Pre-commit formatting pass on cpp files. (#1244)

* Run pre-commit to format files. We were behind a bit. * Update pre-commit config to 16.0.1 to match cudf. Re-ran formatting. * Reformat of code via pre-commit Signed-off-by: db <[email protected]> --------- Signed-off-by: db <[email protected]>
NVIDIA · Jul 5, 2023 · 3b3ced7 · 3b3ced7
1 parent c1c9d03
commit 3b3ced7
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Showing 25 changed files with 2,825 additions and 2,207 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -2,7 +2,7 @@
 
 repos:
       - repo: https://github.com/pre-commit/mirrors-clang-format
-        rev: v14.0.6
+        rev: v16.0.1
         hooks:
               - id: clang-format
                 files: \.(cu|cuh|h|hpp|cpp|inl)$

diff --git a/src/main/cpp/benchmarks/cast_string_to_float.cpp b/src/main/cpp/benchmarks/cast_string_to_float.cpp
@@ -27,16 +27,18 @@
 void string_to_float(nvbench::state& state)
 {
   cudf::size_type const n_rows{(cudf::size_type)state.get_int64("num_rows")};
-  auto const float_tbl = create_random_table({cudf::type_id::FLOAT32}, row_count{n_rows});
-  auto const float_col = float_tbl->get_column(0);
+  auto const float_tbl  = create_random_table({cudf::type_id::FLOAT32}, row_count{n_rows});
+  auto const float_col  = float_tbl->get_column(0);
   auto const string_col = cudf::strings::from_floats(float_col.view());
 
-  state.exec(nvbench::exec_tag::sync,
-  [&](nvbench::launch& launch) {
-      auto rows = spark_rapids_jni::string_to_float(cudf::data_type{cudf::type_id::FLOAT32}, string_col->view(), false, cudf::get_default_stream());
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto rows = spark_rapids_jni::string_to_float(cudf::data_type{cudf::type_id::FLOAT32},
+                                                  string_col->view(),
+                                                  false,
+                                                  cudf::get_default_stream());
   });
 }
 
 NVBENCH_BENCH(string_to_float)
-    .set_name("Strings to Float Cast")
-    .add_int64_axis("num_rows", {1 * 1024 * 1024, 100 * 1024 * 1024});
+  .set_name("Strings to Float Cast")
+  .add_int64_axis("num_rows", {1 * 1024 * 1024, 100 * 1024 * 1024});
diff --git a/src/main/cpp/benchmarks/common/generate_input.hpp b/src/main/cpp/benchmarks/common/generate_input.hpp
@@ -183,8 +183,7 @@ struct distribution_params<T, std::enable_if_t<std::is_same_v<T, cudf::struct_vi
 
 // Present for compilation only. To be implemented once reader/writers support the fixed width type.
 template <typename T>
-struct distribution_params<T, std::enable_if_t<cudf::is_fixed_point<T>()>> {
-};
+struct distribution_params<T, std::enable_if_t<cudf::is_fixed_point<T>()>> {};
 
 /**
  * @brief Returns a vector of types, corresponding to the input type or a type group.

diff --git a/src/main/cpp/benchmarks/row_conversion.cpp b/src/main/cpp/benchmarks/row_conversion.cpp
@@ -28,15 +28,15 @@ void fixed_width(nvbench::state& state)
 {
   cudf::size_type const n_rows{(cudf::size_type)state.get_int64("num_rows")};
   auto const direction = state.get_string("direction");
-  auto const table = create_random_table(cycle_dtypes({cudf::type_id::INT8,
-                                                       cudf::type_id::INT32,
-                                                       cudf::type_id::INT16,
-                                                       cudf::type_id::INT64,
-                                                       cudf::type_id::INT32,
-                                                       cudf::type_id::BOOL8,
-                                                       cudf::type_id::UINT16,
-                                                       cudf::type_id::UINT8,
-                                                       cudf::type_id::UINT64},
+  auto const table     = create_random_table(cycle_dtypes({cudf::type_id::INT8,
+                                                           cudf::type_id::INT32,
+                                                           cudf::type_id::INT16,
+                                                           cudf::type_id::INT64,
+                                                           cudf::type_id::INT32,
+                                                           cudf::type_id::BOOL8,
+                                                           cudf::type_id::UINT16,
+                                                           cudf::type_id::UINT8,
+                                                           cudf::type_id::UINT64},
                                                       212),
                                          row_count{n_rows});
 
@@ -50,16 +50,15 @@ void fixed_width(nvbench::state& state)
 
   auto rows = spark_rapids_jni::convert_to_rows_fixed_width_optimized(table->view());
 
-  state.exec(nvbench::exec_tag::sync,
-  [&](nvbench::launch& launch) {
-      if (direction == "to row") {
-        auto _rows = spark_rapids_jni::convert_to_rows_fixed_width_optimized(table->view());
-      } else {
-        for (auto const &r : rows) {
-          cudf::lists_column_view const l(r->view());
-          auto out = spark_rapids_jni::convert_from_rows_fixed_width_optimized(l, schema);
-        }
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    if (direction == "to row") {
+      auto _rows = spark_rapids_jni::convert_to_rows_fixed_width_optimized(table->view());
+    } else {
+      for (auto const& r : rows) {
+        cudf::lists_column_view const l(r->view());
+        auto out = spark_rapids_jni::convert_from_rows_fixed_width_optimized(l, schema);
       }
+    }
   });
 
   state.add_buffer_size(n_rows, "trc", "Total Rows");
@@ -69,7 +68,7 @@ void fixed_width(nvbench::state& state)
 static void variable_or_fixed_width(nvbench::state& state)
 {
   cudf::size_type const n_rows{(cudf::size_type)state.get_int64("num_rows")};
-  auto const direction = state.get_string("direction");
+  auto const direction       = state.get_string("direction");
   auto const include_strings = state.get_string("strings");
 
   if (n_rows > 1 * 1024 * 1024 && include_strings == "include strings") {
@@ -120,30 +119,29 @@ static void variable_or_fixed_width(nvbench::state& state)
 
   auto rows = spark_rapids_jni::convert_to_rows(table->view());
 
-  state.exec(nvbench::exec_tag::sync,
-  [&](nvbench::launch& launch) {
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
     auto new_rows = spark_rapids_jni::convert_to_rows(table->view());
-          if (direction == "to row") {
-        auto _rows = spark_rapids_jni::convert_to_rows(table->view());
-      } else {
-        for (auto const &r : rows) {
-          cudf::lists_column_view const l(r->view());
-          auto out = spark_rapids_jni::convert_from_rows(l, schema);
-        }
+    if (direction == "to row") {
+      auto _rows = spark_rapids_jni::convert_to_rows(table->view());
+    } else {
+      for (auto const& r : rows) {
+        cudf::lists_column_view const l(r->view());
+        auto out = spark_rapids_jni::convert_from_rows(l, schema);
       }
+    }
   });
 
   state.add_buffer_size(n_rows, "trc", "Total Rows");
   state.add_global_memory_reads<int64_t>(bytes_per_row * table->num_rows());
 }
 
 NVBENCH_BENCH(fixed_width)
-    .set_name("Fixed Width Only")
-    .add_int64_axis("num_rows", {1 * 1024 * 1024, 4 * 1024 * 1024})
-    .add_string_axis("direction", {"to row", "from row"});
+  .set_name("Fixed Width Only")
+  .add_int64_axis("num_rows", {1 * 1024 * 1024, 4 * 1024 * 1024})
+  .add_string_axis("direction", {"to row", "from row"});
 
 NVBENCH_BENCH(variable_or_fixed_width)
-    .set_name("Fixed or Variable Width")
-    .add_int64_axis("num_rows", {1 * 1024 * 1024, 4 * 1024 * 1024})
-    .add_string_axis("direction", {"to row", "from row"})
-    .add_string_axis("strings", {"include strings", "no strings"});
+  .set_name("Fixed or Variable Width")
+  .add_int64_axis("num_rows", {1 * 1024 * 1024, 4 * 1024 * 1024})
+  .add_string_axis("direction", {"to row", "from row"})
+  .add_string_axis("strings", {"include strings", "no strings"});
diff --git a/src/main/cpp/src/DecimalUtilsJni.cpp b/src/main/cpp/src/DecimalUtilsJni.cpp
@@ -14,98 +14,95 @@
  * limitations under the License.
  */
 
-#include "decimal_utils.hpp"
 #include "cudf_jni_apis.hpp"
+#include "decimal_utils.hpp"
 
 extern "C" {
 
-JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_multiply128(JNIEnv *env, jclass,
-                                                                                       jlong j_view_a,
-                                                                                       jlong j_view_b,
-                                                                                       jint j_product_scale) {
+JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_multiply128(
+  JNIEnv* env, jclass, jlong j_view_a, jlong j_view_b, jint j_product_scale)
+{
   JNI_NULL_CHECK(env, j_view_a, "column is null", 0);
   JNI_NULL_CHECK(env, j_view_b, "column is null", 0);
   try {
     cudf::jni::auto_set_device(env);
-    auto view_a = reinterpret_cast<cudf::column_view const *>(j_view_a);
-    auto view_b = reinterpret_cast<cudf::column_view const *>(j_view_b);
-    auto scale = static_cast<int>(j_product_scale);
-    return cudf::jni::convert_table_for_return(env, cudf::jni::multiply_decimal128(*view_a, *view_b,
-                                                                                   scale));
+    auto view_a = reinterpret_cast<cudf::column_view const*>(j_view_a);
+    auto view_b = reinterpret_cast<cudf::column_view const*>(j_view_b);
+    auto scale  = static_cast<int>(j_product_scale);
+    return cudf::jni::convert_table_for_return(
+      env, cudf::jni::multiply_decimal128(*view_a, *view_b, scale));
   }
   CATCH_STD(env, 0);
 }
 
-JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_divide128(JNIEnv *env, jclass,
-                                                                                     jlong j_view_a,
-                                                                                     jlong j_view_b,
-                                                                                     jint j_quotient_scale,
-                                                                                     jboolean j_is_int_div) {
+JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_divide128(
+  JNIEnv* env, jclass, jlong j_view_a, jlong j_view_b, jint j_quotient_scale, jboolean j_is_int_div)
+{
   JNI_NULL_CHECK(env, j_view_a, "column is null", 0);
   JNI_NULL_CHECK(env, j_view_b, "column is null", 0);
   try {
     cudf::jni::auto_set_device(env);
-    auto view_a = reinterpret_cast<cudf::column_view const *>(j_view_a);
-    auto view_b = reinterpret_cast<cudf::column_view const *>(j_view_b);
-    auto scale = static_cast<int>(j_quotient_scale);
+    auto view_a          = reinterpret_cast<cudf::column_view const*>(j_view_a);
+    auto view_b          = reinterpret_cast<cudf::column_view const*>(j_view_b);
+    auto scale           = static_cast<int>(j_quotient_scale);
     auto is_int_division = static_cast<bool>(j_is_int_div);
     if (is_int_division) {
-      return cudf::jni::convert_table_for_return(env, cudf::jni::integer_divide_decimal128(*view_a, *view_b, scale));
+      return cudf::jni::convert_table_for_return(
+        env, cudf::jni::integer_divide_decimal128(*view_a, *view_b, scale));
     } else {
-      return cudf::jni::convert_table_for_return(env, cudf::jni::divide_decimal128(*view_a, *view_b, scale));
+      return cudf::jni::convert_table_for_return(
+        env, cudf::jni::divide_decimal128(*view_a, *view_b, scale));
     }
   }
   CATCH_STD(env, 0);
 }
 
-JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_remainder128(JNIEnv *env, jclass,
-                                                                                     jlong j_view_a,
-                                                                                     jlong j_view_b,
-                                                                                     jint j_remainder_scale) {
+JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_remainder128(
+  JNIEnv* env, jclass, jlong j_view_a, jlong j_view_b, jint j_remainder_scale)
+{
   JNI_NULL_CHECK(env, j_view_a, "column is null", 0);
   JNI_NULL_CHECK(env, j_view_b, "column is null", 0);
   try {
     cudf::jni::auto_set_device(env);
-    auto view_a = reinterpret_cast<cudf::column_view const *>(j_view_a);
-    auto view_b = reinterpret_cast<cudf::column_view const *>(j_view_b);
-    auto scale = static_cast<int>(j_remainder_scale);
-    return cudf::jni::convert_table_for_return(env, cudf::jni::remainder_decimal128(*view_a, *view_b, scale));
+    auto view_a = reinterpret_cast<cudf::column_view const*>(j_view_a);
+    auto view_b = reinterpret_cast<cudf::column_view const*>(j_view_b);
+    auto scale  = static_cast<int>(j_remainder_scale);
+    return cudf::jni::convert_table_for_return(
+      env, cudf::jni::remainder_decimal128(*view_a, *view_b, scale));
   }
   CATCH_STD(env, 0);
 }
 
-JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_add128(JNIEnv *env, jclass,
-                                                                                     jlong j_view_a,
-                                                                                     jlong j_view_b,
-                                                                                     jint j_target_scale) {
+JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_add128(
+  JNIEnv* env, jclass, jlong j_view_a, jlong j_view_b, jint j_target_scale)
+{
   JNI_NULL_CHECK(env, j_view_a, "column is null", 0);
   JNI_NULL_CHECK(env, j_view_b, "column is null", 0);
   try {
     cudf::jni::auto_set_device(env);
-    auto const view_a= reinterpret_cast<cudf::column_view const *>(j_view_a);
-    auto const view_b= reinterpret_cast<cudf::column_view const *>(j_view_b);
-    auto const scale = static_cast<int>(j_target_scale);
-    return cudf::jni::convert_table_for_return(env, cudf::jni::add_decimal128(*view_a, *view_b,
-                                                                                 scale));
+    auto const view_a = reinterpret_cast<cudf::column_view const*>(j_view_a);
+    auto const view_b = reinterpret_cast<cudf::column_view const*>(j_view_b);
+    auto const scale  = static_cast<int>(j_target_scale);
+    return cudf::jni::convert_table_for_return(env,
+                                               cudf::jni::add_decimal128(*view_a, *view_b, scale));
   }
   CATCH_STD(env, 0);
 }
 
-JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_subtract128(JNIEnv *env, jclass,
-                                                                                     jlong j_view_a,
-                                                                                     jlong j_view_b,
-                                                                                     jint j_target_scale) {
+JNIEXPORT jlongArray JNICALL Java_com_nvidia_spark_rapids_jni_DecimalUtils_subtract128(
+  JNIEnv* env, jclass, jlong j_view_a, jlong j_view_b, jint j_target_scale)
+{
   JNI_NULL_CHECK(env, j_view_a, "column is null", 0);
   JNI_NULL_CHECK(env, j_view_b, "column is null", 0);
   try {
     cudf::jni::auto_set_device(env);
-    auto const view_a = reinterpret_cast<cudf::column_view const *>(j_view_a);
-    auto const view_b = reinterpret_cast<cudf::column_view const *>(j_view_b);
-    auto const scale = static_cast<int>(j_target_scale);
-    return cudf::jni::convert_table_for_return(env, cudf::jni::sub_decimal128(*view_a, *view_b,
-                                                                                 scale));
+    auto const view_a = reinterpret_cast<cudf::column_view const*>(j_view_a);
+    auto const view_b = reinterpret_cast<cudf::column_view const*>(j_view_b);
+    auto const scale  = static_cast<int>(j_target_scale);
+    return cudf::jni::convert_table_for_return(env,
+                                               cudf::jni::sub_decimal128(*view_a, *view_b, scale));
   }
   CATCH_STD(env, 0);
 }
 
-} // extern "C"
+}  // extern "C"
diff --git a/src/main/cpp/src/MapUtilsJni.cpp b/src/main/cpp/src/MapUtilsJni.cpp
@@ -22,12 +22,13 @@
 extern "C" {
 
 JNIEXPORT jlong JNICALL Java_com_nvidia_spark_rapids_jni_MapUtils_extractRawMapFromJsonString(
-    JNIEnv *env, jclass, jlong input_handle) {
+  JNIEnv* env, jclass, jlong input_handle)
+{
   JNI_NULL_CHECK(env, input_handle, "json_column_handle is null", 0);
 
   try {
     cudf::jni::auto_set_device(env);
-    auto const input = reinterpret_cast<cudf::column_view const *>(input_handle);
+    auto const input = reinterpret_cast<cudf::column_view const*>(input_handle);
     return cudf::jni::ptr_as_jlong(spark_rapids_jni::from_json(*input).release());
   }
   CATCH_STD(env, 0);