generous_furthest optimization

make generosity depend on

1. M. Keep small M's fast, increase generosity for larger M's to get
   better recall.

2. distance. Keep generosity small when vectors are far from the
   target, increase generosity when the search gets closer. This
   allows to examine more relevant vectors but doesn't waste time
   examining irrelevant vectors. Particularly important with cosine
   metric when the distance is bounded

Author: vuvova

sql/vector_mhnsw.cc

@@ -26,7 +26,6 @@
 
 // Algorithm parameters
 static constexpr float alpha = 1.1f;
-static constexpr float generosity = 1.1f;
 static constexpr uint ef_construction= 10;
 
 static ulonglong mhnsw_cache_size;
@@ -334,6 +333,7 @@ class MHNSW_Context : public Sql_alloc
   size_t vec_len= 0;
   size_t byte_len= 0;
   Atomic_relaxed<double> ef_power{0.6}; // for the bloom filter size heuristic
+  Atomic_relaxed<float>  diameter{0};   // for the generosity heuristic
   FVectorNode *start= 0;
   const uint tref_len;
   const uint gref_len;
@@ -957,6 +957,17 @@ static int update_second_degree_neighbors(MHNSW_Context *ctx, TABLE *graph,
   return 0;
 }
 
+
+static inline float generous_furthest(const Queue<Visited> &q, float maxd, float g)
+{
+  float d0=maxd*g/2;
+  float d= q.top()->distance_to_target;
+  float k= 5;
+  float x= (d-d0)/d0;
+  float sigmoid= k*x/std::sqrt(1+(k*k-1)*x*x); // or any other sigmoid
+  return d*(1 + (g - 1)/2 * (1 - sigmoid));
+}
+
 static int search_layer(MHNSW_Context *ctx, TABLE *graph, const FVector *target,
                         Neighborhood *start_nodes, uint result_size,
                         size_t layer, Neighborhood *result, bool construction)
@@ -968,6 +979,7 @@ static int search_layer(MHNSW_Context *ctx, TABLE *graph, const FVector *target,
   Queue<Visited> candidates, best;
   bool skip_deleted;
   uint ef= result_size;
+  float generosity= 1.1f + ctx->M/500.0f;
 
   if (construction)
   {
@@ -991,17 +1003,19 @@ static int search_layer(MHNSW_Context *ctx, TABLE *graph, const FVector *target,
   best.init(ef, true, Visited::cmp);
 
   DBUG_ASSERT(start_nodes->num <= result_size);
+  float max_distance= ctx->diameter;
   for (size_t i=0; i < start_nodes->num; i++)
   {
     Visited *v= visited.create(start_nodes->links[i]);
+    max_distance= std::max(max_distance, v->distance_to_target);
     candidates.push(v);
     if (skip_deleted && v->node->deleted)
       continue;
     best.push(v);
   }
 
   float furthest_best= best.is_empty() ? FLT_MAX
-                       : best.top()->distance_to_target * generosity;
+                       : generous_furthest(best, max_distance, generosity);
   while (candidates.elements())
   {
     const Visited &cur= *candidates.pop();
@@ -1027,11 +1041,12 @@ static int search_layer(MHNSW_Context *ctx, TABLE *graph, const FVector *target,
         Visited *v= visited.create(links[i]);
         if (!best.is_full())
         {
+          max_distance= std::max(max_distance, v->distance_to_target);
           candidates.push(v);
           if (skip_deleted && v->node->deleted)
             continue;
           best.push(v);
-          furthest_best= best.top()->distance_to_target * generosity;
+          furthest_best= generous_furthest(best, max_distance, generosity);
         }
         else if (v->distance_to_target < furthest_best)
         {
@@ -1041,12 +1056,13 @@ static int search_layer(MHNSW_Context *ctx, TABLE *graph, const FVector *target,
           if (v->distance_to_target < best.top()->distance_to_target)
           {
             best.replace_top(v);
-            furthest_best= best.top()->distance_to_target * generosity;
+            furthest_best= generous_furthest(best, max_distance, generosity);
           }
         }
       }
     }
   }
+  set_if_bigger(ctx->diameter, max_distance); // not atomic, but it's ok
   if (ef > 1 && visited.count*2 > est_size)
   {
     double ef_power= std::log(visited.count*2/est_heuristic) / std::log(ef);