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Revert pruning PRs #331

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b78f3dd
Revert "Fix duplicate-cluster trigger to require >=2 kept candidates …
ethanglaser May 13, 2026
93b6f5e
Revert "Enhance heuristic pruning to handle duplicate clusters (#282)"
ethanglaser May 13, 2026
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102 changes: 0 additions & 102 deletions include/svs/index/vamana/prune.h
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Original file line number Diff line number Diff line change
Expand Up @@ -130,9 +130,6 @@ void heuristic_prune_neighbors(

auto pruned = std::vector<PruneState>(poolsize, PruneState::Available);
float current_alpha = 1.0f;
float anchor_dist = 0.0f;
bool anchor_set = false;
bool all_duplicates = true;
while (result.size() < max_result_size && !cmp(alpha, current_alpha)) {
size_t start = 0;
while (result.size() < max_result_size && start < poolsize) {
Expand All @@ -148,16 +145,6 @@ void heuristic_prune_neighbors(
const auto& query = accessor(dataset, id);
distance::maybe_fix_argument(distance_function, query);
result.push_back(detail::construct_as(lib::Type<I>(), pool[start]));

if (all_duplicates) {
if (!anchor_set) {
anchor_dist = pool[start].distance();
anchor_set = true;
} else if (pool[start].distance() != anchor_dist) {
all_duplicates = false;
}
}

for (size_t t = start + 1; t < poolsize; ++t) {
if (excluded(pruned[t])) {
continue;
Expand All @@ -184,44 +171,6 @@ void heuristic_prune_neighbors(
}
current_alpha *= alpha;
}

// Add a diversity edge if a duplicate cluster is detected.
// A "cluster" requires at least 2 kept candidates sharing the same
// distance; a single retained neighbor is not a cluster and must not
// be replaced (doing so would discard the only true nearest-neighbor
// edge for that node).
if (all_duplicates && anchor_set && result.size() >= 2) {
auto result_id = [](const I& r) -> size_t {
if constexpr (std::integral<I>) {
return static_cast<size_t>(r);
} else {
return static_cast<size_t>(r.id());
}
};
for (size_t t = 0; t < poolsize; ++t) {
const auto& candidate = pool[t];
auto cid = candidate.id();
if (cid == current_node_id || candidate.distance() == anchor_dist) {
continue;
}
bool in_result = false;
for (const auto& r : result) {
if (result_id(r) == static_cast<size_t>(cid)) {
in_result = true;
break;
}
}
assert(
!in_result &&
"Candidate with non-anchor distance should not already be in result"
);
if (in_result) {
continue;
}
result.back() = detail::construct_as(lib::Type<I>(), candidate);
break;
}
}
}

template <
Expand Down Expand Up @@ -254,9 +203,6 @@ void heuristic_prune_neighbors(
std::vector<float> pruned(poolsize, type_traits::tombstone_v<float, decltype(cmp)>);

float current_alpha = 1.0f;
float anchor_dist = 0.0f;
bool anchor_set = false;
bool all_duplicates = true;
while (result.size() < max_result_size && !cmp(alpha, current_alpha)) {
size_t start = 0;
while (result.size() < max_result_size && start < poolsize) {
Expand All @@ -272,16 +218,6 @@ void heuristic_prune_neighbors(
const auto& query = accessor(dataset, id);
distance::maybe_fix_argument(distance_function, query);
result.push_back(detail::construct_as(lib::Type<I>(), pool[start]));

if (all_duplicates) {
if (!anchor_set) {
anchor_dist = pool[start].distance();
anchor_set = true;
} else if (pool[start].distance() != anchor_dist) {
all_duplicates = false;
}
}

for (size_t t = start + 1; t < poolsize; ++t) {
if (cmp(current_alpha, pruned[t])) {
continue;
Expand All @@ -300,44 +236,6 @@ void heuristic_prune_neighbors(
}
current_alpha *= alpha;
}

// Add a diversity edge if a duplicate cluster is detected.
// A "cluster" requires at least 2 kept candidates sharing the same
// distance; a single retained neighbor is not a cluster and must not
// be replaced (doing so would discard the only true nearest-neighbor
// edge for that node).
if (all_duplicates && anchor_set && result.size() >= 2) {
auto result_id = [](const I& r) -> size_t {
if constexpr (std::integral<I>) {
return static_cast<size_t>(r);
} else {
return static_cast<size_t>(r.id());
}
};
for (size_t t = 0; t < poolsize; ++t) {
const auto& candidate = pool[t];
auto cid = candidate.id();
if (cid == current_node_id || candidate.distance() == anchor_dist) {
continue;
}
bool in_result = false;
for (const auto& r : result) {
if (result_id(r) == static_cast<size_t>(cid)) {
in_result = true;
break;
}
}
assert(
!in_result &&
"Candidate with non-anchor distance should not already be in result"
);
if (in_result) {
continue;
}
result.back() = detail::construct_as(lib::Type<I>(), candidate);
break;
}
}
}

///
Expand Down
65 changes: 0 additions & 65 deletions tests/svs/index/vamana/prune.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -17,10 +17,6 @@
// header under test
#include "svs/index/vamana/prune.h"

// core
#include "svs/core/data/simple.h"
#include "svs/core/distance/euclidean.h"

// catch2
#include "catch2/catch_test_macros.hpp"

Expand Down Expand Up @@ -50,65 +46,4 @@ CATCH_TEST_CASE("Pruning", "[index][vamana]") {
CATCH_REQUIRE(v::excluded(v::PruneState::Pruned) == true);
}
}

CATCH_SECTION("Duplicate Cluster Trap") {
auto data = svs::data::SimpleData<float>(6, 4);
auto d0 = std::vector<float>{1.0f, 1.0f, 1.0f, 1.0f};
auto d4 = std::vector<float>{2.0f, 1.0f, 1.0f, 1.0f};
auto d5 = std::vector<float>{1.5f, 1.0f, 1.0f, 1.0f};

for (size_t i = 0; i < 4; ++i) {
data.set_datum(i, d0);
}
data.set_datum(4, d4);
data.set_datum(5, d5);

auto dist = svs::distance::DistanceL2();
auto accessor = svs::data::GetDatumAccessor{};

std::vector<svs::Neighbor<size_t>> pool = {
{size_t{0}, 0.0f},
{size_t{1}, 0.0f},
{size_t{2}, 0.0f},
{size_t{3}, 0.0f},
{size_t{4}, 1.0f}};

CATCH_SECTION("Iterative Strategy Fix") {
std::vector<svs::Neighbor<size_t>> result;
v::heuristic_prune_neighbors(
v::IterativePruneStrategy{},
2,
1.3f,
data,
accessor,
dist,
size_t{5},
std::span<const svs::Neighbor<size_t>>(pool),
result
);

CATCH_REQUIRE(result.size() == 2);
CATCH_REQUIRE(result[0].id() == 0);
CATCH_REQUIRE(result[1].id() == 4);
}

CATCH_SECTION("Progressive Strategy Fix") {
std::vector<svs::Neighbor<size_t>> result;
v::heuristic_prune_neighbors(
v::ProgressivePruneStrategy{},
2,
1.3f,
data,
accessor,
dist,
size_t{5},
std::span<const svs::Neighbor<size_t>>(pool),
result
);

CATCH_REQUIRE(result.size() == 2);
CATCH_REQUIRE(result[0].id() == 0);
CATCH_REQUIRE(result[1].id() == 4);
}
}
}
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