Satisfy pre-commit

rapidsai · Dec 14, 2023 · c9bfc9a · c9bfc9a
1 parent 080b221
commit c9bfc9a
Showing 1 changed file with 12 additions and 12 deletions.
diff --git a/cpp/include/raft/neighbors/detail/refine_host-inl.hpp b/cpp/include/raft/neighbors/detail/refine_host-inl.hpp
@@ -47,14 +47,14 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
   // If the number of queries is small, separete the distance calculation and
   // the top-k calculation into separete loops, and apply finer-grained thread
   // parallelism to the distance calculation loop.
-  if ( n_queries < size_t(suggested_n_threads) ) {
-    std::vector<std::vector<std::tuple<DistanceT, IdxT>>>
-      refined_pairs(n_queries, std::vector<std::tuple<DistanceT, IdxT>>(orig_k));
+  if (n_queries < size_t(suggested_n_threads)) {
+    std::vector<std::vector<std::tuple<DistanceT, IdxT>>> refined_pairs(
+      n_queries, std::vector<std::tuple<DistanceT, IdxT>>(orig_k));
 
     // For efficiency, each thread should read a certain amount of array
     // elements. The number of threads for distance computation is determined
     // taking this into account.
-    auto n_elements = std::max(size_t(512), dim);
+    auto n_elements    = std::max(size_t(512), dim);
     auto max_n_threads = raft::div_rounding_up_safe<size_t>(n_queries * orig_k * dim, n_elements);
     auto suggested_n_threads_for_distance = std::min(size_t(suggested_n_threads), max_n_threads);
 
@@ -65,9 +65,9 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
 #pragma omp parallel for collapse(2) num_threads(suggested_n_threads_for_distance)
     for (size_t i = 0; i < n_queries; i++) {
       for (size_t j = 0; j < orig_k; j++) {
-	const DataT* query = queries.data_handle() + dim * i;
-	IdxT id            = neighbor_candidates(i, j);
-	DistanceT distance = 0.0;
+        const DataT* query = queries.data_handle() + dim * i;
+        IdxT id            = neighbor_candidates(i, j);
+        DistanceT distance = 0.0;
         if (static_cast<size_t>(id) >= n_rows) {
           distance = std::numeric_limits<DistanceT>::max();
         } else {
@@ -76,7 +76,7 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
             distance += DC::template eval<DistanceT>(query[k], row[k]);
           }
         }
-	refined_pairs[i][j] = std::make_tuple(distance, id);
+        refined_pairs[i][j] = std::make_tuple(distance, id);
       }
     }
 
@@ -86,10 +86,10 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
       std::sort(refined_pairs[i].begin(), refined_pairs[i].end());
       // Store first refined_k neighbors
       for (size_t j = 0; j < refined_k; j++) {
-	indices(i, j) = std::get<1>(refined_pairs[i][j]);
-	if (distances.data_handle() != nullptr) {
-	  distances(i, j) = DC::template postprocess(std::get<0>(refined_pairs[i][j]));
-	}
+        indices(i, j) = std::get<1>(refined_pairs[i][j]);
+        if (distances.data_handle() != nullptr) {
+          distances(i, j) = DC::template postprocess(std::get<0>(refined_pairs[i][j]));
+        }
       }
     }
     return;