense2SparseTree(hTree, nodes, param);
+    Dense2SparseTree(hTree, nodes_, param_);
   }
 
-  void split2node(int nNodes, NodeIdT nodeStart) {
+  void Split2Node(int nNodes, NodeIdT nodeStart) {
-    auto d_nodes = nodes.GetSpan();
+    auto d_nodes = nodes_.GetSpan();
-    auto d_gradScans = gradScans.GetSpan();
+    auto d_gradScans = gradscans_.GetSpan();
-    auto d_gradSums = gradSums.GetSpan();
+    auto d_gradsums = gradsums_.GetSpan();
-    auto d_nodeAssigns = nodeAssigns.CurrentSpan();
+    auto d_nodeAssigns = nodeAssigns_.CurrentSpan();
-    auto d_colIds = colIds.GetSpan();
+    auto d_colIds = colIds_.GetSpan();
-    auto d_vals = vals.Current();
+    auto d_vals = vals_.Current();
-    auto d_nodeSplits = nodeSplits.Data();
+    auto d_nodeSplits = nodeSplits_.Data();
     int nUniqKeys = nNodes;
-    float min_split_loss = param.min_split_loss;
+    float min_split_loss = param_.min_split_loss;
-    auto gpu_param = GPUTrainingParam(param);
+    auto gpu_param = GPUTrainingParam(param_);
 
-    dh::LaunchN(param.gpu_id, nNodes, [=] __device__(int uid) {
+    dh::LaunchN(param_.gpu_id, nNodes, [=] __device__(int uid) {
       int absNodeId = uid + nodeStart;
       ExactSplitCandidate s = d_nodeSplits[uid];
-      if (s.isSplittable(min_split_loss)) {
+      if (s.IsSplittable(min_split_loss)) {
         int idx = s.index;
         int nodeInstId =
             Abs2UniqueKey(idx, d_nodeAssigns, d_colIds, nodeStart, nUniqKeys);
         bool missingLeft = true;
         const DeviceNodeStats& n = d_nodes[absNodeId];
         GradientPair gradScan = d_gradScans[idx];
-        GradientPair gradSum = d_gradSums[nodeInstId];
+        GradientPair gradSum = d_gradsums[nodeInstId];
         float thresh = d_vals[idx];
         int colId = d_colIds[idx];
         // get the default direction for the current node
@@ -679,54 +678,53 @@ class GPUMaker : public TreeUpdater {
     });
   }
 
-  void findSplit(int level, NodeIdT nodeStart, int nNodes) {
+  void FindSplit(int level, NodeIdT nodeStart, int nNodes) {
-    ReduceScanByKey(gradSums.GetSpan(), gradScans.GetSpan(), gradsInst.GetSpan(),
+    ReduceScanByKey(gradsums_.GetSpan(), gradscans_.GetSpan(), gradsInst_.GetSpan(),
-                    instIds.CurrentSpan(), nodeAssigns.CurrentSpan(), nVals, nNodes,
+                    instIds_.CurrentSpan(), nodeAssigns_.CurrentSpan(), n_vals_, nNodes,
-                    nCols, tmpScanGradBuff.GetSpan(), tmpScanKeyBuff.GetSpan(),
+                    n_cols_, tmpScanGradBuff_.GetSpan(), tmp_scan_key_buff_.GetSpan(),
-                    colIds.GetSpan(), nodeStart);
+                    colIds_.GetSpan(), nodeStart);
-    ArgMaxByKey(nodeSplits.GetSpan(), gradScans.GetSpan(), gradSums.GetSpan(),
+    ArgMaxByKey(nodeSplits_.GetSpan(), gradscans_.GetSpan(), gradsums_.GetSpan(),
-                vals.CurrentSpan(), colIds.GetSpan(), nodeAssigns.CurrentSpan(),
+                vals_.CurrentSpan(), colIds_.GetSpan(), nodeAssigns_.CurrentSpan(),
-                nodes.GetSpan(), nNodes, nodeStart, nVals, param,
+                nodes_.GetSpan(), nNodes, nodeStart, n_vals_, param_,
                 level <= kMaxAbkLevels ? kAbkSmem : kAbkGmem);
-    split2node(nNodes, nodeStart);
+    Split2Node(nNodes, nodeStart);
   }
 
-  void allocateAllData(int offsetSize) {
+  void AllocateAllData(int offsetSize) {
-    int tmpBuffSize = ScanTempBufferSize(nVals);
+    int tmpBuffSize = ScanTempBufferSize(n_vals_);
-    ba.Allocate(param.gpu_id, &vals, nVals,
+    ba_.Allocate(param_.gpu_id, &vals_, n_vals_,
-                &vals_cached, nVals, &instIds, nVals, &instIds_cached, nVals,
+                 &vals_cached_, n_vals_, &instIds_, n_vals_, &inst_ids_cached_, n_vals_,
-                &colOffsets, offsetSize, &gradsInst, nRows, &nodeAssigns, nVals,
+                 &colOffsets_, offsetSize, &gradsInst_, n_rows_, &nodeAssigns_, n_vals_,
-                &nodeLocations, nVals, &nodes, maxNodes, &nodeAssignsPerInst,
+                 &nodeLocations_, n_vals_, &nodes_, maxNodes_, &node_assigns_per_inst_,
-                nRows, &gradSums, maxLeaves * nCols, &gradScans, nVals,
+                 n_rows_, &gradsums_, maxLeaves_ * n_cols_, &gradscans_, n_vals_,
-                &nodeSplits, maxLeaves, &tmpScanGradBuff, tmpBuffSize,
+                 &nodeSplits_, maxLeaves_, &tmpScanGradBuff_, tmpBuffSize,
-                &tmpScanKeyBuff, tmpBuffSize, &colIds, nVals);
+                 &tmp_scan_key_buff_, tmpBuffSize, &colIds_, n_vals_);
   }
 
   void SetupOneTimeData(DMatrix* dmat) {
-    size_t free_memory = dh::AvailableMemory(param.gpu_id);
     if (!dmat->SingleColBlock()) {
       LOG(FATAL) << "exact::GPUBuilder - must have 1 column block";
     }
     std::vector<float> fval;
     std::vector<int> fId;
     std::vector<size_t> offset;
-    convertToCsc(dmat, &fval, &fId, &offset);
+    ConvertToCsc(dmat, &fval, &fId, &offset);
-    allocateAllData(static_cast<int>(offset.size()));
+    AllocateAllData(static_cast<int>(offset.size()));
-    transferAndSortData(fval, fId, offset);
+    TransferAndSortData(fval, fId, offset);
-    allocated = true;
+    allocated_ = true;
   }
 
-  void convertToCsc(DMatrix* dmat, std::vector<float>* fval,
+  void ConvertToCsc(DMatrix* dmat, std::vector<float>* fval,
                     std::vector<int>* fId, std::vector<size_t>* offset) {
     const MetaInfo& info = dmat->Info();
     CHECK(info.num_col_ < std::numeric_limits<int>::max());
     CHECK(info.num_row_ < std::numeric_limits<int>::max());
-    nRows = static_cast<int>(info.num_row_);
+    n_rows_ = static_cast<int>(info.num_row_);
-    nCols = static_cast<int>(info.num_col_);
+    n_cols_ = static_cast<int>(info.num_col_);
-    offset->reserve(nCols + 1);
+    offset->reserve(n_cols_ + 1);
     offset->push_back(0);
-    fval->reserve(nCols * nRows);
+    fval->reserve(n_cols_ * n_rows_);
-    fId->reserve(nCols * nRows);
+    fId->reserve(n_cols_ * n_rows_);
     // in case you end up with a DMatrix having no column access
     // then make sure to enable that before copying the data!
     for (const auto& batch : dmat->GetSortedColumnBatches()) {
@@ -741,59 +739,59 @@ class GPUMaker : public TreeUpdater {
       }
     }
     CHECK(fval->size() < std::numeric_limits<int>::max());
-    nVals = static_cast<int>(fval->size());
+    n_vals_ = static_cast<int>(fval->size());
   }
 
-  void transferAndSortData(const std::vector<float>& fval,
+  void TransferAndSortData(const std::vector<float>& fval,
                            const std::vector<int>& fId,
                            const std::vector<size_t>& offset) {
-    vals.CurrentDVec() = fval;
+    vals_.CurrentDVec() = fval;
-    instIds.CurrentDVec() = fId;
+    instIds_.CurrentDVec() = fId;
-    colOffsets = offset;
+    colOffsets_ = offset;
-    dh::SegmentedSort<float, int>(&tmp_mem, &vals, &instIds, nVals, nCols,
+    dh::SegmentedSort<float, int>(&tmp_mem_, &vals_, &instIds_, n_vals_, n_cols_,
-                                  colOffsets);
+                                  colOffsets_);
-    vals_cached = vals.CurrentDVec();
+    vals_cached_ = vals_.CurrentDVec();
-    instIds_cached = instIds.CurrentDVec();
+    inst_ids_cached_ = instIds_.CurrentDVec();
-    assignColIds<<<nCols, 512>>>(colIds.Data(), colOffsets.Data());
+    AssignColIds<<<n_cols_, 512>>>(colIds_.Data(), colOffsets_.Data());
   }
 
-  void transferGrads(HostDeviceVector<GradientPair>* gpair) {
+  void TransferGrads(HostDeviceVector<GradientPair>* gpair) {
-    gpair->GatherTo(gradsInst.tbegin(), gradsInst.tend());
+    gpair->GatherTo(gradsInst_.tbegin(), gradsInst_.tend());
     // evaluate the full-grad reduction for the root node
-    dh::SumReduction<GradientPair>(tmp_mem, gradsInst, gradSums, nRows);
+    dh::SumReduction<GradientPair>(tmp_mem_, gradsInst_, gradsums_, n_rows_);
   }
 
-  void initNodeData(int level, NodeIdT nodeStart, int nNodes) {
+  void InitNodeData(int level, NodeIdT nodeStart, int nNodes) {
     // all instances belong to root node at the beginning!
     if (level == 0) {
-      nodes.Fill(DeviceNodeStats());
+      nodes_.Fill(DeviceNodeStats());
-      nodeAssigns.CurrentDVec().Fill(0);
+      nodeAssigns_.CurrentDVec().Fill(0);
-      nodeAssignsPerInst.Fill(0);
+      node_assigns_per_inst_.Fill(0);
       // for root node, just update the gradient/score/weight/id info
       // before splitting it! Currently all data is on GPU, hence this
       // stupid little kernel
-      auto d_nodes = nodes.Data();
+      auto d_nodes = nodes_.Data();
-      auto d_sums = gradSums.Data();
+      auto d_sums = gradsums_.Data();
-      auto gpu_params = GPUTrainingParam(param);
+      auto gpu_params = GPUTrainingParam(param_);
-      dh::LaunchN(param.gpu_id, 1, [=] __device__(int idx) {
+      dh::LaunchN(param_.gpu_id, 1, [=] __device__(int idx) {
         d_nodes[0] = DeviceNodeStats(d_sums[0], 0, gpu_params);
       });
     } else {
       const int BlkDim = 256;
       const int ItemsPerThread = 4;
       // assign default node ids first
-      int nBlks = dh::DivRoundUp(nRows, BlkDim);
+      int nBlks = dh::DivRoundUp(n_rows_, BlkDim);
-      fillDefaultNodeIds<<<nBlks, BlkDim>>>(nodeAssignsPerInst.Data(),
+      FillDefaultNodeIds<<<nBlks, BlkDim>>>(node_assigns_per_inst_.Data(),
-                                            nodes.Data(), nRows);
+                                            nodes_.Data(), n_rows_);
       // evaluate the correct child indices of non-missing values next
-      nBlks = dh::DivRoundUp(nVals, BlkDim * ItemsPerThread);
+      nBlks = dh::DivRoundUp(n_vals_, BlkDim * ItemsPerThread);
-      assignNodeIds<<<nBlks, BlkDim>>>(
+      AssignNodeIds<<<nBlks, BlkDim>>>(
-          nodeAssignsPerInst.Data(), nodeLocations.Current(),
+          node_assigns_per_inst_.Data(), nodeLocations_.Current(),
-          nodeAssigns.Current(), instIds.Current(), nodes.Data(),
+          nodeAssigns_.Current(), instIds_.Current(), nodes_.Data(),
-          colOffsets.Data(), vals.Current(), nVals, nCols);
+          colOffsets_.Data(), vals_.Current(), n_vals_, n_cols_);
       // gather the node assignments across all other columns too
-      dh::Gather(param.gpu_id, nodeAssigns.Current(),
+      dh::Gather(param_.gpu_id, nodeAssigns_.Current(),
-                 nodeAssignsPerInst.Data(), instIds.Current(), nVals);
+                 node_assigns_per_inst_.Data(), instIds_.Current(), n_vals_);
       SortKeys(level);
     }
   }
@@ -801,19 +799,19 @@ class GPUMaker : public TreeUpdater {
   void SortKeys(int level) {
     // segmented-sort the arrays based on node-id's
     // but we don't need more than level+1 bits for sorting!
-    SegmentedSort(&tmp_mem, &nodeAssigns, &nodeLocations, nVals, nCols,
+    SegmentedSort(&tmp_mem_, &nodeAssigns_, &nodeLocations_, n_vals_, n_cols_,
-                  colOffsets, 0, level + 1);
+                  colOffsets_, 0, level + 1);
-    dh::Gather<float, int>(param.gpu_id, vals.other(),
+    dh::Gather<float, int>(param_.gpu_id, vals_.other(),
-                           vals.Current(), instIds.other(), instIds.Current(),
+                           vals_.Current(), instIds_.other(), instIds_.Current(),
-                           nodeLocations.Current(), nVals);
+                           nodeLocations_.Current(), n_vals_);
-    vals.buff().selector ^= 1;
+    vals_.buff().selector ^= 1;
-    instIds.buff().selector ^= 1;
+    instIds_.buff().selector ^= 1;
   }
 
-  void markLeaves() {
+  void MarkLeaves() {
     const int BlkDim = 128;
-    int nBlks = dh::DivRoundUp(maxNodes, BlkDim);
+    int nBlks = dh::DivRoundUp(maxNodes_, BlkDim);
-    markLeavesKernel<<<nBlks, BlkDim>>>(nodes.Data(), maxNodes);
+    MarkLeavesKernel<<<nBlks, BlkDim>>>(nodes_.Data(), maxNodes_);
   }
 };
 

src/tree/updater_gpu_common.cuh

@@ -14,7 +14,7 @@
 
 #if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600
 
-#else
+#else  // In device code and CUDA < 600
 XGBOOST_DEVICE __forceinline__ double atomicAdd(double* address, double val) {
   unsigned long long int* address_as_ull =
       (unsigned long long int*)address;                   // NOLINT

src/tree/updater_gpu_hist.cu

@@ -108,7 +108,7 @@ __device__ GradientSumT ReduceFeature(common::Span<const GradientSumT> feature_h
 }
 
 /*! \brief Find the thread with best gain. */
-template <int BLOCK_THREADS, typename ReduceT, typename scan_t,
+template <int BLOCK_THREADS, typename ReduceT, typename ScanT,
           typename MaxReduceT, typename TempStorageT, typename GradientSumT>
 __device__ void EvaluateFeature(
     int fidx,
@@ -142,7 +142,7 @@ __device__ void EvaluateFeature(
     // Gradient value for current bin.
     GradientSumT bin =
         thread_active ? node_histogram[scan_begin + threadIdx.x] : GradientSumT();
-    scan_t(temp_storage->scan).ExclusiveScan(bin, bin, cub::Sum(), prefix_op);
+    ScanT(temp_storage->scan).ExclusiveScan(bin, bin, cub::Sum(), prefix_op);
 
     // Whether the gradient of missing values is put to the left side.
     bool missing_left = true;
@@ -198,12 +198,12 @@ __global__ void EvaluateSplitKernel(
     ValueConstraint value_constraint,
     common::Span<int> d_monotonic_constraints) {
   // KeyValuePair here used as threadIdx.x -> gain_value
-  typedef cub::KeyValuePair<int, float> ArgMaxT;
+  using ArgMaxT = cub::KeyValuePair<int, float>;
-  typedef cub::BlockScan<
+  using BlockScanT =
-    GradientSumT, BLOCK_THREADS, cub::BLOCK_SCAN_WARP_SCANS> BlockScanT;
+      cub::BlockScan<GradientSumT, BLOCK_THREADS, cub::BLOCK_SCAN_WARP_SCANS>;
-  typedef cub::BlockReduce<ArgMaxT, BLOCK_THREADS> MaxReduceT;
+  using MaxReduceT = cub::BlockReduce<ArgMaxT, BLOCK_THREADS>;
 
-  typedef cub::BlockReduce<GradientSumT, BLOCK_THREADS> SumReduceT;
+  using SumReduceT = cub::BlockReduce<GradientSumT, BLOCK_THREADS>;
 
   union TempStorage {
     typename BlockScanT::TempStorage scan;
@@ -274,51 +274,56 @@ __device__ int BinarySearchRow(bst_uint begin, bst_uint end, GidxIterT data,
  * \date    28/07/2018
  */
 template <typename GradientSumT>
-struct DeviceHistogram {
+class DeviceHistogram {
+ private:
   /*! \brief Map nidx to starting index of its histogram. */
-  std::map<int, size_t> nidx_map;
+  std::map<int, size_t> nidx_map_;
-  thrust::device_vector<typename GradientSumT::ValueT> data;
+  thrust::device_vector<typename GradientSumT::ValueT> data_;
-  const size_t kStopGrowingSize = 1 << 26;  // Do not grow beyond this size
+  static constexpr size_t kStopGrowingSize = 1 << 26;  // Do not grow beyond this size
-  int n_bins;
+  int n_bins_;
   int device_id_;
 
+ public:
   void Init(int device_id, int n_bins) {
-    this->n_bins = n_bins;
+    this->n_bins_ = n_bins;
     this->device_id_ = device_id;
   }
 
   void Reset() {
     dh::safe_cuda(cudaSetDevice(device_id_));
     dh::safe_cuda(cudaMemsetAsync(
-        data.data().get(), 0,
+        data_.data().get(), 0,
-        data.size() * sizeof(typename decltype(data)::value_type)));
+        data_.size() * sizeof(typename decltype(data_)::value_type)));
-    nidx_map.clear();
+    nidx_map_.clear();
+  }
+  bool HistogramExists(int nidx) {
+    return nidx_map_.find(nidx) != nidx_map_.end();
   }
 
-  bool HistogramExists(int nidx) {
+  thrust::device_vector<typename GradientSumT::ValueT> &Data() {
-    return nidx_map.find(nidx) != nidx_map.end();
+    return data_;
   }
 
   void AllocateHistogram(int nidx) {
     if (HistogramExists(nidx)) return;
     size_t current_size =
-        nidx_map.size() * n_bins * 2;  // Number of items currently used in data
+        nidx_map_.size() * n_bins_ * 2;  // Number of items currently used in data
     dh::safe_cuda(cudaSetDevice(device_id_));
-    if (data.size() >= kStopGrowingSize) {
+    if (data_.size() >= kStopGrowingSize) {
       // Recycle histogram memory
-      std::pair<int, size_t> old_entry = *nidx_map.begin();
+      std::pair<int, size_t> old_entry = *nidx_map_.begin();
-      nidx_map.erase(old_entry.first);
+      nidx_map_.erase(old_entry.first);
-      dh::safe_cuda(cudaMemsetAsync(data.data().get() + old_entry.second, 0,
+      dh::safe_cuda(cudaMemsetAsync(data_.data().get() + old_entry.second, 0,
-                               n_bins * sizeof(GradientSumT)));
+                                    n_bins_ * sizeof(GradientSumT)));
-      nidx_map[nidx] = old_entry.second;
+      nidx_map_[nidx] = old_entry.second;
     } else {
       // Append new node histogram
-      nidx_map[nidx] = current_size;
+      nidx_map_[nidx] = current_size;
-      if (data.size() < current_size + n_bins * 2) {
+      if (data_.size() < current_size + n_bins_ * 2) {
         size_t new_size = current_size * 2;  // Double in size
-        new_size = std::max(static_cast<size_t>(n_bins * 2),
+        new_size = std::max(static_cast<size_t>(n_bins_ * 2),
                             new_size);  // Have at least one histogram
-        data.resize(new_size);
+        data_.resize(new_size);
       }
     }
   }
@@ -330,9 +335,9 @@ struct DeviceHistogram {
    */
   common::Span<GradientSumT> GetNodeHistogram(int nidx) {
     CHECK(this->HistogramExists(nidx));
-    auto ptr = data.data().get() + nidx_map[nidx];
+    auto ptr = data_.data().get() + nidx_map_[nidx];
     return common::Span<GradientSumT>(
-        reinterpret_cast<GradientSumT*>(ptr), n_bins);
+        reinterpret_cast<GradientSumT*>(ptr), n_bins_);
   }
 };
 
@@ -351,7 +356,7 @@ struct CalcWeightTrainParam {
 };
 
 // Bin each input data entry, store the bin indices in compressed form.
-__global__ void compress_bin_ellpack_k(
+__global__ void CompressBinEllpackKernel(
     common::CompressedBufferWriter wr,
     common::CompressedByteT* __restrict__ buffer,  // gidx_buffer
     const size_t* __restrict__ row_ptrs,           // row offset of input data
@@ -366,8 +371,9 @@ __global__ void compress_bin_ellpack_k(
     unsigned int null_gidx_value) {
   size_t irow = threadIdx.x + blockIdx.x * blockDim.x;
   int ifeature = threadIdx.y + blockIdx.y * blockDim.y;
-  if (irow >= n_rows || ifeature >= row_stride)
+  if (irow >= n_rows || ifeature >= row_stride) {
     return;
+  }
   int row_length = static_cast<int>(row_ptrs[irow + 1] - row_ptrs[irow]);
   unsigned int bin = null_gidx_value;
   if (ifeature < row_length) {
@@ -380,8 +386,9 @@ __global__ void compress_bin_ellpack_k(
     // Assigning the bin in current entry.
     // S.t.: fvalue < feature_cuts[bin]
     bin = dh::UpperBound(feature_cuts, ncuts, fvalue);
-    if (bin >= ncuts)
+    if (bin >= ncuts) {
       bin = ncuts - 1;
+    }
     // Add the number of bins in previous features.
     bin += cut_rows[feature];
   }
@@ -419,7 +426,7 @@ struct Segment {
   size_t begin;
   size_t end;
 
-  Segment() : begin(0), end(0) {}
+  Segment() : begin{0}, end{0} {}
 
   Segment(size_t begin, size_t end) : begin(begin), end(end) {
     CHECK_GE(end, begin);
@@ -487,7 +494,9 @@ struct GPUHistBuilderBase {
 // Manage memory for a single GPU
 template <typename GradientSumT>
 struct DeviceShard {
-  int device_id_;
+  int n_bins;
+  int device_id;
+
   dh::BulkAllocator<dh::MemoryType::kDevice> ba;
 
   /*! \brief HistCutMatrix stored in device. */
@@ -498,14 +507,12 @@ struct DeviceShard {
     dh::DVec<bst_float> min_fvalue;
     /*! \brief Cut. */
     dh::DVec<bst_float> gidx_fvalue_map;
-  } cut_;
+  } d_cut;
 
   /*! \brief Range of rows for each node. */
   std::vector<Segment> ridx_segments;
   DeviceHistogram<GradientSumT> hist;
 
-  /*! \brief global index of histogram, which is stored in ELLPack format. */
-  dh::DVec<common::CompressedByteT> gidx_buffer;
   /*! \brief row length for ELLPack. */
   size_t row_stride;
   common::CompressedIterator<uint32_t> gidx;
@@ -526,6 +533,8 @@ struct DeviceShard {
   /*! \brief Sum gradient for each node. */
   std::vector<GradientPair> node_sum_gradients;
   dh::DVec<GradientPair> node_sum_gradients_d;
+  /*! \brief global index of histogram, which is stored in ELLPack format. */
+  dh::DVec<common::CompressedByteT> gidx_buffer;
   /*! \brief row offset in SparsePage (the input data). */
   thrust::device_vector<size_t> row_ptrs;
   /*! \brief On-device feature set, only actually used on one of the devices */
@@ -534,7 +543,6 @@ struct DeviceShard {
   bst_uint row_begin_idx;
   bst_uint row_end_idx;
   bst_uint n_rows;
-  int n_bins;
 
   TrainParam param;
   bool prediction_cache_initialised;
@@ -544,21 +552,21 @@ struct DeviceShard {
   std::unique_ptr<GPUHistBuilderBase<GradientSumT>> hist_builder;
 
   // TODO(canonizer): do add support multi-batch DMatrix here
-  DeviceShard(int device_id, bst_uint row_begin, bst_uint row_end,
+  DeviceShard(int _device_id, bst_uint row_begin, bst_uint row_end,
               TrainParam _param)
-      : device_id_(device_id),
+      : device_id(_device_id),
         row_begin_idx(row_begin),
         row_end_idx(row_end),
         row_stride(0),
         n_rows(row_end - row_begin),
-        n_bins(0),
+        n_bins{0},
         null_gidx_value(0),
-        param(_param),
+        param(std::move(_param)),
         prediction_cache_initialised(false) {}
 
   /* Init row_ptrs and row_stride */
   void InitRowPtrs(const SparsePage& row_batch) {
-    dh::safe_cuda(cudaSetDevice(device_id_));
+    dh::safe_cuda(cudaSetDevice(device_id));
     const auto& offset_vec = row_batch.offset.HostVector();
     row_ptrs.resize(n_rows + 1);
     thrust::copy(offset_vec.data() + row_begin_idx,
@@ -589,12 +597,11 @@ struct DeviceShard {
 
   void CreateHistIndices(const SparsePage& row_batch);
 
-  ~DeviceShard() {
+  ~DeviceShard() = default;
-  }
 
   // Reset values for each update iteration
   void Reset(HostDeviceVector<GradientPair>* dh_gpair) {
-    dh::safe_cuda(cudaSetDevice(device_id_));
+    dh::safe_cuda(cudaSetDevice(device_id));
     position.CurrentDVec().Fill(0);
     std::fill(node_sum_gradients.begin(), node_sum_gradients.end(),
               GradientPair());
@@ -603,8 +610,8 @@ struct DeviceShard {
 
     std::fill(ridx_segments.begin(), ridx_segments.end(), Segment(0, 0));
     ridx_segments.front() = Segment(0, ridx.Size());
-    this->gpair.copy(dh_gpair->tcbegin(device_id_),
+    this->gpair.copy(dh_gpair->tcbegin(device_id),
-                     dh_gpair->tcend(device_id_));
+                     dh_gpair->tcend(device_id));
     SubsampleGradientPair(&gpair, param.subsample, row_begin_idx);
     hist.Reset();
   }
@@ -612,7 +619,7 @@ struct DeviceShard {
   DeviceSplitCandidate EvaluateSplit(int nidx,
                                      const std::vector<int>& feature_set,
                                      ValueConstraint value_constraint) {
-    dh::safe_cuda(cudaSetDevice(device_id_));
+    dh::safe_cuda(cudaSetDevice(device_id));
     auto d_split_candidates = temp_memory.GetSpan<DeviceSplitCandidate>(feature_set.size());
     feature_set_d.resize(feature_set.size());
     auto d_features = common::Span<int>(feature_set_d.data().get(),
@@ -622,14 +629,13 @@ struct DeviceShard {
     DeviceNodeStats node(node_sum_gradients[nidx], nidx, param);
 
     // One block for each feature
-    int constexpr BLOCK_THREADS = 256;
+    int constexpr kBlockThreads = 256;
-    EvaluateSplitKernel<BLOCK_THREADS, GradientSumT>
+    EvaluateSplitKernel<kBlockThreads, GradientSumT>
-        <<<uint32_t(feature_set.size()), BLOCK_THREADS, 0>>>(
+      <<<uint32_t(feature_set.size()), kBlockThreads, 0>>>
-            hist.GetNodeHistogram(nidx), d_features, node,
+      (hist.GetNodeHistogram(nidx), d_features, node,
-            cut_.feature_segments.GetSpan(), cut_.min_fvalue.GetSpan(),
+       d_cut.feature_segments.GetSpan(), d_cut.min_fvalue.GetSpan(),
-            cut_.gidx_fvalue_map.GetSpan(), GPUTrainingParam(param),
+       d_cut.gidx_fvalue_map.GetSpan(), GPUTrainingParam(param),
-            d_split_candidates, value_constraint,
+       d_split_candidates, value_constraint, monotone_constraints.GetSpan());
-            monotone_constraints.GetSpan());
 
     std::vector<DeviceSplitCandidate> split_candidates(feature_set.size());
     dh::safe_cuda(cudaMemcpy(split_candidates.data(), d_split_candidates.data(),
@@ -655,7 +661,7 @@ struct DeviceShard {
     auto d_node_hist_histogram = hist.GetNodeHistogram(nidx_histogram);
     auto d_node_hist_subtraction = hist.GetNodeHistogram(nidx_subtraction);
 
-    dh::LaunchN(device_id_, hist.n_bins, [=] __device__(size_t idx) {
+    dh::LaunchN(device_id, n_bins, [=] __device__(size_t idx) {
       d_node_hist_subtraction[idx] =
           d_node_hist_parent[idx] - d_node_hist_histogram[idx];
     });
@@ -673,7 +679,7 @@ struct DeviceShard {
                       int64_t split_gidx, bool default_dir_left, bool is_dense,
                       int fidx_begin,  // cut.row_ptr[fidx]
                       int fidx_end) {  // cut.row_ptr[fidx + 1]
-    dh::safe_cuda(cudaSetDevice(device_id_));
+    dh::safe_cuda(cudaSetDevice(device_id));
     Segment segment = ridx_segments[nidx];
     bst_uint* d_ridx = ridx.Current();
     int* d_position = position.Current();
@@ -681,7 +687,7 @@ struct DeviceShard {
     size_t row_stride = this->row_stride;
     // Launch 1 thread for each row
     dh::LaunchN<1, 128>(
-        device_id_, segment.Size(), [=] __device__(bst_uint idx) {
+        device_id, segment.Size(), [=] __device__(bst_uint idx) {
           idx += segment.begin;
           bst_uint ridx = d_ridx[idx];
           auto row_begin = row_stride * ridx;
@@ -737,14 +743,14 @@ struct DeviceShard {
     const auto d_position_other = position.other() + segment.begin;
     const auto d_ridx_current = ridx.Current() + segment.begin;
     const auto d_ridx_other = ridx.other() + segment.begin;
-    dh::LaunchN(device_id_, segment.Size(), [=] __device__(size_t idx) {
+    dh::LaunchN(device_id, segment.Size(), [=] __device__(size_t idx) {
       d_position_current[idx] = d_position_other[idx];
       d_ridx_current[idx] = d_ridx_other[idx];
     });
   }
 
   void UpdatePredictionCache(bst_float* out_preds_d) {
-    dh::safe_cuda(cudaSetDevice(device_id_));
+    dh::safe_cuda(cudaSetDevice(device_id));
     if (!prediction_cache_initialised) {
       dh::safe_cuda(cudaMemcpyAsync(
           prediction_cache.Data(), out_preds_d,
@@ -764,7 +770,7 @@ struct DeviceShard {
     auto d_prediction_cache = prediction_cache.Data();
 
     dh::LaunchN(
-        device_id_, prediction_cache.Size(), [=] __device__(int local_idx) {
+        device_id, prediction_cache.Size(), [=] __device__(int local_idx) {
           int pos = d_position[local_idx];
           bst_float weight = CalcWeight(param_d, d_node_sum_gradients[pos]);
           d_prediction_cache[d_ridx[local_idx]] +=
@@ -799,7 +805,7 @@ struct SharedMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
     if (grid_size <= 0) {
       return;
     }
-    dh::safe_cuda(cudaSetDevice(shard->device_id_));
+    dh::safe_cuda(cudaSetDevice(shard->device_id));
     SharedMemHistKernel<<<grid_size, block_threads, smem_size>>>
         (shard->row_stride, d_ridx, d_gidx, null_gidx_value, d_node_hist.data(), d_gpair,
          segment_begin, n_elements);
@@ -819,7 +825,7 @@ struct GlobalMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
     size_t const row_stride = shard->row_stride;
     int const null_gidx_value = shard->null_gidx_value;
 
-    dh::LaunchN(shard->device_id_, n_elements, [=] __device__(size_t idx) {
+    dh::LaunchN(shard->device_id, n_elements, [=] __device__(size_t idx) {
         int ridx = d_ridx[(idx / row_stride) + segment.begin];
         // lookup the index (bin) of histogram.
         int gidx = d_gidx[ridx * row_stride + idx % row_stride];
@@ -834,31 +840,31 @@ struct GlobalMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
 template <typename GradientSumT>
 inline void DeviceShard<GradientSumT>::InitCompressedData(
     const common::HistCutMatrix& hmat, const SparsePage& row_batch) {
-  n_bins = hmat.row_ptr.back();
+  n_bins = hmat.NumBins();
-  null_gidx_value = hmat.row_ptr.back();
+  null_gidx_value = hmat.NumBins();
 
   int max_nodes =
       param.max_leaves > 0 ? param.max_leaves * 2 : MaxNodesDepth(param.max_depth);
 
-  ba.Allocate(device_id_,
+  ba.Allocate(device_id,
               &gpair, n_rows,
               &ridx, n_rows,
               &position, n_rows,
               &prediction_cache, n_rows,
               &node_sum_gradients_d, max_nodes,
-              &cut_.feature_segments, hmat.row_ptr.size(),
+              &d_cut.feature_segments, hmat.row_ptr.size(),
-              &cut_.gidx_fvalue_map, hmat.cut.size(),
+              &d_cut.gidx_fvalue_map, hmat.cut.size(),
-              &cut_.min_fvalue, hmat.min_val.size(),
+              &d_cut.min_fvalue, hmat.min_val.size(),
               &monotone_constraints, param.monotone_constraints.size());
-  cut_.gidx_fvalue_map = hmat.cut;
+  d_cut.gidx_fvalue_map = hmat.cut;
-  cut_.min_fvalue = hmat.min_val;
+  d_cut.min_fvalue = hmat.min_val;
-  cut_.feature_segments = hmat.row_ptr;
+  d_cut.feature_segments = hmat.row_ptr;
   monotone_constraints = param.monotone_constraints;
 
   node_sum_gradients.resize(max_nodes);
   ridx_segments.resize(max_nodes);
 
-  dh::safe_cuda(cudaSetDevice(device_id_));
+  dh::safe_cuda(cudaSetDevice(device_id));
 
   // allocate compressed bin data
   int num_symbols = n_bins + 1;
@@ -870,7 +876,7 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
   CHECK(!(param.max_leaves == 0 && param.max_depth == 0))
       << "Max leaves and max depth cannot both be unconstrained for "
       "gpu_hist.";
-  ba.Allocate(device_id_, &gidx_buffer, compressed_size_bytes);
+  ba.Allocate(device_id, &gidx_buffer, compressed_size_bytes);
   gidx_buffer.Fill(0);
 
   int nbits = common::detail::SymbolBits(num_symbols);
@@ -882,7 +888,7 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
   // check if we can use shared memory for building histograms
   // (assuming atleast we need 2 CTAs per SM to maintain decent latency hiding)
   auto histogram_size = sizeof(GradientSumT) * null_gidx_value;
-  auto max_smem = dh::MaxSharedMemory(device_id_);
+  auto max_smem = dh::MaxSharedMemory(device_id);
   if (histogram_size <= max_smem) {
     hist_builder.reset(new SharedMemHistBuilder<GradientSumT>);
   } else {
@@ -890,7 +896,7 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
   }
 
   // Init histogram
-  hist.Init(device_id_, hmat.row_ptr.back());
+  hist.Init(device_id, hmat.NumBins());
 }
 
 
@@ -900,7 +906,7 @@ inline void DeviceShard<GradientSumT>::CreateHistIndices(const SparsePage& row_b
   // bin and compress entries in batches of rows
   size_t gpu_batch_nrows =
       std::min
-      (dh::TotalMemory(device_id_) / (16 * row_stride * sizeof(Entry)),
+      (dh::TotalMemory(device_id) / (16 * row_stride * sizeof(Entry)),
        static_cast<size_t>(n_rows));
   const std::vector<Entry>& data_vec = row_batch.data.HostVector();
 
@@ -924,12 +930,12 @@ inline void DeviceShard<GradientSumT>::CreateHistIndices(const SparsePage& row_b
     const dim3 block3(32, 8, 1);  // 256 threads
     const dim3 grid3(dh::DivRoundUp(n_rows, block3.x),
                      dh::DivRoundUp(row_stride, block3.y), 1);
-    compress_bin_ellpack_k<<<grid3, block3>>>
+    CompressBinEllpackKernel<<<grid3, block3>>>
         (common::CompressedBufferWriter(num_symbols),
          gidx_buffer.Data(),
          row_ptrs.data().get() + batch_row_begin,
          entries_d.data().get(),
-         cut_.gidx_fvalue_map.Data(), cut_.feature_segments.Data(),
+         d_cut.gidx_fvalue_map.Data(), d_cut.feature_segments.Data(),
          batch_row_begin, batch_nrows,
          row_ptrs[batch_row_begin],
          row_stride, null_gidx_value);
@@ -948,7 +954,7 @@ class GPUHistMakerSpecialised{
  public:
   struct ExpandEntry;
 
-  GPUHistMakerSpecialised() : initialised_(false), p_last_fmat_(nullptr) {}
+  GPUHistMakerSpecialised() : initialised_{false}, p_last_fmat_{nullptr} {}
   void Init(
       const std::vector<std::pair<std::string, std::string>>& args) {
     param_.InitAllowUnknown(args);
@@ -977,8 +983,8 @@ class GPUHistMakerSpecialised{
     ValueConstraint::Init(&param_, dmat->Info().num_col_);
     // build tree
     try {
-      for (size_t i = 0; i < trees.size(); ++i) {
+      for (xgboost::RegTree* tree : trees) {
-        this->UpdateTree(gpair, dmat, trees[i]);
+        this->UpdateTree(gpair, dmat, tree);
       }
       dh::safe_cuda(cudaGetLastError());
     } catch (const std::exception& e) {
@@ -1056,14 +1062,16 @@ class GPUHistMakerSpecialised{
   }
 
   void AllReduceHist(int nidx) {
-    if (shards_.size() == 1 && !rabit::IsDistributed()) return;
+    if (shards_.size() == 1 && !rabit::IsDistributed()) {
+      return;
+    }
     monitor_.StartCuda("AllReduce");
 
     reducer_.GroupStart();
     for (auto& shard : shards_) {
       auto d_node_hist = shard->hist.GetNodeHistogram(nidx).data();
       reducer_.AllReduceSum(
-          dist_.Devices().Index(shard->device_id_),
+          dist_.Devices().Index(shard->device_id),
           reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
           reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
           n_bins_ * (sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT)));
@@ -1141,14 +1149,14 @@ class GPUHistMakerSpecialised{
   }
 
   void InitRoot(RegTree* p_tree) {
-    constexpr int root_nidx = 0;
+    constexpr int kRootNIdx = 0;
     // Sum gradients
     std::vector<GradientPair> tmp_sums(shards_.size());
 
     dh::ExecuteIndexShards(
         &shards_,
         [&](int i, std::unique_ptr<DeviceShard<GradientSumT>>& shard) {
-          dh::safe_cuda(cudaSetDevice(shard->device_id_));
+          dh::safe_cuda(cudaSetDevice(shard->device_id));
           tmp_sums[i] = dh::SumReduction(
               shard->temp_memory, shard->gpair.Data(), shard->gpair.Size());
         });
@@ -1156,35 +1164,36 @@ class GPUHistMakerSpecialised{
     GradientPair sum_gradient =
         std::accumulate(tmp_sums.begin(), tmp_sums.end(), GradientPair());
 
-    rabit::Allreduce<rabit::op::Sum>((GradientPair::ValueT*)&sum_gradient, 2);
+    rabit::Allreduce<rabit::op::Sum>(
+        reinterpret_cast<GradientPair::ValueT*>(&sum_gradient), 2);
 
     // Generate root histogram
     dh::ExecuteIndexShards(
         &shards_,
         [&](int idx, std::unique_ptr<DeviceShard<GradientSumT>>& shard) {
-          shard->BuildHist(root_nidx);
+          shard->BuildHist(kRootNIdx);
         });
 
-    this->AllReduceHist(root_nidx);
+    this->AllReduceHist(kRootNIdx);
 
     // Remember root stats
-    p_tree->Stat(root_nidx).sum_hess = sum_gradient.GetHess();
+    p_tree->Stat(kRootNIdx).sum_hess = sum_gradient.GetHess();
     auto weight = CalcWeight(param_, sum_gradient);
-    p_tree->Stat(root_nidx).base_weight = weight;
+    p_tree->Stat(kRootNIdx).base_weight = weight;
-    (*p_tree)[root_nidx].SetLeaf(param_.learning_rate * weight);
+    (*p_tree)[kRootNIdx].SetLeaf(param_.learning_rate * weight);
 
     // Store sum gradients
     for (auto& shard : shards_) {
-      shard->node_sum_gradients[root_nidx] = sum_gradient;
+      shard->node_sum_gradients[kRootNIdx] = sum_gradient;
     }
 
     // Initialise root constraint
     node_value_constraints_.resize(p_tree->GetNodes().size());
 
     // Generate first split
-    auto split = this->EvaluateSplit(root_nidx, p_tree);
+    auto split = this->EvaluateSplit(kRootNIdx, p_tree);
     qexpand_->push(
-        ExpandEntry(root_nidx, p_tree->GetDepth(root_nidx), split, 0));
+        ExpandEntry(kRootNIdx, p_tree->GetDepth(kRootNIdx), split, 0));
   }
 
   void UpdatePosition(const ExpandEntry& candidate, RegTree* p_tree) {
@@ -1302,15 +1311,16 @@ class GPUHistMakerSpecialised{
 
   bool UpdatePredictionCache(
       const DMatrix* data, HostDeviceVector<bst_float>* p_out_preds) {
-    if (shards_.empty() || p_last_fmat_ == nullptr || p_last_fmat_ != data)
-      return false;
     monitor_.StartCuda("UpdatePredictionCache");
+    if (shards_.empty() || p_last_fmat_ == nullptr || p_last_fmat_ != data) {
+      return false;
+    }
     p_out_preds->Reshard(dist_.Devices());
     dh::ExecuteIndexShards(
         &shards_,
         [&](int idx, std::unique_ptr<DeviceShard<GradientSumT>>& shard) {
           shard->UpdatePredictionCache(
-              p_out_preds->DevicePointer(shard->device_id_));
+              p_out_preds->DevicePointer(shard->device_id));
         });
     monitor_.StopCuda("UpdatePredictionCache");
     return true;
@@ -1321,15 +1331,23 @@ class GPUHistMakerSpecialised{
     int depth;
     DeviceSplitCandidate split;
     uint64_t timestamp;
-    ExpandEntry(int nid, int depth, const DeviceSplitCandidate& split,
+    ExpandEntry(int _nid, int _depth, const DeviceSplitCandidate _split,
-                uint64_t timestamp)
+                uint64_t _timestamp) :
-        : nid(nid), depth(depth), split(split), timestamp(timestamp) {}
+        nid{_nid}, depth{_depth}, split(std::move(_split)),
+        timestamp{_timestamp} {}
     bool IsValid(const TrainParam& param, int num_leaves) const {
-      if (split.loss_chg <= kRtEps) return false;
+      if (split.loss_chg <= kRtEps) {
-      if (split.left_sum.GetHess() == 0 || split.right_sum.GetHess() == 0)
         return false;
-      if (param.max_depth > 0 && depth == param.max_depth) return false;
+      }
-      if (param.max_leaves > 0 && num_leaves == param.max_leaves) return false;
+      if (split.left_sum.GetHess() == 0 || split.right_sum.GetHess() == 0) {
+        return false;
+      }
+      if (param.max_depth > 0 && depth == param.max_depth) {
+        return false;
+      }
+      if (param.max_leaves > 0 && num_leaves == param.max_leaves) {
+        return false;
+      }
       return true;
     }
 
@@ -1365,28 +1383,36 @@ class GPUHistMakerSpecialised{
       return lhs.split.loss_chg < rhs.split.loss_chg;  // favor large loss_chg
     }
   }
-  TrainParam param_;
+
-  GPUHistMakerTrainParam hist_maker_param_;
+  TrainParam param_;            // NOLINT
-  common::HistCutMatrix hmat_;
+  common::HistCutMatrix hmat_;  // NOLINT
-  common::GHistIndexMatrix gmat_;
+  MetaInfo* info_;              // NOLINT
-  MetaInfo* info_;
+
+  std::vector<std::unique_ptr<DeviceShard<GradientSumT>>> shards_;  // NOLINT
+  common::ColumnSampler column_sampler_;                            // NOLINT
+
+  std::vector<ValueConstraint> node_value_constraints_;  // NOLINT
+
+ private:
   bool initialised_;
+
   int n_devices_;
   int n_bins_;
 
-  std::vector<std::unique_ptr<DeviceShard<GradientSumT>>> shards_;
+  GPUHistMakerTrainParam hist_maker_param_;
-  common::ColumnSampler column_sampler_;
+  common::GHistIndexMatrix gmat_;
+
   using ExpandQueue = std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
     std::function<bool(ExpandEntry, ExpandEntry)>>;
   std::unique_ptr<ExpandQueue> qexpand_;
-  common::Monitor monitor_;
   dh::AllReducer reducer_;
-  std::vector<ValueConstraint> node_value_constraints_;
-  /*! List storing device id. */
-  std::vector<int> device_list_;
 
   DMatrix* p_last_fmat_;
   GPUDistribution dist_;
+
+  common::Monitor monitor_;
+  /*! List storing device id. */
+  std::vector<int> device_list_;
 };
 
 class GPUHistMaker : public TreeUpdater {

src/tree/updater_histmaker.cc

@@ -69,9 +69,9 @@ class HistMaker: public BaseMaker {
     std::vector<GradStats> data;
     /*! \brief */
     inline HistUnit operator[](size_t fid) {
-      return HistUnit(cut + rptr[fid],
+      return {cut + rptr[fid],
               &data[0] + rptr[fid],
-                      rptr[fid+1] - rptr[fid]);
+              rptr[fid+1] - rptr[fid]};
     }
   };
   // thread workspace

src/tree/updater_quantile_hist.cc

@@ -95,11 +95,10 @@ void QuantileHistMaker::Builder::SyncHistograms(
   perf_monitor.TickStart();
   this->histred_.Allreduce(hist_[starting_index].data(), hist_builder_.GetNumBins() * sync_count);
   // use Subtraction Trick
-  for (auto local_it = nodes_for_subtraction_trick_.begin();
+  for (auto const& node_pair : nodes_for_subtraction_trick_) {
-    local_it != nodes_for_subtraction_trick_.end(); local_it++) {
+    hist_.AddHistRow(node_pair.first);
-    hist_.AddHistRow(local_it->first);
+    SubtractionTrick(hist_[node_pair.first], hist_[node_pair.second],
-    SubtractionTrick(hist_[local_it->first], hist_[local_it->second],
+                     hist_[(*p_tree)[node_pair.first].Parent()]);
-                     hist_[(*p_tree)[local_it->first].Parent()]);
   }
   perf_monitor.UpdatePerfTimer(TreeGrowingPerfMonitor::timer_name::BUILD_HIST);
 }
@@ -112,8 +111,8 @@ void QuantileHistMaker::Builder::BuildLocalHistograms(
     RegTree *p_tree,
     const std::vector<GradientPair> &gpair_h) {
   perf_monitor.TickStart();
-  for (size_t k = 0; k < qexpand_depth_wise_.size(); k++) {
+  for (auto const& entry : qexpand_depth_wise_) {
-    int nid = qexpand_depth_wise_[k].nid;
+    int nid = entry.nid;
     RegTree::Node &node = (*p_tree)[nid];
     if (rabit::IsDistributed()) {
       if (node.IsRoot() || node.IsLeftChild()) {
@@ -160,8 +159,8 @@ void QuantileHistMaker::Builder::BuildNodeStats(
     RegTree *p_tree,
     const std::vector<GradientPair> &gpair_h) {
   perf_monitor.TickStart();
-  for (size_t k = 0; k < qexpand_depth_wise_.size(); k++) {
+  for (auto const& entry : qexpand_depth_wise_) {
-    int nid = qexpand_depth_wise_[k].nid;
+    int nid = entry.nid;
     this->InitNewNode(nid, gmat, gpair_h, *p_fmat, *p_tree);
     // add constraints
     if (!(*p_tree)[nid].IsLeftChild() && !(*p_tree)[nid].IsRoot()) {
@@ -185,8 +184,8 @@ void QuantileHistMaker::Builder::EvaluateSplits(
     int depth,
     unsigned *timestamp,
     std::vector<ExpandEntry> *temp_qexpand_depth) {
-  for (size_t k = 0; k < qexpand_depth_wise_.size(); k++) {
+  for (auto const& entry : qexpand_depth_wise_) {
-    int nid = qexpand_depth_wise_[k].nid;
+    int nid = entry.nid;
     perf_monitor.TickStart();
     this->EvaluateSplit(nid, gmat, hist_, *p_fmat, *p_tree);
     perf_monitor.UpdatePerfTimer(TreeGrowingPerfMonitor::timer_name::EVALUATE_SPLIT);
@@ -221,7 +220,7 @@ void QuantileHistMaker::Builder::ExpandWithDepthWidth(
   int num_leaves = 0;
 
   // in depth_wise growing, we feed loss_chg with 0.0 since it is not used anyway
-  qexpand_depth_wise_.push_back(ExpandEntry(0, p_tree->GetDepth(0), 0.0, timestamp++));
+  qexpand_depth_wise_.emplace_back(ExpandEntry(0, p_tree->GetDepth(0), 0.0, timestamp++));
   ++num_leaves;
   for (int depth = 0; depth < param_.max_depth + 1; depth++) {
     int starting_index = std::numeric_limits<int>::max();

tests/ci_build/clang_tidy.sh

@@ -1,12 +1,18 @@
 #!/bin/bash
 
-rm -rf gtest googletest-release-1.7.0
+export GTEST_PKG_NAME=release-1.8.1
-wget -nc https://github.com/google/googletest/archive/release-1.7.0.zip
+export GTEST_DIR_NAME=googletest-${GTEST_PKG_NAME}  # uncompressed directory
-unzip -n release-1.7.0.zip
+export GTEST_ZIP_FILE=${GTEST_PKG_NAME}.zip	    # downloaded zip ball name
-mv googletest-release-1.7.0 gtest && cd gtest
-cmake . && make
-mkdir lib && mv libgtest.a lib
-cd ..
-rm -rf release-1.7.0.zip*
 
-python3 tests/ci_build/tidy.py --gtest-path=${PWD}/gtest
+rm -rf gtest googletest-release*
+
+wget -nc https://github.com/google/googletest/archive/${GTEST_ZIP_FILE}
+unzip -n ${GTEST_ZIP_FILE}
+mv ${GTEST_DIR_NAME} gtest && cd gtest
+cmake . -DCMAKE_INSTALL_PREFIX=./ins && make
+make install
+
+cd ..
+rm ${GTEST_ZIP_FILE}
+
+python3 tests/ci_build/tidy.py --gtest-path=${PWD}/gtest/ins

tests/ci_build/test_tidy.cc

@@ -0,0 +1,11 @@
+#include <iostream>
+#include <vector>
+
+struct Foo {
+  int bar_;
+};
+
+int main() {
+  std::vector<Foo> values;
+  values.push_back(Foo());
+}

tests/ci_build/tidy.py

@@ -5,23 +5,24 @@ import json
 from multiprocessing import Pool, cpu_count
 import shutil
 import os
+import sys
 import re
 import argparse
 
 
 def call(args):
     '''Subprocess run wrapper.'''
-    completed = subprocess.run(args, stdout=subprocess.PIPE,
+    completed = subprocess.run(args,
-                               stderr=subprocess.DEVNULL)
+                               stdout=subprocess.PIPE,
+                               stderr=subprocess.PIPE)
     error_msg = completed.stdout.decode('utf-8')
-    matched = re.match('.*xgboost.*warning.*', error_msg,
+    matched = re.search('(src|tests)/.*warning:', error_msg,
-                       re.MULTILINE | re.DOTALL)
+                        re.MULTILINE)
     if matched is None:
         return_code = 0
     else:
-        print(error_msg, '\n')
         return_code = 1
-    return completed.returncode | return_code
+    return (completed.returncode, return_code, error_msg)
 
 
 class ClangTidy(object):
@@ -69,8 +70,8 @@ class ClangTidy(object):
 
     def _configure_flags(self, path, command):
         common_args = ['clang-tidy',
-                       # "-header-filter='(xgboost\\/src|xgboost\\/include)'",
+                       "-header-filter='(xgboost\\/src|xgboost\\/include)'",
-                       '-config='+str(self.clang_tidy)]
+                       '-config='+self.clang_tidy]
         common_args.append(path)
         common_args.append('--')
 
@@ -112,6 +113,9 @@ class ClangTidy(object):
         def should_lint(path):
             if not self.cpp_lint and path.endswith('.cc'):
                 return False
+            isxgb = path.find('rabit') == -1
+            isxgb = isxgb and path.find('dmlc-core') == -1
+            if isxgb:
                 return True
 
         cdb_file = os.path.join(self.cdb_path, 'compile_commands.json')
@@ -120,6 +124,7 @@ class ClangTidy(object):
         tidy_file = os.path.join(self.root_path, '.clang-tidy')
         with open(tidy_file) as fd:
             self.clang_tidy = yaml.load(fd)
+            self.clang_tidy = str(self.clang_tidy)
         all_files = []
         for entry in self.compile_commands:
             path = entry['file']
@@ -132,15 +137,59 @@ class ClangTidy(object):
     def run(self):
         '''Run clang-tidy.'''
         all_files = self._configure()
+        passed = True
+        BAR = '-'*32
         with Pool(cpu_count()) as pool:
             results = pool.map(call, all_files)
-        passed = True
+            for (process_status, tidy_status, msg) in results:
-        if 1 in results:
+                # Don't enforce clang-tidy to pass for now due to namespace
-            print('Please correct clang-tidy warnings.')
+                # for cub in thrust is not correct.
+                if tidy_status == 1:
                     passed = False
+                    print(BAR, '\n'
+                          'Process return code:', process_status, ', ',
+                          'Tidy result code:', tidy_status, ', ',
+                          'Message:\n', msg,
+                          BAR, '\n')
+        if not passed:
+            print('Please correct clang-tidy warnings.')
         return passed
 
 
+def test_tidy():
+    '''See if clang-tidy and our regex is working correctly.  There are
+many subtleties we need to be careful.  For instances:
+
+    * Is the string re-directed to pipe encoded as UTF-8? or is it
+bytes?
+
+    * On Jenkins there's no 'xgboost' directory, are we catching the
+right keywords?
+
+    * Should we use re.DOTALL?
+
+    * Should we use re.MULTILINE?
+
+    Tests here are not thorough, at least we want to guarantee tidy is
+    not missing anything on Jenkins.
+
+    '''
+    root_path = os.path.abspath(os.path.curdir)
+    tidy_file = os.path.join(root_path, '.clang-tidy')
+    test_file_path = os.path.join(root_path,
+                                  'tests', 'ci_build', 'test_tidy.cc')
+
+    with open(tidy_file) as fd:
+        tidy_config = fd.read()
+        tidy_config = str(tidy_config)
+    tidy_config = '-config='+tidy_config
+    args = ['clang-tidy', tidy_config, test_file_path]
+    (proc_code, tidy_status, error_msg) = call(args)
+    assert proc_code == 0
+    assert tidy_status == 1
+    print('clang-tidy is working.')
+
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Run clang-tidy.')
     parser.add_argument('--cpp', type=int, default=1)
@@ -148,8 +197,10 @@ if __name__ == '__main__':
     parser.add_argument('--gtest-path', required=True,
                         help='Full path of Google Test library directory')
     args = parser.parse_args()
+
+    test_tidy()
+
     with ClangTidy(args.gtest_path, args.cpp, args.cuda) as linter:
         passed = linter.run()
-    # Uncomment it once the code base is clang-tidy conformant.
+    if not passed:
-    # if not passed:
+        sys.exit(1)
-    #     sys.exit(1)

tests/cpp/common/test_span.cc

@@ -172,7 +172,7 @@ struct BaseClass {
   virtual void operator()() {}
 };
 struct DerivedClass : public BaseClass {
-  virtual void operator()() {}
+  void operator()() override {}
 };
 
 TEST(Span, FromOther) {

tests/cpp/common/test_span.cu

@@ -15,6 +15,7 @@ namespace xgboost {
 namespace common {
 
 struct TestStatus {
+ private:
   int *status_;
 
  public:
@@ -28,33 +29,35 @@ struct TestStatus {
     dh::safe_cuda(cudaFree(status_));
   }
 
-  int get() {
+  int Get() {
     int h_status;
     dh::safe_cuda(cudaMemcpy(&h_status, status_,
                              sizeof(int), cudaMemcpyDeviceToHost));
     return h_status;
   }
 
-  int* data() {
+  int* Data() {
     return status_;
   }
 };
 
-__global__ void test_from_other_kernel(Span<float> span) {
+__global__ void TestFromOtherKernel(Span<float> span) {
   // don't get optimized out
   size_t idx = threadIdx.x + blockIdx.x * blockDim.x;
 
-  if (idx >= span.size())
+  if (idx >= span.size()) {
     return;
   }
+}
 // Test converting different T
-  __global__ void test_from_other_kernel_const(Span<float const, 16> span) {
+  __global__ void TestFromOtherKernelConst(Span<float const, 16> span) {
   // don't get optimized out
   size_t idx = threadIdx.x + blockIdx.x * blockDim.x;
 
-  if (idx >= span.size())
+  if (idx >= span.size()) {
     return;
   }
+}
 
 /*!
  * \brief Here we just test whether the code compiles.
@@ -68,42 +71,44 @@ TEST(GPUSpan, FromOther) {
   // dynamic extent
   {
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_from_other_kernel<<<1, 16>>>(span);
+    TestFromOtherKernel<<<1, 16>>>(span);
   }
   {
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_from_other_kernel_const<<<1, 16>>>(span);
+    TestFromOtherKernelConst<<<1, 16>>>(span);
   }
   // static extent
   {
     Span<float, 16> span(d_vec.data().get(), d_vec.data().get() + 16);
-    test_from_other_kernel<<<1, 16>>>(span);
+    TestFromOtherKernel<<<1, 16>>>(span);
   }
   {
     Span<float, 16> span(d_vec.data().get(), d_vec.data().get() + 16);
-    test_from_other_kernel_const<<<1, 16>>>(span);
+    TestFromOtherKernelConst<<<1, 16>>>(span);
   }
 }
 
 TEST(GPUSpan, Assignment) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestAssignment{status.data()});
+  dh::LaunchN(0, 16, TestAssignment{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpan, TestStatus) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestTestStatus{status.data()});
+  dh::LaunchN(0, 16, TestTestStatus{status.Data()});
-  ASSERT_EQ(status.get(), -1);
+  ASSERT_EQ(status.Get(), -1);
 }
 
 template <typename T>
 struct TestEqual {
+ private:
   T *lhs_, *rhs_;
   int *status_;
 
+ public:
   TestEqual(T* _lhs, T* _rhs, int * _status) :
       lhs_(_lhs), rhs_(_rhs), status_(_status) {}
 
@@ -140,10 +145,10 @@ TEST(GPUSpan, WithTrust) {
 
     dh::LaunchN(0, 16, TestEqual<float>{
         thrust::raw_pointer_cast(d_vec1.data()),
-        s.data(), status.data()});
+        s.data(), status.Data()});
-    ASSERT_EQ(status.get(), 1);
+    ASSERT_EQ(status.Get(), 1);
 
-    // FIXME: memory error!
+    // FIXME(trivialfis): memory error!
     // bool res = thrust::equal(thrust::device,
     //                          d_vec.begin(), d_vec.end(),
     //                          s.begin());
@@ -153,23 +158,23 @@ TEST(GPUSpan, WithTrust) {
 TEST(GPUSpan, BeginEnd) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestBeginEnd{status.data()});
+  dh::LaunchN(0, 16, TestBeginEnd{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpan, RBeginREnd) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestRBeginREnd{status.data()});
+  dh::LaunchN(0, 16, TestRBeginREnd{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
-__global__ void test_modify_kernel(Span<float> span) {
+__global__ void TestModifyKernel(Span<float> span) {
   size_t idx = threadIdx.x + blockIdx.x * blockDim.x;
 
-  if (idx >= span.size())
+  if (idx >= span.size()) {
     return;
-
+  }
   span[idx] = span.size() - idx;
 }
 
@@ -182,7 +187,7 @@ TEST(GPUSpan, Modify) {
 
   Span<float> span (d_vec.data().get(), d_vec.size());
 
-  test_modify_kernel<<<1, 16>>>(span);
+  TestModifyKernel<<<1, 16>>>(span);
 
   for (size_t i = 0; i < d_vec.size(); ++i) {
     ASSERT_EQ(d_vec[i], d_vec.size() - i);
@@ -192,21 +197,23 @@ TEST(GPUSpan, Modify) {
 TEST(GPUSpan, Observers) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestObservers{status.data()});
+  dh::LaunchN(0, 16, TestObservers{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpan, Compare) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestIterCompare{status.data()});
+  dh::LaunchN(0, 16, TestIterCompare{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 struct TestElementAccess {
+ private:
   Span<float> span_;
 
-  XGBOOST_DEVICE TestElementAccess (Span<float> _span) : span_(_span) {}
+ public:
+  XGBOOST_DEVICE explicit TestElementAccess (Span<float> _span) : span_(_span) {}
 
   XGBOOST_DEVICE float operator()(size_t _idx) {
     float tmp = span_[_idx];
@@ -232,16 +239,16 @@ TEST(GPUSpan, ElementAccess) {
   std::string output = testing::internal::GetCapturedStdout();
 }
 
-__global__ void test_first_dynamic_kernel(Span<float> _span) {
+__global__ void TestFirstDynamicKernel(Span<float> _span) {
   _span.first<-1>();
 }
-__global__ void test_first_static_kernel(Span<float> _span) {
+__global__ void TestFirstStaticKernel(Span<float> _span) {
   _span.first(-1);
 }
-__global__ void test_last_dynamic_kernel(Span<float> _span) {
+__global__ void TestLastDynamicKernel(Span<float> _span) {
   _span.last<-1>();
 }
-__global__ void test_last_static_kernel(Span<float> _span) {
+__global__ void TestLastStaticKernel(Span<float> _span) {
   _span.last(-1);
 }
 
@@ -256,7 +263,7 @@ TEST(GPUSpan, FirstLast) {
     thrust::copy(h_vec.begin(), h_vec.end(), d_vec.begin());
 
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_first_dynamic_kernel<<<1, 1>>>(span);
+    TestFirstDynamicKernel<<<1, 1>>>(span);
   };
   testing::internal::CaptureStdout();
   EXPECT_DEATH(lambda_first_dy(), "");
@@ -270,7 +277,7 @@ TEST(GPUSpan, FirstLast) {
     thrust::copy(h_vec.begin(), h_vec.end(), d_vec.begin());
 
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_first_static_kernel<<<1, 1>>>(span);
+    TestFirstStaticKernel<<<1, 1>>>(span);
   };
   testing::internal::CaptureStdout();
   EXPECT_DEATH(lambda_first_static(), "");
@@ -284,7 +291,7 @@ TEST(GPUSpan, FirstLast) {
     thrust::copy(h_vec.begin(), h_vec.end(), d_vec.begin());
 
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_last_dynamic_kernel<<<1, 1>>>(span);
+    TestLastDynamicKernel<<<1, 1>>>(span);
   };
   testing::internal::CaptureStdout();
   EXPECT_DEATH(lambda_last_dy(), "");
@@ -298,7 +305,7 @@ TEST(GPUSpan, FirstLast) {
     thrust::copy(h_vec.begin(), h_vec.end(), d_vec.begin());
 
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_last_static_kernel<<<1, 1>>>(span);
+    TestLastStaticKernel<<<1, 1>>>(span);
   };
   testing::internal::CaptureStdout();
   EXPECT_DEATH(lambda_last_static(), "");
@@ -306,10 +313,10 @@ TEST(GPUSpan, FirstLast) {
 }
 
 
-__global__ void test_subspan_dynamic_kernel(Span<float> _span) {
+__global__ void TestSubspanDynamicKernel(Span<float> _span) {
   _span.subspan(16, 0);
 }
-__global__ void test_subspan_static_kernel(Span<float> _span) {
+__global__ void TestSubspanStaticKernel(Span<float> _span) {
   _span.subspan<16>();
 }
 TEST(GPUSpan, Subspan) {
@@ -321,7 +328,7 @@ TEST(GPUSpan, Subspan) {
     thrust::copy(h_vec.begin(), h_vec.end(), d_vec.begin());
 
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_subspan_dynamic_kernel<<<1, 1>>>(span);
+    TestSubspanDynamicKernel<<<1, 1>>>(span);
   };
   testing::internal::CaptureStdout();
   EXPECT_DEATH(lambda_subspan_dynamic(), "");
@@ -335,7 +342,7 @@ TEST(GPUSpan, Subspan) {
     thrust::copy(h_vec.begin(), h_vec.end(), d_vec.begin());
 
     Span<float> span (d_vec.data().get(), d_vec.size());
-    test_subspan_static_kernel<<<1, 1>>>(span);
+    TestSubspanStaticKernel<<<1, 1>>>(span);
   };
   testing::internal::CaptureStdout();
   EXPECT_DEATH(lambda_subspan_static(), "");
@@ -345,43 +352,43 @@ TEST(GPUSpan, Subspan) {
 TEST(GPUSpanIter, Construct) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestIterConstruct{status.data()});
+  dh::LaunchN(0, 16, TestIterConstruct{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpanIter, Ref) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestIterRef{status.data()});
+  dh::LaunchN(0, 16, TestIterRef{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpanIter, Calculate) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestIterCalculate{status.data()});
+  dh::LaunchN(0, 16, TestIterCalculate{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpanIter, Compare) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestIterCompare{status.data()});
+  dh::LaunchN(0, 16, TestIterCompare{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpan, AsBytes) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestAsBytes{status.data()});
+  dh::LaunchN(0, 16, TestAsBytes{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 TEST(GPUSpan, AsWritableBytes) {
   dh::safe_cuda(cudaSetDevice(0));
   TestStatus status;
-  dh::LaunchN(0, 16, TestAsWritableBytes{status.data()});
+  dh::LaunchN(0, 16, TestAsWritableBytes{status.Data()});
-  ASSERT_EQ(status.get(), 1);
+  ASSERT_EQ(status.Get(), 1);
 }
 
 }  // namespace common

tests/cpp/data/test_data.cc

@@ -13,7 +13,7 @@ TEST(SparsePage, PushCSC) {
 
   offset = {0, 1, 4};
   for (size_t i = 0; i < offset.back(); ++i) {
-    data.push_back(Entry(i, 0.1f));
+    data.emplace_back(Entry(i, 0.1f));
   }
 
   SparsePage other;
@@ -52,4 +52,4 @@ TEST(SparsePage, PushCSC) {
     ASSERT_EQ(inst[i].index, indices_sol[i % 3]);
   }
 }
-}
+}  // namespace xgboost

tests/cpp/data/test_simple_dmatrix.cc

@@ -27,7 +27,7 @@ TEST(SimpleDMatrix, RowAccess) {
   xgboost::DMatrix * dmat = xgboost::DMatrix::Load(tmp_file, false, false);
 
   // Loop over the batches and count the records
-  long row_count = 0;
+  int64_t row_count = 0;
   for (auto &batch : dmat->GetRowBatches()) {
     row_count += batch.Size();
   }
@@ -54,7 +54,7 @@ TEST(SimpleDMatrix, ColAccessWithoutBatches) {
   ASSERT_TRUE(dmat->SingleColBlock());
 
   // Loop over the batches and assert the data is as expected
-  long num_col_batch = 0;
+  int64_t num_col_batch = 0;
   for (const auto &batch : dmat->GetSortedColumnBatches()) {
     num_col_batch += 1;
     EXPECT_EQ(batch.Size(), dmat->Info().num_col_)

tests/cpp/data/test_sparse_page_dmatrix.cc

@@ -1,6 +1,8 @@
 // Copyright by Contributors
 #include <xgboost/data.h>
 #include <dmlc/filesystem.h>
+#include <cinttypes>
+
 #include "../../../src/data/sparse_page_dmatrix.h"
 
 #include "../helpers.h"
@@ -33,7 +35,7 @@ TEST(SparsePageDMatrix, RowAccess) {
   EXPECT_TRUE(FileExists(tmp_file + ".cache.row.page"));
 
   // Loop over the batches and count the records
-  long row_count = 0;
+  int64_t row_count = 0;
   for (auto &batch : dmat->GetRowBatches()) {
     row_count += batch.Size();
   }

tests/cpp/helpers.cc

@@ -4,13 +4,14 @@
 #include "./helpers.h"
 #include "xgboost/c_api.h"
 #include <random>
+#include <cinttypes>
 
 bool FileExists(const std::string& filename) {
   struct stat st;
   return stat(filename.c_str(), &st) == 0;
 }
 
-long GetFileSize(const std::string& filename) {
+int64_t GetFileSize(const std::string& filename) {
   struct stat st;
   stat(filename.c_str(), &st);
   return st.st_size;
@@ -30,7 +31,7 @@ void CreateBigTestData(const std::string& filename, size_t n_entries) {
   }
 }
 
-void _CheckObjFunction(xgboost::ObjFunction * obj,
+void CheckObjFunctionImpl(xgboost::ObjFunction * obj,
                           std::vector<xgboost::bst_float> preds,
                           std::vector<xgboost::bst_float> labels,
                           std::vector<xgboost::bst_float> weights,
@@ -64,7 +65,7 @@ void CheckObjFunction(xgboost::ObjFunction * obj,
   info.labels_.HostVector() = labels;
   info.weights_.HostVector() = weights;
 
-  _CheckObjFunction(obj, preds, labels, weights, info, out_grad, out_hess);
+  CheckObjFunctionImpl(obj, preds, labels, weights, info, out_grad, out_hess);
 }
 
 void CheckRankingObjFunction(xgboost::ObjFunction * obj,
@@ -80,7 +81,7 @@ void CheckRankingObjFunction(xgboost::ObjFunction * obj,
   info.weights_.HostVector() = weights;
   info.group_ptr_ = groups;
 
-  _CheckObjFunction(obj, preds, labels, weights, info, out_grad, out_hess);
+  CheckObjFunctionImpl(obj, preds, labels, weights, info, out_grad, out_hess);
 }
 
 

tests/cpp/metric/test_metric.cc

@@ -4,11 +4,16 @@
 #include "../helpers.h"
 
 TEST(Metric, UnknownMetric) {
-  xgboost::Metric * metric;
+  xgboost::Metric * metric = nullptr;
   EXPECT_ANY_THROW(metric = xgboost::Metric::Create("unknown_name"));
   EXPECT_NO_THROW(metric = xgboost::Metric::Create("rmse"));
-  delete metric;
+  if (metric) {
-  EXPECT_ANY_THROW(metric = xgboost::Metric::Create("unknown_name@1"));
-  EXPECT_NO_THROW(metric = xgboost::Metric::Create("error@0.5f"));
     delete metric;
   }
+  metric = nullptr;
+  EXPECT_ANY_THROW(metric = xgboost::Metric::Create("unknown_name@1"));
+  EXPECT_NO_THROW(metric = xgboost::Metric::Create("error@0.5f"));
+  if (metric) {
+    delete metric;
+  }
+}

tests/cpp/objective/test_objective.cc

@@ -4,8 +4,10 @@
 #include "../helpers.h"
 
 TEST(Objective, UnknownFunction) {
-  xgboost::ObjFunction* obj;
+  xgboost::ObjFunction* obj = nullptr;
   EXPECT_ANY_THROW(obj = xgboost::ObjFunction::Create("unknown_name"));
   EXPECT_NO_THROW(obj = xgboost::ObjFunction::Create("reg:linear"));
+  if (obj) {
     delete obj;
   }
+}

tests/cpp/objective/test_regression_obj.cc

@@ -85,7 +85,7 @@ TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
 TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson");
   std::vector<std::pair<std::string, std::string> > args;
-  args.push_back(std::make_pair("max_delta_step", "0.1f"));
+  args.emplace_back(std::make_pair("max_delta_step", "0.1f"));
   obj->Configure(args);
   CheckObjFunction(obj,
                    {   0,  0.1f,  0.9f,    1,    0,  0.1f,  0.9f,    1},
@@ -176,7 +176,7 @@ TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
 TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:tweedie");
   std::vector<std::pair<std::string, std::string> > args;
-  args.push_back(std::make_pair("tweedie_variance_power", "1.1f"));
+  args.emplace_back(std::make_pair("tweedie_variance_power", "1.1f"));
   obj->Configure(args);
   CheckObjFunction(obj,
                    {   0,  0.1f,  0.9f,    1, 0,  0.1f,  0.9f,    1},

tests/cpp/predictor/test_cpu_predictor.cc

@@ -41,21 +41,20 @@ TEST(cpu_predictor, Test) {
   // Test predict leaf
   std::vector<float> leaf_out_predictions;
   cpu_predictor->PredictLeaf((*dmat).get(), &leaf_out_predictions, model);
-  for (int i = 0; i < leaf_out_predictions.size(); i++) {
+  for (auto v : leaf_out_predictions) {
-    ASSERT_EQ(leaf_out_predictions[i], 0);
+    ASSERT_EQ(v, 0);
   }
 
   // Test predict contribution
   std::vector<float> out_contribution;
   cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model);
-  for (int i = 0; i < out_contribution.size(); i++) {
+  for (auto const& contri : out_contribution) {
-    ASSERT_EQ(out_contribution[i], 1.5);
+    ASSERT_EQ(contri, 1.5);
   }
-
   // Test predict contribution (approximate method)
   cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model, true);
-  for (int i = 0; i < out_contribution.size(); i++) {
+  for (auto const& contri : out_contribution) {
-    ASSERT_EQ(out_contribution[i], 1.5);
+    ASSERT_EQ(contri, 1.5);
   }
 
   delete dmat;

tests/cpp/test_learner.cc

@@ -8,7 +8,7 @@
 namespace xgboost {
 
 TEST(Learner, Basic) {
-  typedef std::pair<std::string, std::string> Arg;
+  using Arg = std::pair<std::string, std::string>;
   auto args = {Arg("tree_method", "exact")};
   auto mat_ptr = CreateDMatrix(10, 10, 0);
   std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};

tests/cpp/tree/test_gpu_exact.cu

@@ -20,13 +20,13 @@ TEST(GPUExact, Update) {
   auto* p_gpuexact_maker = TreeUpdater::Create("grow_gpu");
   p_gpuexact_maker->Init(args);
 
-  size_t constexpr n_rows = 4;
+  size_t constexpr kNRows = 4;
-  size_t constexpr n_cols = 8;
+  size_t constexpr kNCols = 8;
-  bst_float constexpr sparsity = 0.0f;
+  bst_float constexpr kSparsity = 0.0f;
 
-  auto dmat = CreateDMatrix(n_rows, n_cols, sparsity, 3);
+  auto dmat = CreateDMatrix(kNRows, kNCols, kSparsity, 3);
-  std::vector<GradientPair> h_gpair(n_rows);
+  std::vector<GradientPair> h_gpair(kNRows);
-  for (size_t i = 0; i < n_rows; ++i) {
+  for (size_t i = 0; i < kNRows; ++i) {
       h_gpair[i] = GradientPair(i % 2, 1);
   }
   HostDeviceVector<GradientPair> gpair (h_gpair);

tests/cpp/tree/test_gpu_hist.cu

@@ -46,20 +46,20 @@ void BuildGidx(DeviceShard<GradientSumT>* shard, int n_rows, int n_cols,
 }
 
 TEST(GpuHist, BuildGidxDense) {
-  int const n_rows = 16, n_cols = 8;
+  int constexpr kNRows = 16, kNCols = 8;
   TrainParam param;
   param.max_depth = 1;
   param.n_gpus = 1;
   param.max_leaves = 0;
 
-  DeviceShard<GradientPairPrecise> shard(0, 0, n_rows, param);
+  DeviceShard<GradientPairPrecise> shard(0, 0, kNRows, param);
-  BuildGidx(&shard, n_rows, n_cols);
+  BuildGidx(&shard, kNRows, kNCols);
 
   std::vector<common::CompressedByteT> h_gidx_buffer;
   h_gidx_buffer = shard.gidx_buffer.AsVector();
   common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
 
-  ASSERT_EQ(shard.row_stride, n_cols);
+  ASSERT_EQ(shard.row_stride, kNCols);
 
   std::vector<uint32_t> solution = {
     0, 3, 8,  9, 14, 17, 20, 21,
@@ -79,20 +79,20 @@ TEST(GpuHist, BuildGidxDense) {
     2, 4, 8, 10, 14, 15, 19, 22,
     1, 4, 7, 10, 14, 16, 19, 21,
   };
-  for (size_t i = 0; i < n_rows * n_cols; ++i) {
+  for (size_t i = 0; i < kNRows * kNCols; ++i) {
     ASSERT_EQ(solution[i], gidx[i]);
   }
 }
 
 TEST(GpuHist, BuildGidxSparse) {
-  int const n_rows = 16, n_cols = 8;
+  int constexpr kNRows = 16, kNCols = 8;
   TrainParam param;
   param.max_depth = 1;
   param.n_gpus = 1;
   param.max_leaves = 0;
 
-  DeviceShard<GradientPairPrecise> shard(0, 0, n_rows, param);
+  DeviceShard<GradientPairPrecise> shard(0, 0, kNRows, param);
-  BuildGidx(&shard, n_rows, n_cols, 0.9f);
+  BuildGidx(&shard, kNRows, kNCols, 0.9f);
 
   std::vector<common::CompressedByteT> h_gidx_buffer;
   h_gidx_buffer = shard.gidx_buffer.AsVector();
@@ -106,7 +106,7 @@ TEST(GpuHist, BuildGidxSparse) {
     24, 24, 24, 24, 24,  5, 24, 24,  0, 16, 24, 15, 24, 24, 24, 24,
     24,  7, 14, 16,  4, 24, 24, 24, 24, 24,  9, 24, 24,  1, 24, 24
   };
-  for (size_t i = 0; i < n_rows * shard.row_stride; ++i) {
+  for (size_t i = 0; i < kNRows * shard.row_stride; ++i) {
     ASSERT_EQ(solution[i], gidx[i]);
   }
 }
@@ -128,27 +128,27 @@ std::vector<GradientPairPrecise> GetHostHistGpair() {
 
 template <typename GradientSumT>
 void TestBuildHist(GPUHistBuilderBase<GradientSumT>& builder) {
-  int const n_rows = 16, n_cols = 8;
+  int const kNRows = 16, kNCols = 8;
 
   TrainParam param;
   param.max_depth = 6;
   param.n_gpus = 1;
   param.max_leaves = 0;
 
-  DeviceShard<GradientSumT> shard(0, 0, n_rows, param);
+  DeviceShard<GradientSumT> shard(0, 0, kNRows, param);
 
-  BuildGidx(&shard, n_rows, n_cols);
+  BuildGidx(&shard, kNRows, kNCols);
 
   xgboost::SimpleLCG gen;
   xgboost::SimpleRealUniformDistribution<bst_float> dist(0.0f, 1.0f);
-  std::vector<GradientPair> h_gpair(n_rows);
+  std::vector<GradientPair> h_gpair(kNRows);
-  for (size_t i = 0; i < h_gpair.size(); ++i) {
+  for (auto &gpair : h_gpair) {
     bst_float grad = dist(&gen);
     bst_float hess = dist(&gen);
-    h_gpair[i] = GradientPair(grad, hess);
+    gpair = GradientPair(grad, hess);
   }
 
-  thrust::device_vector<GradientPair> gpair (n_rows);
+  thrust::device_vector<GradientPair> gpair (kNRows);
   gpair = h_gpair;
 
   int num_symbols = shard.n_bins + 1;
@@ -164,7 +164,7 @@ void TestBuildHist(GPUHistBuilderBase<GradientSumT>& builder) {
                                                    num_symbols);
 
   shard.ridx_segments.resize(1);
-  shard.ridx_segments[0] = Segment(0, n_rows);
+  shard.ridx_segments[0] = Segment(0, kNRows);
   shard.hist.AllocateHistogram(0);
   shard.gpair.copy(gpair.begin(), gpair.end());
   thrust::sequence(shard.ridx.CurrentDVec().tbegin(),
@@ -175,11 +175,11 @@ void TestBuildHist(GPUHistBuilderBase<GradientSumT>& builder) {
 
   auto node_histogram = d_hist.GetNodeHistogram(0);
   // d_hist.data stored in float, not gradient pair
-  thrust::host_vector<GradientSumT> h_result (d_hist.data.size()/2);
+  thrust::host_vector<GradientSumT> h_result (d_hist.Data().size() / 2);
   size_t data_size =
       sizeof(GradientSumT) /
       (sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT));
-  data_size *= d_hist.data.size();
+  data_size *= d_hist.Data().size();
   dh::safe_cuda(cudaMemcpy(h_result.data(), node_histogram.data(), data_size,
                            cudaMemcpyDeviceToHost));
 
@@ -224,8 +224,8 @@ common::HistCutMatrix GetHostCutMatrix () {
 
 // TODO(trivialfis): This test is over simplified.
 TEST(GpuHist, EvaluateSplits) {
-  constexpr int n_rows = 16;
+  constexpr int kNRows = 16;
-  constexpr int n_cols = 8;
+  constexpr int kNCols = 8;
 
   TrainParam param;
   param.max_depth = 1;
@@ -240,14 +240,15 @@ TEST(GpuHist, EvaluateSplits) {
   param.reg_lambda = 0;
   param.max_delta_step = 0.0;
 
-  for (size_t i = 0; i < n_cols; ++i) {
+  for (size_t i = 0; i < kNCols; ++i) {
     param.monotone_constraints.emplace_back(0);
   }
 
   int max_bins = 4;
 
   // Initialize DeviceShard
-  std::unique_ptr<DeviceShard<GradientPairPrecise>> shard {new DeviceShard<GradientPairPrecise>(0, 0, n_rows, param)};
+  std::unique_ptr<DeviceShard<GradientPairPrecise>> shard {
+    new DeviceShard<GradientPairPrecise>(0, 0, kNRows, param)};
   // Initialize DeviceShard::node_sum_gradients
   shard->node_sum_gradients = {{6.4f, 12.8f}};
 
@@ -257,17 +258,17 @@ TEST(GpuHist, EvaluateSplits) {
   // Copy cut matrix to device.
   DeviceShard<GradientPairPrecise>::DeviceHistCutMatrix cut;
   shard->ba.Allocate(0,
-                     &(shard->cut_.feature_segments), cmat.row_ptr.size(),
+                     &(shard->d_cut.feature_segments), cmat.row_ptr.size(),
-                     &(shard->cut_.min_fvalue), cmat.min_val.size(),
+                     &(shard->d_cut.min_fvalue), cmat.min_val.size(),
-                     &(shard->cut_.gidx_fvalue_map), 24,
+                     &(shard->d_cut.gidx_fvalue_map), 24,
-                     &(shard->monotone_constraints), n_cols);
+                     &(shard->monotone_constraints), kNCols);
-  shard->cut_.feature_segments.copy(cmat.row_ptr.begin(), cmat.row_ptr.end());
+  shard->d_cut.feature_segments.copy(cmat.row_ptr.begin(), cmat.row_ptr.end());
-  shard->cut_.gidx_fvalue_map.copy(cmat.cut.begin(), cmat.cut.end());
+  shard->d_cut.gidx_fvalue_map.copy(cmat.cut.begin(), cmat.cut.end());
   shard->monotone_constraints.copy(param.monotone_constraints.begin(),
                                    param.monotone_constraints.end());
 
   // Initialize DeviceShard::hist
-  shard->hist.Init(0, (max_bins - 1) * n_cols);
+  shard->hist.Init(0, (max_bins - 1) * kNCols);
   shard->hist.AllocateHistogram(0);
   // Each row of hist_gpair represents gpairs for one feature.
   // Each entry represents a bin.
@@ -278,16 +279,16 @@ TEST(GpuHist, EvaluateSplits) {
     hist.push_back(pair.GetHess());
   }
 
-  ASSERT_EQ(shard->hist.data.size(), hist.size());
+  ASSERT_EQ(shard->hist.Data().size(), hist.size());
   thrust::copy(hist.begin(), hist.end(),
-               shard->hist.data.begin());
+               shard->hist.Data().begin());
 
   // Initialize GPUHistMaker
   GPUHistMakerSpecialised<GradientPairPrecise> hist_maker =
       GPUHistMakerSpecialised<GradientPairPrecise>();
   hist_maker.param_ = param;
   hist_maker.shards_.push_back(std::move(shard));
-  hist_maker.column_sampler_.Init(n_cols,
+  hist_maker.column_sampler_.Init(kNCols,
                                   param.colsample_bynode,
                                   param.colsample_bylevel,
                                   param.colsample_bytree,
@@ -295,8 +296,8 @@ TEST(GpuHist, EvaluateSplits) {
 
   RegTree tree;
   MetaInfo info;
-  info.num_row_ = n_rows;
+  info.num_row_ = kNRows;
-  info.num_col_ = n_cols;
+  info.num_col_ = kNCols;
 
   hist_maker.info_ = &info;
   hist_maker.node_value_constraints_.resize(1);
@@ -313,30 +314,30 @@ TEST(GpuHist, EvaluateSplits) {
 TEST(GpuHist, ApplySplit) {
   GPUHistMakerSpecialised<GradientPairPrecise> hist_maker =
       GPUHistMakerSpecialised<GradientPairPrecise>();
-  int constexpr nid = 0;
+  int constexpr kNId = 0;
-  int constexpr n_rows = 16;
+  int constexpr kNRows = 16;
-  int constexpr n_cols = 8;
+  int constexpr kNCols = 8;
 
   TrainParam param;
   std::vector<std::pair<std::string, std::string>> args = {};
   param.InitAllowUnknown(args);
 
   // Initialize shard
-  for (size_t i = 0; i < n_cols; ++i) {
+  for (size_t i = 0; i < kNCols; ++i) {
     param.monotone_constraints.emplace_back(0);
   }
 
   hist_maker.shards_.resize(1);
-  hist_maker.shards_[0].reset(new DeviceShard<GradientPairPrecise>(0, 0, n_rows, param));
+  hist_maker.shards_[0].reset(new DeviceShard<GradientPairPrecise>(0, 0, kNRows, param));
 
   auto& shard = hist_maker.shards_.at(0);
   shard->ridx_segments.resize(3);  // 3 nodes.
   shard->node_sum_gradients.resize(3);
 
-  shard->ridx_segments[0] = Segment(0, n_rows);
+  shard->ridx_segments[0] = Segment(0, kNRows);
-  shard->ba.Allocate(0, &(shard->ridx), n_rows,
+  shard->ba.Allocate(0, &(shard->ridx), kNRows,
-                     &(shard->position), n_rows);
+                     &(shard->position), kNRows);
-  shard->row_stride = n_cols;
+  shard->row_stride = kNCols;
   thrust::sequence(shard->ridx.CurrentDVec().tbegin(),
                    shard->ridx.CurrentDVec().tend());
   // Initialize GPUHistMaker
@@ -349,31 +350,30 @@ TEST(GpuHist, ApplySplit) {
                    GradientPair(8.2, 2.8), GradientPair(6.3, 3.6),
                    GPUTrainingParam(param));
   GPUHistMakerSpecialised<GradientPairPrecise>::ExpandEntry candidate_entry {0, 0, candidate, 0};
-  candidate_entry.nid = nid;
+  candidate_entry.nid = kNId;
 
   auto const& nodes = tree.GetNodes();
-  size_t n_nodes = nodes.size();
 
   // Used to get bin_id in update position.
   common::HistCutMatrix cmat = GetHostCutMatrix();
   hist_maker.hmat_ = cmat;
 
   MetaInfo info;
-  info.num_row_ = n_rows;
+  info.num_row_ = kNRows;
-  info.num_col_ = n_cols;
+  info.num_col_ = kNCols;
-  info.num_nonzero_ = n_rows * n_cols;  // Dense
+  info.num_nonzero_ = kNRows * kNCols;  // Dense
 
   // Initialize gidx
   int n_bins = 24;
-  int row_stride = n_cols;
+  int row_stride = kNCols;
   int num_symbols = n_bins + 1;
   size_t compressed_size_bytes =
       common::CompressedBufferWriter::CalculateBufferSize(
-          row_stride * n_rows, num_symbols);
+          row_stride * kNRows, num_symbols);
   shard->ba.Allocate(0, &(shard->gidx_buffer), compressed_size_bytes);
 
   common::CompressedBufferWriter wr(num_symbols);
-  std::vector<int> h_gidx (n_rows * row_stride);
+  std::vector<int> h_gidx (kNRows * row_stride);
   std::iota(h_gidx.begin(), h_gidx.end(), 0);
   std::vector<common::CompressedByteT> h_gidx_compressed (compressed_size_bytes);
 
@@ -387,10 +387,10 @@ TEST(GpuHist, ApplySplit) {
   hist_maker.ApplySplit(candidate_entry, &tree);
   hist_maker.UpdatePosition(candidate_entry, &tree);
 
-  ASSERT_FALSE(tree[nid].IsLeaf());
+  ASSERT_FALSE(tree[kNId].IsLeaf());
 
-  int left_nidx = tree[nid].LeftChild();
+  int left_nidx = tree[kNId].LeftChild();
-  int right_nidx = tree[nid].RightChild();
+  int right_nidx = tree[kNId].RightChild();
 
   ASSERT_EQ(shard->ridx_segments[left_nidx].begin, 0);
   ASSERT_EQ(shard->ridx_segments[left_nidx].end, 6);

tests/cpp/tree/test_prune.cc

@@ -13,14 +13,14 @@ namespace xgboost {
 namespace tree {
 
 TEST(Updater, Prune) {
-  int constexpr n_rows = 32, n_cols = 16;
+  int constexpr kNRows = 32, kNCols = 16;
 
   std::vector<std::pair<std::string, std::string>> cfg;
-  cfg.push_back(std::pair<std::string, std::string>(
+  cfg.emplace_back(std::pair<std::string, std::string>(
-      "num_feature", std::to_string(n_cols)));
+      "num_feature", std::to_string(kNCols)));
-  cfg.push_back(std::pair<std::string, std::string>(
+  cfg.emplace_back(std::pair<std::string, std::string>(
       "min_split_loss", "10"));
-  cfg.push_back(std::pair<std::string, std::string>(
+  cfg.emplace_back(std::pair<std::string, std::string>(
       "silent", "1"));
 
   // These data are just place holders.

tests/cpp/tree/test_quantile_hist.cc

@@ -133,12 +133,12 @@ class QuantileHistMock : public QuantileHistMaker {
       std::vector<GradientPair> row_gpairs =
           { {1.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {2.27f, 0.28f},
             {0.27f, 0.29f}, {0.37f, 0.39f}, {-0.47f, 0.49f}, {0.57f, 0.59f} };
-      size_t constexpr max_bins = 4;
+      size_t constexpr kMaxBins = 4;
-      auto dmat = CreateDMatrix(n_rows, n_cols, 0, 3);
+      auto dmat = CreateDMatrix(kNRows, kNCols, 0, 3);
         // dense, no missing values
 
       common::GHistIndexMatrix gmat;
-      gmat.Init((*dmat).get(), max_bins);
+      gmat.Init((*dmat).get(), kMaxBins);
 
       RealImpl::InitData(gmat, row_gpairs, *(*dmat), tree);
       hist_.AddHistRow(0);
@@ -167,7 +167,8 @@ class QuantileHistMock : public QuantileHistMaker {
       // 2) no regularization, i.e. set min_child_weight, reg_lambda, reg_alpha,
       //    and max_delta_step to 0.
       bst_float best_split_gain = 0.0f;
-      size_t best_split_threshold, best_split_feature;
+      size_t best_split_threshold = std::numeric_limits<size_t>::max();
+      size_t best_split_feature = std::numeric_limits<size_t>::max();
       // Enumerate all features
       for (size_t fid = 0; fid < num_feature; ++fid) {
         const size_t bin_id_min = gmat.cut.row_ptr[fid];
@@ -213,56 +214,56 @@ class QuantileHistMock : public QuantileHistMaker {
     }
   };
 
-  int static constexpr n_rows = 8, n_cols = 16;
+  int static constexpr kNRows = 8, kNCols = 16;
-  std::shared_ptr<xgboost::DMatrix> *dmat;
+  std::shared_ptr<xgboost::DMatrix> *dmat_;
-  const std::vector<std::pair<std::string, std::string> > cfg;
+  const std::vector<std::pair<std::string, std::string> > cfg_;
   std::shared_ptr<BuilderMock> builder_;
 
  public:
   explicit QuantileHistMock(
       const std::vector<std::pair<std::string, std::string> >& args) :
-      cfg{args} {
+      cfg_{args} {
     QuantileHistMaker::Init(args);
     builder_.reset(
         new BuilderMock(
             param_,
             std::move(pruner_),
             std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone())));
-    dmat = CreateDMatrix(n_rows, n_cols, 0.8, 3);
+    dmat_ = CreateDMatrix(kNRows, kNCols, 0.8, 3);
   }
-  ~QuantileHistMock() { delete dmat; }
+  ~QuantileHistMock() override { delete dmat_; }
 
-  static size_t GetNumColumns() { return n_cols; }
+  static size_t GetNumColumns() { return kNCols; }
 
   void TestInitData() {
-    size_t constexpr max_bins = 4;
+    size_t constexpr kMaxBins = 4;
     common::GHistIndexMatrix gmat;
-    gmat.Init((*dmat).get(), max_bins);
+    gmat.Init((*dmat_).get(), kMaxBins);
 
     RegTree tree = RegTree();
-    tree.param.InitAllowUnknown(cfg);
+    tree.param.InitAllowUnknown(cfg_);
 
     std::vector<GradientPair> gpair =
         { {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f},
           {0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f} };
 
-    builder_->TestInitData(gmat, gpair, dmat->get(), tree);
+    builder_->TestInitData(gmat, gpair, dmat_->get(), tree);
   }
 
   void TestBuildHist() {
     RegTree tree = RegTree();
-    tree.param.InitAllowUnknown(cfg);
+    tree.param.InitAllowUnknown(cfg_);
 
-    size_t constexpr max_bins = 4;
+    size_t constexpr kMaxBins = 4;
     common::GHistIndexMatrix gmat;
-    gmat.Init((*dmat).get(), max_bins);
+    gmat.Init((*dmat_).get(), kMaxBins);
 
-    builder_->TestBuildHist(0, gmat, *(*dmat).get(), tree);
+    builder_->TestBuildHist(0, gmat, *(*dmat_).get(), tree);
   }
 
   void TestEvaluateSplit() {
     RegTree tree = RegTree();
-    tree.param.InitAllowUnknown(cfg);
+    tree.param.InitAllowUnknown(cfg_);
 
     builder_->TestEvaluateSplit(gmatb_, tree);
   }

tests/cpp/tree/test_refresh.cc

@@ -13,15 +13,15 @@ namespace xgboost {
 namespace tree {
 
 TEST(Updater, Refresh) {
-  int constexpr n_rows = 8, n_cols = 16;
+  int constexpr kNRows = 8, kNCols = 16;
 
   HostDeviceVector<GradientPair> gpair =
       { {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f},
         {0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f} };
-  auto dmat = CreateDMatrix(n_rows, n_cols, 0.4, 3);
+  auto dmat = CreateDMatrix(kNRows, kNCols, 0.4, 3);
   std::vector<std::pair<std::string, std::string>> cfg {
     {"reg_alpha", "0.0"},
-    {"num_feature", std::to_string(n_cols)},
+    {"num_feature", std::to_string(kNCols)},
     {"reg_lambda", "1"}};
 
   RegTree tree = RegTree();