Merge branch-23.04 into branch-23.06

.github/workflows/pr.yaml

@@ -105,7 +105,7 @@ jobs:
       build_type: pull-request
       package-name: raft_dask
       # Always want to test against latest dask/distributed.
-      test-before-amd64: "RAPIDS_PY_WHEEL_NAME=pylibraft_cu11 rapids-download-wheels-from-s3 ./local-pylibraft-dep && pip install --no-deps ./local-pylibraft-dep/pylibraft*.whl && pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main git+https://github.com/rapidsai/[email protected]"
-      test-before-arm64: "RAPIDS_PY_WHEEL_NAME=pylibraft_cu11 rapids-download-wheels-from-s3 ./local-pylibraft-dep && pip install --no-deps ./local-pylibraft-dep/pylibraft*.whl && pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main git+https://github.com/rapidsai/[email protected]"
+      test-before-amd64: "RAPIDS_PY_WHEEL_NAME=pylibraft_cu11 rapids-download-wheels-from-s3 ./local-pylibraft-dep && pip install --no-deps ./local-pylibraft-dep/pylibraft*.whl && pip install git+https://github.com/dask/dask.git@2023.3.2 git+https://github.com/dask/distributed.git@2023.3.2.1 git+https://github.com/rapidsai/[email protected]"
+      test-before-arm64: "RAPIDS_PY_WHEEL_NAME=pylibraft_cu11 rapids-download-wheels-from-s3 ./local-pylibraft-dep && pip install --no-deps ./local-pylibraft-dep/pylibraft*.whl && pip install git+https://github.com/dask/dask.git@2023.3.2 git+https://github.com/dask/distributed.git@2023.3.2.1 git+https://github.com/rapidsai/[email protected]"
       test-unittest: "python -m pytest -v ./python/raft-dask/raft_dask/test"
       test-smoketest: "python ./ci/wheel_smoke_test_raft_dask.py"

.github/workflows/test.yaml

@@ -51,6 +51,6 @@ jobs:
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
       package-name: raft_dask
-      test-before-amd64: "pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main git+https://github.com/rapidsai/[email protected]"
-      test-before-arm64: "pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main git+https://github.com/rapidsai/[email protected]"
+      test-before-amd64: "pip install git+https://github.com/dask/dask.git@2023.3.2 git+https://github.com/dask/distributed.git@2023.3.2.1 git+https://github.com/rapidsai/[email protected]"
+      test-before-arm64: "pip install git+https://github.com/dask/dask.git@2023.3.2 git+https://github.com/dask/distributed.git@2023.3.2.1 git+https://github.com/rapidsai/[email protected]"
       test-unittest: "python -m pytest -v ./python/raft-dask/raft_dask/test"

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -18,9 +18,10 @@ dependencies:
 - cupy
 - cxx-compiler
 - cython>=0.29,<0.30
+- dask-core==2023.3.2
 - dask-cuda==23.6.*
-- dask>=2023.1.1
-- distributed>=2023.1.1
+- dask==2023.3.2
+- distributed==2023.3.2.1
 - doxygen>=1.8.20
 - gcc_linux-64=11.*
 - graphviz

conda/recipes/raft-dask/meta.yaml

@@ -46,9 +46,10 @@ requirements:
   run:
     - {{ pin_compatible('cudatoolkit', max_pin='x', min_pin='x') }}
     - cuda-python >=11.7.1,<12.0
-    - dask >=2023.1.1
+    - dask ==2023.3.2
+    - dask-core ==2023.3.2
     - dask-cuda ={{ minor_version }}
-    - distributed >=2023.1.1
+    - distributed ==2023.3.2.1
     - joblib >=0.11
     - nccl >=2.9.9
     - pylibraft {{ version }}

cpp/include/raft/neighbors/cagra.cuh

@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/cagra/cagra_build.cuh"
+#include "detail/cagra/cagra_search.cuh"
+#include "detail/cagra/graph_core.cuh"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/neighbors/cagra_types.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace raft::neighbors::experimental::cagra {
+
+/**
+ * @defgroup cagra CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
+/**
+ * @brief Build a kNN graph.
+ *
+ * The kNN graph is the first building block for CAGRA index.
+ * This function uses the IVF-PQ method to build a kNN graph.
+ *
+ * The output is a dense matrix that stores the neighbor indices for each pont in the dataset.
+ * Each point has the same number of neighbors.
+ *
+ * See [cagra::build](#cagra::build) for an alternative method.
+ *
+ * The following distance metrics are supported:
+ * - L2Expanded
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params build_params;
+ *   ivf_pq::search_params search_params
+ *   auto knn_graph      = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 128);
+ *   // create knn graph
+ *   cagra::build_knn_graph(res, dataset, knn_graph.view(), 2, build_params, search_params);
+ *   auto pruned_gaph      = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 64);
+ *   cagra::prune(res, dataset, knn_graph.view(), pruned_graph.view());
+ *   // Construct an index from dataset and pruned knn_graph
+ *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset, pruned_graph.view());
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res raft resources
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ * @param[out] knn_graph a host matrix view to store the output knn graph [n_rows, graph_degree]
+ * @param[in] refine_rate refinement rate for ivf-pq search
+ * @param[in] build_params (optional) ivf_pq index building parameters for knn graph
+ * @param[in] search_params (optional) ivf_pq search parameters
+ */
+template <typename DataT, typename IdxT, typename accessor>
+void build_knn_graph(raft::device_resources const& res,
+                     mdspan<const DataT, matrix_extent<IdxT>, row_major, accessor> dataset,
+                     raft::host_matrix_view<IdxT, IdxT, row_major> knn_graph,
+                     std::optional<float> refine_rate                   = std::nullopt,
+                     std::optional<ivf_pq::index_params> build_params   = std::nullopt,
+                     std::optional<ivf_pq::search_params> search_params = std::nullopt)
+{
+  detail::build_knn_graph(res, dataset, knn_graph, refine_rate, build_params, search_params);
+}
+
+/**
+ * @brief Prune a KNN graph.
+ *
+ * Decrease the number of neighbors for each node.
+ *
+ * See [cagra::build_knn_graph](#cagra::build_knn_graph) for usage example
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res raft resources
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ * @param[in] knn_graph a matrix view (host or device) of the input knn graph [n_rows,
+ * knn_graph_degree]
+ * @param[out] new_graph a host matrix view of the pruned knn graph [n_rows, graph_degree]
+ */
+template <class DATA_T,
+          typename IdxT = uint32_t,
+          typename d_accessor =
+            host_device_accessor<std::experimental::default_accessor<DATA_T>, memory_type::device>,
+          typename g_accessor =
+            host_device_accessor<std::experimental::default_accessor<DATA_T>, memory_type::host>>
+void prune(raft::device_resources const& res,
+           mdspan<const DATA_T, matrix_extent<IdxT>, row_major, d_accessor> dataset,
+           mdspan<IdxT, matrix_extent<IdxT>, row_major, g_accessor> knn_graph,
+           raft::host_matrix_view<IdxT, IdxT, row_major> new_graph)
+{
+  detail::graph::prune(res, dataset, knn_graph, new_graph);
+}
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and prune it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * It is required that dataset and the pruned graph fit the GPU memory.
+ *
+ * To customize the parameters for knn-graph building and pruning, and to reuse the
+ * intermediate results, you could build the index in two steps using
+ * [cagra::build_knn_graph](#cagra::build_knn_graph) and [cagra::prune](#cagra::prune).
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   ivf_pq::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+template <typename T,
+          typename IdxT = uint32_t,
+          typename Accessor =
+            host_device_accessor<std::experimental::default_accessor<T>, memory_type::host>>
+index<T, IdxT> build(raft::device_resources const& res,
+                     const index_params& params,
+                     mdspan<const T, matrix_extent<IdxT>, row_major, Accessor> dataset)
+{
+  size_t degree = params.intermediate_graph_degree;
+  if (degree >= dataset.extent(0)) {
+    RAFT_LOG_WARN(
+      "Intermediate graph degree cannot be larger than dataset size, reducing it to %lu",
+      dataset.extent(0));
+    degree = dataset.extent(0) - 1;
+  }
+  RAFT_EXPECTS(degree >= params.graph_degree,
+               "Intermediate graph degree cannot be smaller than final graph degree");
+
+  auto knn_graph = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), degree);
+
+  build_knn_graph(res, dataset, knn_graph.view());
+
+  auto cagra_graph = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), params.graph_degree);
+
+  prune<T, IdxT>(res, dataset, knn_graph.view(), cagra_graph.view());
+
+  // Construct an index from dataset and pruned knn graph.
+  return index<T, IdxT>(res, params.metric, dataset, cagra_graph.view());
+}
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] idx cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+template <typename T, typename IdxT>
+void search(raft::device_resources const& res,
+            const search_params& params,
+            const index<T, IdxT>& idx,
+            raft::device_matrix_view<const T, IdxT, row_major> queries,
+            raft::device_matrix_view<IdxT, IdxT, row_major> neighbors,
+            raft::device_matrix_view<float, IdxT, row_major> distances)
+{
+  RAFT_EXPECTS(
+    queries.extent(0) == neighbors.extent(0) && queries.extent(0) == distances.extent(0),
+    "Number of rows in output neighbors and distances matrices must equal the number of queries.");
+
+  RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1),
+               "Number of columns in output neighbors and distances matrices must equal k");
+
+  RAFT_EXPECTS(queries.extent(1) == idx.dim(),
+               "Number of query dimensions should equal number of dimensions in the index.");
+
+  detail::search_main(res, params, idx, queries, neighbors, distances);
+}
+/** @} */  // end group cagra
+
+}  // namespace raft::neighbors::experimental::cagra