CI: Add custom GitHub Actions job to run clang-tidy

.github/workflows/pr.yaml

@@ -13,6 +13,7 @@ jobs:
   pr-builder:
     needs:
       - checks
+      - clang-tidy
       - conda-cpp-build
       - conda-cpp-tests
       - conda-python-build
@@ -29,6 +30,16 @@ jobs:
     uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@cuda-120
     with:
       enable_check_generated_files: false
+  clang-tidy:
+    needs: checks
+    secrets: inherit
+    uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.08
+    with:
+      build_type: pull-request
+      node_type: "cpu8"
+      arch: "amd64"
+      container_image: "rapidsai/ci:latest"
+      run_script: "ci/run_clang_tidy.sh"
   conda-cpp-build:
     needs: checks
     secrets: inherit

CONTRIBUTING.md

@@ -115,6 +115,47 @@ please see the `.pre-commit-config.yaml` file.
   of files are up-to-date and in the correct format.
 - `codespell`: Checks for spelling mistakes
 
+### Clang-tidy
+
+In order to maintain high-quality code, cuML uses not only pre-commit hooks
+featuring various formatters and linters but also the clang-tidy tool.
+Clang-tidy is designed to detect potential issues within the C and C++ code. It
+is typically run as part of our continuous integration (CI) process.
+
+While it's generally unnecessary for contributors to run clang-tidy locally,
+there might be cases where you would want to do so. There are two primary
+methods to run clang-tidy on your local machine: using Docker or Conda.
+
+* **Docker**
+
+    1. Navigate to the repository root directory.
+    2. Run the following Docker command:
+
+        ```bash
+        docker run --rm --pull always \
+            --mount type=bind,source="$(pwd)",target=/opt/repo --workdir /opt/repo \
+            -e SCCACHE_S3_NO_CREDENTIALS=1 \
+            rapidsai/ci:latest /opt/repo/ci/run_clang_tidy.sh
+        ```
+
+
+* **Conda**
+
+    1. Navigate to the repository root directory.
+    2. Create and activate the needed conda environment:
+        ```bash
+        conda env create --force -n cuml-clang-tidy -f conda/environments/clang_tidy_cuda-118_arch-x86_64.yaml
+        conda activate cuml-clang-tidy
+        ```
+    3. Generate the compile command database with
+        ```bash
+        ./build.sh --configure-only libcuml
+        ```
+    3. Run clang-tidy with the following command:
+        ```bash
+        python cpp/scripts/run-clang-tidy.py --config pyproject.toml
+        ```
+
 ### Managing PR labels
 
 Each PR must be labeled according to whether it is a "breaking" or "non-breaking" change (using Github labels). This is used to highlight changes that users should know about when upgrading.

build.sh

@@ -19,7 +19,7 @@ ARGS=$*
 REPODIR=$(cd $(dirname $0); pwd)
 
 VALIDTARGETS="clean libcuml cuml cpp-mgtests prims bench prims-bench cppdocs pydocs"
-VALIDFLAGS="-v -g -n --allgpuarch --singlegpu --nolibcumltest --nvtx --show_depr_warn --codecov --ccache -h --help "
+VALIDFLAGS="-v -g -n --allgpuarch --singlegpu --nolibcumltest --nvtx --show_depr_warn --codecov --ccache --configure-only -h --help "
 VALIDARGS="${VALIDTARGETS} ${VALIDFLAGS}"
 HELP="$0 [<target> ...] [<flag> ...]
  where <target> is:
@@ -46,6 +46,7 @@ HELP="$0 [<target> ...] [<flag> ...]
    --codecov         - Enable code coverage support by compiling with Cython linetracing
                        and profiling enabled (WARNING: Impacts performance)
    --ccache          - Use ccache to cache previous compilations
+   --configure-only  - Invoke CMake without actually building
    --nocloneraft     - CMake will clone RAFT even if it is in the environment, use this flag to disable that behavior
    --static-treelite - Force CMake to use the Treelite static libs, cloning and building them if necessary
 
@@ -133,6 +134,7 @@ LONG_ARGUMENT_LIST=(
     "ccache"
     "nolibcumltest"
     "nocloneraft"
+    "configure-only"
 )
 
 # Short arguments
@@ -260,7 +262,7 @@ if completeBuild || hasArg libcuml || hasArg prims || hasArg bench || hasArg pri
 fi
 
 # If `./build.sh cuml` is called, don't build C/C++ components
-if completeBuild || hasArg libcuml || hasArg prims || hasArg bench || hasArg cpp-mgtests; then
+if (! hasArg --configure-only) && (completeBuild || hasArg libcuml || hasArg prims || hasArg bench || hasArg cpp-mgtests); then
     cd ${LIBCUML_BUILD_DIR}
     if [ -n "${INSTALL_TARGET}" ]; then
       cmake --build ${LIBCUML_BUILD_DIR} -j${PARALLEL_LEVEL} ${build_args} --target ${INSTALL_TARGET} ${VERBOSE_FLAG}
@@ -269,14 +271,14 @@ if completeBuild || hasArg libcuml || hasArg prims || hasArg bench || hasArg cpp
     fi
 fi
 
-if hasArg cppdocs; then
+if (! hasArg --configure-only) && hasArg cppdocs; then
     cd ${LIBCUML_BUILD_DIR}
     cmake --build ${LIBCUML_BUILD_DIR} --target docs_cuml
 fi
 
 
 # Build and (optionally) install the cuml Python package
-if completeBuild || hasArg cuml || hasArg pydocs; then
+if (! hasArg --configure-only) && (completeBuild || hasArg cuml || hasArg pydocs); then
     # Append `-DFIND_CUML_CPP=ON` to CUML_EXTRA_CMAKE_ARGS unless a user specified the option.
     SKBUILD_EXTRA_CMAKE_ARGS="${CUML_EXTRA_CMAKE_ARGS}"
     if [[ "${CUML_EXTRA_CMAKE_ARGS}" != *"DFIND_CUML_CPP"* ]]; then

ci/run_clang_tidy.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+rapids-logger "Create clang_tidy conda environment"
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key clang_tidy \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n clang_tidy
+# Temporarily allow unbound variables for conda activation.
+set +u && conda activate clang_tidy && set -u
+
+./build.sh --configure-only libcuml
+
+rapids-logger "Run clang-tidy"
+
+python cpp/scripts/run-clang-tidy.py --config pyproject.toml

conda/environments/clang_tidy_cuda-118_arch-x86_64.yaml

@@ -0,0 +1,38 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- c-compiler
+- clang-tools==15.0.7
+- clang==15.0.7
+- cmake>=3.26.4
+- cuda-version=11.8
+- cudatoolkit
+- cxx-compiler
+- gcc_linux-64=11.*
+- gmock>=1.13.0
+- gtest>=1.13.0
+- libcublas-dev=11.11.3.6
+- libcublas=11.11.3.6
+- libcufft-dev=10.9.0.58
+- libcufft=10.9.0.58
+- libcumlprims==23.8.*
+- libcurand-dev=10.3.0.86
+- libcurand=10.3.0.86
+- libcusolver-dev=11.4.1.48
+- libcusolver=11.4.1.48
+- libcusparse-dev=11.7.5.86
+- libcusparse=11.7.5.86
+- libraft-headers==23.8.*
+- libraft==23.8.*
+- librmm==23.8.*
+- ninja
+- nvcc_linux-64=11.8
+- sysroot_linux-64==2.17
+- tomli
+name: clang_tidy_cuda-118_arch-x86_64

cpp/include/cuml/experimental/fil/detail/raft_proto/buffer.hpp

@@ -94,7 +94,7 @@ struct buffer {
         }
         return result;
       }()},
-      data_{[this, input_data, mem_type]() {
+      data_{[input_data, mem_type]() {
         auto result = data_store{};
         switch (mem_type) {
           case device_type::cpu: result = non_owning_buffer<device_type::cpu, T>{input_data}; break;
@@ -134,7 +134,7 @@ struct buffer {
         }
         return result;
       }()},
-      data_{[this, &other, mem_type, device, stream]() {
+      data_{[this, &other, mem_type, stream]() {
         auto result      = data_store{};
         auto result_data = static_cast<T*>(nullptr);
         if (mem_type == device_type::cpu) {