Add search parameters for IndexRefine::search() and IndexRefineFlat::search()

faiss/IndexRefine.cpp

@@ -96,12 +96,24 @@ void IndexRefine::search(
         idx_t k,
         float* distances,
         idx_t* labels,
-        const SearchParameters* params) const {
-    FAISS_THROW_IF_NOT_MSG(
-            !params, "search params not supported for this index");
+        const SearchParameters* params_in) const {
+    const IndexRefineSearchParameters* params = nullptr;
+    if (params_in) {
+        params = dynamic_cast<const IndexRefineSearchParameters*>(params_in);
+        FAISS_THROW_IF_NOT_MSG(params, "IndexRefine params have incorrect type");
+    }
+
+    idx_t k_base = (params != nullptr) ? idx_t(k * params->k_factor) : idx_t(k * k_factor);
+    SearchParameters* base_index_params = 
+        (params != nullptr) ? params->base_index_params : nullptr;
+
+    FAISS_THROW_IF_NOT(k_base >= k);
+
+    FAISS_THROW_IF_NOT(base_index);
+    FAISS_THROW_IF_NOT(refine_index);
+
     FAISS_THROW_IF_NOT(k > 0);
     FAISS_THROW_IF_NOT(is_trained);
-    idx_t k_base = idx_t(k * k_factor);
     idx_t* base_labels = labels;
     float* base_distances = distances;
     ScopeDeleter<idx_t> del1;
@@ -114,7 +126,7 @@ void IndexRefine::search(
         del2.set(base_distances);
     }
 
-    base_index->search(n, x, k_base, base_distances, base_labels);
+    base_index->search(n, x, k_base, base_distances, base_labels, base_index_params);
 
     for (int i = 0; i < n * k_base; i++)
         assert(base_labels[i] >= -1 && base_labels[i] < ntotal);
@@ -225,12 +237,24 @@ void IndexRefineFlat::search(
         idx_t k,
         float* distances,
         idx_t* labels,
-        const SearchParameters* params) const {
-    FAISS_THROW_IF_NOT_MSG(
-            !params, "search params not supported for this index");
+        const SearchParameters* params_in) const {
+    const IndexRefineSearchParameters* params = nullptr;
+    if (params_in) {
+        params = dynamic_cast<const IndexRefineSearchParameters*>(params_in);
+        FAISS_THROW_IF_NOT_MSG(params, "IndexRefineFlat params have incorrect type");
+    }
+
+    idx_t k_base = (params != nullptr) ? idx_t(k * params->k_factor) : idx_t(k * k_factor);
+    SearchParameters* base_index_params = 
+        (params != nullptr) ? params->base_index_params : nullptr;
+
+    FAISS_THROW_IF_NOT(k_base >= k);
+
+    FAISS_THROW_IF_NOT(base_index);
+    FAISS_THROW_IF_NOT(refine_index);
+
     FAISS_THROW_IF_NOT(k > 0);
     FAISS_THROW_IF_NOT(is_trained);
-    idx_t k_base = idx_t(k * k_factor);
     idx_t* base_labels = labels;
     float* base_distances = distances;
     ScopeDeleter<idx_t> del1;
@@ -243,7 +267,7 @@ void IndexRefineFlat::search(
         del2.set(base_distances);
     }
 
-    base_index->search(n, x, k_base, base_distances, base_labels);
+    base_index->search(n, x, k_base, base_distances, base_labels, base_index_params);
 
     for (int i = 0; i < n * k_base; i++)
         assert(base_labels[i] >= -1 && base_labels[i] < ntotal);

faiss/IndexRefine.h

@@ -11,6 +11,13 @@
 
 namespace faiss {
 
+struct IndexRefineSearchParameters : SearchParameters {
+    float k_factor = 1;
+    SearchParameters* base_index_params = nullptr;  // non-owning
+
+    virtual ~IndexRefineSearchParameters() = default;
+};
+
 /** Index that queries in a base_index (a fast one) and refines the
  *  results with an exact search, hopefully improving the results.
  */

tests/test_refine.py

@@ -64,3 +64,58 @@ def test_distance_computer_AQ_LUT(self):
 
     def test_distance_computer_AQ_LUT_IP(self):
         self.do_test("RQ3x4_Nqint8", faiss.METRIC_INNER_PRODUCT)
+
+
+class TestIndexRefineSearchParams(unittest.TestCase):
+
+    def do_test(self, factory_string):
+        ds = datasets.SyntheticDataset(32, 256, 100, 40)
+
+        index = faiss.index_factory(32, factory_string)
+        index.train(ds.get_train())
+        index.add(ds.get_database())
+        index.nprobe = 4
+        xq = ds.get_queries()
+
+        # do a search with k_factor = 1
+        D1, I1 = index.search(xq, 10)
+        inter1 = faiss.eval_intersection(I1, ds.get_groundtruth(10))
+
+        # do a search with k_factor = 1.5
+        params = faiss.IndexRefineSearchParameters(k_factor=1.1)
+        D2, I2 = index.search(xq, 10, params=params)
+        inter2 = faiss.eval_intersection(I2, ds.get_groundtruth(10))
+
+        # do a search with k_factor = 2
+        params = faiss.IndexRefineSearchParameters(k_factor=2)
+        D3, I3 = index.search(xq, 10, params=params)
+        inter3 = faiss.eval_intersection(I3, ds.get_groundtruth(10))
+
+        # make sure that the recall rate increases with k_factor
+        self.assertGreater(inter2, inter1)
+        self.assertGreater(inter3, inter2)
+
+        # make sure that the baseline k_factor is unchanged
+        self.assertEqual(index.k_factor, 1)
+
+        # try passing params for the baseline index, change nprobe
+        base_params = faiss.IVFSearchParameters(nprobe=10)
+        params = faiss.IndexRefineSearchParameters(k_factor=1, base_index_params=base_params)
+        D4, I4 = index.search(xq, 10, params=params)
+        inter4 = faiss.eval_intersection(I4, ds.get_groundtruth(10))
+
+        base_params = faiss.IVFSearchParameters(nprobe=2)
+        params = faiss.IndexRefineSearchParameters(k_factor=1, base_index_params=base_params)
+        D5, I5 = index.search(xq, 10, params=params)
+        inter5 = faiss.eval_intersection(I5, ds.get_groundtruth(10))
+
+        # make sure that the recall rate changes
+        self.assertNotEqual(inter4, inter5)
+
+    def test_rflat(self):
+        # flat is handled by the IndexRefineFlat class
+        self.do_test("IVF8,PQ2x4np,RFlat")
+
+    def test_refine_sq8(self):
+        # this case uses the IndexRefine class
+        self.do_test("IVF8,PQ2x4np,Refine(SQ8)")