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Merged
GPUtester merged 1 commit into from
Jul 29, 2026
Merged

Forward-merge release/26.08 into main #2378

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104 changes: 72 additions & 32 deletions cpp/src/cluster/detail/minClusterDistanceCompute.cu
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
/*
* SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
* SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*/

Expand Down Expand Up @@ -27,15 +27,15 @@ void minClusterAndDistanceCompute(
int batch_centroids,
rmm::device_uvector<char>& workspace)
{
cudaStream_t stream = raft::resource::get_cuda_stream(handle);
auto n_samples = X.extent(0);
auto n_features = X.extent(1);
auto n_clusters = centroids.extent(0);
bool is_fused = metric == cuvs::distance::DistanceType::L2Expanded ||
metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
metric == cuvs::distance::DistanceType::CosineExpanded;

if (is_fused) {
cudaStream_t stream = raft::resource::get_cuda_stream(handle);
auto n_samples = X.extent(0);
auto n_features = X.extent(1);
auto n_clusters = centroids.extent(0);
const bool can_use_fused_path = metric == cuvs::distance::DistanceType::L2Expanded ||
metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
metric == cuvs::distance::DistanceType::CosineExpanded;

if (can_use_fused_path) {
L2NormBuf_OR_DistBuf.resize(n_clusters, stream);
auto centroidsNorm =
raft::make_device_vector_view<DataT, IndexT>(L2NormBuf_OR_DistBuf.data(), n_clusters);
Expand All @@ -51,10 +51,10 @@ void minClusterAndDistanceCompute(
raft::KeyValuePair<IndexT, DataT> initial_value(0, std::numeric_limits<DataT>::max());
raft::matrix::fill(handle, minClusterAndDistance, initial_value);

bool should_use_fused =
const bool use_fused_path =
use_fused<DataT, IndexT, IndexT>(handle, n_samples, n_clusters, n_features);

if (should_use_fused) {
if (use_fused_path) {
workspace.resize((sizeof(int)) * n_samples, stream);

cuvs::distance::fusedDistanceNNMinReduce<DataT, raft::KeyValuePair<IndexT, DataT>, IndexT>(
Expand All @@ -74,26 +74,66 @@ void minClusterAndDistanceCompute(
0.0f,
stream);
} else {
workspace.resize(sizeof(DataT) * n_samples * n_clusters, stream);

cuvs::distance::
unfusedDistanceNNMinReduce<DataT, DataT, raft::KeyValuePair<IndexT, DataT>, IndexT>(
handle,
minClusterAndDistance.data_handle(),
X.data_handle(),
centroids.data_handle(),
L2NormX.data_handle(),
centroidsNorm.data_handle(),
n_samples,
n_clusters,
n_features,
(void*)workspace.data(),
metric != cuvs::distance::DistanceType::L2Expanded,
false,
true,
metric,
0.0f,
stream);
auto dataBatchSize = getDataBatchSize(batch_samples, n_samples);
auto centroidsBatchSize = getCentroidsBatchSize(batch_centroids, n_clusters);

// The unfused reduction indexes its distance matrix with IndexT.
dataBatchSize =
std::min(dataBatchSize, std::numeric_limits<IndexT>::max() / centroidsBatchSize);

workspace.resize(sizeof(DataT) * dataBatchSize * centroidsBatchSize, stream);

using KeyValueT = raft::KeyValuePair<IndexT, DataT>;
const bool tileCentroids = centroidsBatchSize < n_clusters;
rmm::device_uvector<KeyValueT> batchMinClusterAndDistance(tileCentroids ? dataBatchSize : 0,
stream);

for (IndexT dIdx = 0; dIdx < n_samples;) {
auto ns = std::min(dataBatchSize, n_samples - dIdx);
auto minClusterAndDistanceView = raft::make_device_vector_view<KeyValueT, IndexT>(
minClusterAndDistance.data_handle() + dIdx, ns);

for (IndexT cIdx = 0; cIdx < n_clusters;) {
auto nc = std::min(centroidsBatchSize, n_clusters - cIdx);
auto batchMin = tileCentroids ? batchMinClusterAndDistance.data()
: minClusterAndDistanceView.data_handle();

cuvs::distance::unfusedDistanceNNMinReduce<DataT, DataT, KeyValueT, IndexT>(
handle,
batchMin,
X.data_handle() + dIdx * n_features,
centroids.data_handle() + cIdx * n_features,
L2NormX.data_handle() + dIdx,
centroidsNorm.data_handle() + cIdx,
ns,
nc,
n_features,
(void*)workspace.data(),
metric != cuvs::distance::DistanceType::L2Expanded,
tileCentroids,
true,
metric,
0.0f,
stream);

if (tileCentroids) {
// Convert tile-local centroid indices and merge the tile minima.
auto batchMinView =
raft::make_device_vector_view<const KeyValueT, IndexT>(batchMin, ns);
raft::linalg::map(
handle,
minClusterAndDistanceView,
[cIdx] __device__(KeyValueT current, KeyValueT batch) {
batch.key += cIdx;
return batch.value < current.value ? batch : current;
},
raft::make_const_mdspan(minClusterAndDistanceView),
batchMinView);
}
cIdx += nc;
}
dIdx += ns;
}
}
} else {
auto dataBatchSize = getDataBatchSize(batch_samples, n_samples);
Expand Down
9 changes: 7 additions & 2 deletions cpp/tests/CMakeLists.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -123,8 +123,13 @@ ConfigureTest(

ConfigureTest(
NAME CLUSTER_TEST
PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu cluster/linkage.cu
cluster/connect_knn.cu cluster/spectral.cu
PATH cluster/kmeans.cu
cluster/kmeans_balanced.cu
cluster/kmeans_find_k.cu
cluster/kmeans_predict_batching.cu
cluster/linkage.cu
cluster/connect_knn.cu
cluster/spectral.cu
GPUS 1
PERCENT 100
)
Expand Down
155 changes: 155 additions & 0 deletions cpp/tests/cluster/kmeans_predict_batching.cu
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Original file line number Diff line number Diff line change
@@ -0,0 +1,155 @@
/*
* SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*/

#include <cuvs/cluster/kmeans.hpp>

#include "../../src/cluster/detail/kmeans_common.cuh"

#include <raft/core/copy.cuh>
#include <raft/core/device_mdarray.hpp>
#include <raft/core/memory_stats_resources.hpp>
#include <raft/core/resource/cuda_stream.hpp>
#include <raft/util/cudart_utils.hpp>

#include <gtest/gtest.h>

#include <array>
#include <cstddef>
#include <cstdint>
#include <optional>

namespace cuvs::cluster::kmeans {

struct BatchConfig {
char const* name;
int batch_samples;
int batch_centroids;
};

constexpr int test_n_samples = 8;
constexpr int test_n_clusters = 8;
constexpr int test_n_features = 2;

template <typename IndexT>
std::size_t run_predict_with_batching(raft::resources const& handle, BatchConfig config)
{
SCOPED_TRACE(config.name);

using DataT = float;

constexpr IndexT n_samples = test_n_samples;
constexpr IndexT n_clusters = test_n_clusters;
constexpr IndexT n_features = test_n_features;

// The nearest centroids span all three centroid tiles. The batched configurations also have
// partial final tiles, exercising the tile-local index adjustment and the minima merge.
constexpr std::array<DataT, n_samples * n_features> h_x{0.0f,
0.0f,
0.5f,
0.5f,
3.0f,
3.0f,
3.25f,
3.25f,
6.0f,
6.0f,
5.75f,
5.75f,
2.0f,
2.0f,
4.0f,
4.0f};
constexpr std::array<DataT, n_clusters * n_features> h_centroids{0.0f,
0.0f,
100.0f,
100.0f,
200.0f,
200.0f,
3.0f,
3.0f,
400.0f,
400.0f,
500.0f,
500.0f,
6.0f,
6.0f,
700.0f,
700.0f};
constexpr std::array<IndexT, n_samples> expected_clusters{0, 0, 3, 3, 6, 6, 3, 3};
constexpr DataT expected_inertia = 4.75f;

auto stream = raft::resource::get_cuda_stream(handle);
auto x = raft::make_device_matrix<DataT, IndexT>(handle, n_samples, n_features);
auto centroids = raft::make_device_matrix<DataT, IndexT>(handle, n_clusters, n_features);
auto labels = raft::make_device_vector<IndexT, IndexT>(handle, n_samples);

raft::update_device(x.data_handle(), h_x.data(), h_x.size(), stream);
raft::update_device(centroids.data_handle(), h_centroids.data(), h_centroids.size(), stream);

params kmeans_params;
kmeans_params.n_clusters = n_clusters;
kmeans_params.batch_samples = config.batch_samples;
kmeans_params.batch_centroids = config.batch_centroids;

DataT inertia = 0;
std::size_t total_device_bytes = 0;
{
raft::memory_stats_resources tracked_handle{handle};
predict(tracked_handle,
kmeans_params,
raft::make_const_mdspan(x.view()),
std::optional<raft::device_vector_view<const DataT, IndexT>>{std::nullopt},
raft::make_const_mdspan(centroids.view()),
labels.view(),
false,
raft::make_host_scalar_view(&inertia));
total_device_bytes = tracked_handle.get_bytes_total_allocated().device_global;
}

std::array<IndexT, n_samples> h_labels;
raft::update_host(h_labels.data(), labels.data_handle(), h_labels.size(), stream);
raft::resource::sync_stream(handle);

EXPECT_NEAR(inertia, expected_inertia, 1e-4f);
for (std::size_t i = 0; i < h_labels.size(); ++i) {
EXPECT_EQ(h_labels[i], expected_clusters[i]) << "sample " << i;
}

return total_device_bytes;
}

TEST(KMeansPredict, BatchParametersPreserveResultsAndReduceUnfusedAllocations)
{
raft::resources handle;
constexpr std::array<BatchConfig, 4> batch_configs{{
{"no batching", 0, 0},
{"samples only", 3, 0},
{"centroids only", 0, 3},
{"samples and centroids", 3, 3},
}};

auto unbatched_int_bytes = run_predict_with_batching<int>(handle, batch_configs.front());
auto unbatched_int64_bytes = run_predict_with_batching<int64_t>(handle, batch_configs.front());

// predict selects fused or unfused 1-NN according to the architecture heuristic. The batching
// parameters only affect the unfused path, so every GPU checks the results while allocation
// reductions are required only when this problem shape dispatches to unfused 1-NN.
const bool uses_unfused_path =
!detail::use_fused<float, int, int>(handle, test_n_samples, test_n_clusters, test_n_features);

for (std::size_t i = 1; i < batch_configs.size(); ++i) {
auto config = batch_configs[i];
auto int_bytes = run_predict_with_batching<int>(handle, config);
auto int64_bytes = run_predict_with_batching<int64_t>(handle, config);

if (uses_unfused_path) {
// Verify that batching uses less memory than the unbatched path.
EXPECT_LT(int_bytes, unbatched_int_bytes) << config.name;
EXPECT_LT(int64_bytes, unbatched_int64_bytes) << config.name;
}
}
}

} // namespace cuvs::cluster::kmeans
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