Generalize ResultHanlder, support range search for HNSW and Fast Scan (…

…#3190)

Summary:
Pull Request resolved: #3190

This diff adds more result handlers in order to expose them externally.
This enables range search for HSNW and Fast Scan, and nprobe parameter support for FastScan.

Differential Revision: D52547384

fbshipit-source-id: 0e12da504fedfe27480722582715326418a3ff86

CHANGELOG.md

@@ -9,6 +9,7 @@ We try to indicate most contributions here with the contributor names who are no
 the Facebook Faiss team.  Feel free to add entries here if you submit a PR.
 
 ## [Unreleased]
+- Support for range search in HNSW and Fast scan IVF.
 ## [1.7.4] - 2023-04-12
 ### Added
 - Added big batch IVF search for conducting efficient search with big batches of queries

benchs/link_and_code/README.md

@@ -39,6 +39,8 @@ The code runs on top of Faiss. The HNSW index can be extended with a
 `ReconstructFromNeighbors` C++ object that refines the distances. The
 training is implemented in Python.
 
+Update: 2023-12-28: the current Faiss dropped support for reconstruction with
+this method.
 
 Reproducing Table 2 in the paper
 --------------------------------

contrib/evaluation.py

@@ -261,6 +261,7 @@ def check_ref_knn_with_draws(Dref, Iref, Dnew, Inew, rtol=1e-5):
             mask = DrefC == dis
             testcase.assertEqual(set(Iref[i, mask]), set(Inew[i, mask]))
 
+
 def check_ref_range_results(Lref, Dref, Iref,
                             Lnew, Dnew, Inew):
     """ compare range search results wrt. a reference result,

faiss/IndexAdditiveQuantizer.cpp

@@ -114,18 +114,19 @@ struct AQDistanceComputerLUT : FlatCodesDistanceComputer {
  * scanning implementation for search
  ************************************************************/
 
-template <class VectorDistance, class ResultHandler>
+template <class VectorDistance, class BlockResultHandler>
 void search_with_decompress(
         const IndexAdditiveQuantizer& ir,
         const float* xq,
         VectorDistance& vd,
-        ResultHandler& res) {
+        BlockResultHandler& res) {
     const uint8_t* codes = ir.codes.data();
     size_t ntotal = ir.ntotal;
     size_t code_size = ir.code_size;
     const AdditiveQuantizer* aq = ir.aq;
 
-    using SingleResultHandler = typename ResultHandler::SingleResultHandler;
+    using SingleResultHandler =
+            typename BlockResultHandler::SingleResultHandler;
 
 #pragma omp parallel for if (res.nq > 100)
     for (int64_t q = 0; q < res.nq; q++) {
@@ -142,19 +143,23 @@ void search_with_decompress(
     }
 }
 
-template <bool is_IP, AdditiveQuantizer::Search_type_t st, class ResultHandler>
+template <
+        bool is_IP,
+        AdditiveQuantizer::Search_type_t st,
+        class BlockResultHandler>
 void search_with_LUT(
         const IndexAdditiveQuantizer& ir,
         const float* xq,
-        ResultHandler& res) {
+        BlockResultHandler& res) {
     const AdditiveQuantizer& aq = *ir.aq;
     const uint8_t* codes = ir.codes.data();
     size_t ntotal = ir.ntotal;
     size_t code_size = aq.code_size;
     size_t nq = res.nq;
     size_t d = ir.d;
 
-    using SingleResultHandler = typename ResultHandler::SingleResultHandler;
+    using SingleResultHandler =
+            typename BlockResultHandler::SingleResultHandler;
     std::unique_ptr<float[]> LUT(new float[nq * aq.total_codebook_size]);
 
     aq.compute_LUT(nq, xq, LUT.get());
@@ -241,21 +246,23 @@ void IndexAdditiveQuantizer::search(
         if (metric_type == METRIC_L2) {
             using VD = VectorDistance<METRIC_L2>;
             VD vd = {size_t(d), metric_arg};
-            HeapResultHandler<VD::C> rh(n, distances, labels, k);
+            HeapBlockResultHandler<VD::C> rh(n, distances, labels, k);
             search_with_decompress(*this, x, vd, rh);
         } else if (metric_type == METRIC_INNER_PRODUCT) {
             using VD = VectorDistance<METRIC_INNER_PRODUCT>;
             VD vd = {size_t(d), metric_arg};
-            HeapResultHandler<VD::C> rh(n, distances, labels, k);
+            HeapBlockResultHandler<VD::C> rh(n, distances, labels, k);
             search_with_decompress(*this, x, vd, rh);
         }
     } else {
         if (metric_type == METRIC_INNER_PRODUCT) {
-            HeapResultHandler<CMin<float, idx_t>> rh(n, distances, labels, k);
+            HeapBlockResultHandler<CMin<float, idx_t>> rh(
+                    n, distances, labels, k);
             search_with_LUT<true, AdditiveQuantizer::ST_LUT_nonorm>(
                     *this, x, rh);
         } else {
-            HeapResultHandler<CMax<float, idx_t>> rh(n, distances, labels, k);
+            HeapBlockResultHandler<CMax<float, idx_t>> rh(
+                    n, distances, labels, k);
             switch (aq->search_type) {
 #define DISPATCH(st)                                                 \
     case AdditiveQuantizer::st:                                      \

faiss/IndexAdditiveQuantizerFastScan.cpp

@@ -203,9 +203,9 @@ void IndexAdditiveQuantizerFastScan::search(
 
     NormTableScaler scaler(norm_scale);
     if (metric_type == METRIC_L2) {
-        search_dispatch_implem<true>(n, x, k, distances, labels, scaler);
+        search_dispatch_implem<true>(n, x, k, distances, labels, &scaler);
     } else {
-        search_dispatch_implem<false>(n, x, k, distances, labels, scaler);
+        search_dispatch_implem<false>(n, x, k, distances, labels, &scaler);
     }
 }
 

faiss/IndexBinaryHNSW.cpp

@@ -5,8 +5,6 @@
  * LICENSE file in the root directory of this source tree.
  */
 
-// -*- c++ -*-
-
 #include <faiss/IndexBinaryHNSW.h>
 
 #include <omp.h>
@@ -28,6 +26,7 @@
 #include <faiss/impl/AuxIndexStructures.h>
 #include <faiss/impl/DistanceComputer.h>
 #include <faiss/impl/FaissAssert.h>
+#include <faiss/impl/ResultHandler.h>
 #include <faiss/utils/Heap.h>
 #include <faiss/utils/hamming.h>
 #include <faiss/utils/random.h>
@@ -201,27 +200,31 @@ void IndexBinaryHNSW::search(
             !params, "search params not supported for this index");
     FAISS_THROW_IF_NOT(k > 0);
 
+    // we use the buffer for distances as float but convert them back
+    // to int in the end
+    float* distances_f = (float*)distances;
+
+    using RH = HeapBlockResultHandler<HNSW::C>;
+    RH bres(n, distances_f, labels, k);
+
 #pragma omp parallel
     {
         VisitedTable vt(ntotal);
         std::unique_ptr<DistanceComputer> dis(get_distance_computer());
+        RH::SingleResultHandler res(bres);
 
 #pragma omp for
         for (idx_t i = 0; i < n; i++) {
-            idx_t* idxi = labels + i * k;
-            float* simi = (float*)(distances + i * k);
-
+            res.begin(i);
             dis->set_query((float*)(x + i * code_size));
-
-            maxheap_heapify(k, simi, idxi);
-            hnsw.search(*dis, k, idxi, simi, vt);
-            maxheap_reorder(k, simi, idxi);
+            hnsw.search(*dis, res, vt);
+            res.end();
         }
     }
 
 #pragma omp parallel for
     for (int i = 0; i < n * k; ++i) {
-        distances[i] = std::round(((float*)distances)[i]);
+        distances[i] = std::round(distances_f[i]);
     }
 }