Add support for refinement with uint32_t index type

cpp/include/cuvs/neighbors/refine.hpp

@@ -76,6 +76,51 @@ void refine(raft::resources const& handle,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
 
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<const uint32_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> indices,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
 /**
  * @brief Refine nearest neighbor search.
  *

cpp/src/neighbors/ivf_flat_index.cpp

@@ -226,6 +226,7 @@ void index<T, IdxT>::check_consistency()
     "inconsistent number of lists (clusters)");
 }
 
+template struct index<float, uint32_t>;  // Used for refine function
 template struct index<float, int64_t>;
 template struct index<half, int64_t>;
 template struct index<int8_t, int64_t>;

cpp/src/neighbors/refine/detail/refine_device_float_float.cu

@@ -43,5 +43,6 @@
   }
 
 instantiate_cuvs_neighbors_refine_d(int64_t, float, float, int64_t);
+instantiate_cuvs_neighbors_refine_d(uint32_t, float, float, int64_t);
 
 #undef instantiate_cuvs_neighbors_refine_d

cpp/src/neighbors/refine/refine_device.cuh

@@ -84,12 +84,13 @@ void refine_device(
   cuvs::neighbors::ivf_flat::index<data_t, idx_t> refinement_index(
     handle, cuvs::distance::DistanceType(metric), n_queries, false, true, dim);
 
-  cuvs::neighbors::ivf_flat::detail::fill_refinement_index(handle,
-                                                           &refinement_index,
-                                                           dataset.data_handle(),
-                                                           neighbor_candidates.data_handle(),
-                                                           n_queries,
-                                                           n_candidates);
+  cuvs::neighbors::ivf_flat::detail::fill_refinement_index<data_t, idx_t>(
+    handle,
+    &refinement_index,
+    dataset.data_handle(),
+    neighbor_candidates.data_handle(),
+    (idx_t)n_queries,
+    (uint32_t)n_candidates);
   uint32_t grid_dim_x = 1;
 
   // the neighbor ids will be computed in uint32_t as offset