Fix CPP lint. (#8807)

.github/workflows/main.yml

@@ -144,7 +144,18 @@ jobs:
         python -m pip install wheel setuptools cpplint pylint
     - name: Run lint
       run: |
-        python dmlc-core/scripts/lint.py xgboost cpp R-package/src
+        python3 dmlc-core/scripts/lint.py xgboost cpp R-package/src
+
+        python3 dmlc-core/scripts/lint.py --exclude_path \
+            python-package/xgboost/dmlc-core \
+            python-package/xgboost/include \
+            python-package/xgboost/lib \
+            python-package/xgboost/rabit \
+            python-package/xgboost/src \
+            --pylint-rc python-package/.pylintrc \
+            xgboost \
+            cpp \
+            include src python-package
 
   sphinx:
     runs-on: ubuntu-latest

include/xgboost/metric.h

@@ -8,15 +8,16 @@
 #define XGBOOST_METRIC_H_
 
 #include <dmlc/registry.h>
-#include <xgboost/model.h>
-#include <xgboost/data.h>
 #include <xgboost/base.h>
+#include <xgboost/data.h>
 #include <xgboost/host_device_vector.h>
+#include <xgboost/model.h>
 
-#include <vector>
-#include <string>
 #include <functional>
+#include <memory>  // shared_ptr
+#include <string>
 #include <utility>
+#include <vector>
 
 namespace xgboost {
 struct Context;

src/data/sparse_page_dmatrix.cc

@@ -19,7 +19,7 @@ const MetaInfo &SparsePageDMatrix::Info() const { return info_; }
 
 namespace detail {
 // Use device dispatch
-std::size_t NSamplesDevice(DMatrixProxy *)
+std::size_t NSamplesDevice(DMatrixProxy *)  // NOLINT
 #if defined(XGBOOST_USE_CUDA)
 ;  // NOLINT
 #else
@@ -28,7 +28,7 @@ std::size_t NSamplesDevice(DMatrixProxy *)
   return 0;
 }
 #endif
-std::size_t NFeaturesDevice(DMatrixProxy *)
+std::size_t NFeaturesDevice(DMatrixProxy *)  // NOLINT
 #if defined(XGBOOST_USE_CUDA)
 ;  // NOLINT
 #else

src/metric/auc.cu

@@ -5,7 +5,7 @@
 
 #include <algorithm>
 #include <cassert>
-#include <cub/cub.cuh>
+#include <cub/cub.cuh>  // NOLINT
 #include <limits>
 #include <memory>
 #include <tuple>

src/metric/metric_common.h

@@ -6,6 +6,7 @@
 #define XGBOOST_METRIC_METRIC_COMMON_H_
 
 #include <limits>
+#include <memory>  // shared_ptr
 #include <string>
 
 #include "../common/common.h"

src/objective/adaptive.cu

@@ -4,7 +4,7 @@
 #include <thrust/sort.h>
 
 #include <cstdint>                     // std::int32_t
-#include <cub/cub.cuh>
+#include <cub/cub.cuh>                 // NOLINT
 
 #include "../common/cuda_context.cuh"  // CUDAContext
 #include "../common/device_helpers.cuh"

src/predictor/cpu_treeshap.cc

@@ -3,6 +3,7 @@
  */
 #include "cpu_treeshap.h"
 
+#include <algorithm>             // copy
 #include <cinttypes>             // std::uint32_t
 
 #include "predict_fn.h"          // GetNextNode

src/predictor/cpu_treeshap.h

@@ -1,6 +1,10 @@
+#ifndef XGBOOST_PREDICTOR_CPU_TREESHAP_H_
+#define XGBOOST_PREDICTOR_CPU_TREESHAP_H_
 /**
  * Copyright by XGBoost Contributors 2017-2022
  */
+#include <vector>                // vector
+
 #include "xgboost/tree_model.h"  // RegTree
 
 namespace xgboost {
@@ -15,3 +19,4 @@ void CalculateContributions(RegTree const &tree, const RegTree::FVec &feat,
                             std::vector<float> *mean_values, bst_float *out_contribs, int condition,
                             unsigned condition_feature);
 }  // namespace xgboost
+#endif  // XGBOOST_PREDICTOR_CPU_TREESHAP_H_

src/tree/gpu_hist/evaluate_splits.cu

@@ -359,7 +359,7 @@ void GPUHistEvaluator::LaunchEvaluateSplits(
 
   // One block for each feature
   uint32_t constexpr kBlockThreads = 32;
-  dh::LaunchKernel{static_cast<uint32_t>(combined_num_features), kBlockThreads,
+  dh::LaunchKernel {static_cast<uint32_t>(combined_num_features), kBlockThreads,
                     0}(
       EvaluateSplitsKernel<kBlockThreads>, max_active_features, d_inputs,
       shared_inputs,

src/tree/gpu_hist/histogram.cu

@@ -299,7 +299,7 @@ void BuildGradientHistogram(CUDAContext const* ctx, EllpackDeviceAccessor const&
         min(grid_size,
             unsigned(common::DivRoundUp(items_per_group, kMinItemsPerBlock)));
 
-    dh::LaunchKernel{dim3(grid_size, num_groups), static_cast<uint32_t>(kBlockThreads), smem_size,
+    dh::LaunchKernel {dim3(grid_size, num_groups), static_cast<uint32_t>(kBlockThreads), smem_size,
                      ctx->Stream()} (kernel, matrix, feature_groups, d_ridx, histogram.data(),
                                      gpair.data(), rounding);
   };