support heterogenous fanout type

cpp/include/cugraph/sampling_functions.hpp

@@ -129,14 +129,124 @@ uniform_neighbor_sample(
  std::optional<raft::device_span<label_t const>> starting_vertex_labels,
  std::optional<std::tuple<raft::device_span<label_t const>, raft::device_span<int32_t const>>>
  label_to_output_comm_rank,
- raft::host_span<int32_t const> fan_out,
+ std::optional<raft::host_span<int32_t const>> fan_out,
+ std::optional<std::tuple<raft::host_span<int32_t const>, raft::host_span<int32_t const>>>
+ heterogeneous_fan_out,
  raft::random::RngState& rng_state,
  bool return_hops,
  bool with_replacement = true,
  prior_sources_behavior_t prior_sources_behavior = prior_sources_behavior_t::DEFAULT,
  bool dedupe_sources = false,
  bool do_expensive_check = false);
 
+#if 0
+/* FIXME:
+ There are two options to support heterogeneous fanout
+ 1) Create a new function 'heterogeneous_uniform_neighbor_sample' which will take as input
+ only heterogeneous fanout type. Drawback: code redundancy
+ 2) Update 'uniform_neighbor_sample' to support both fanout types
+*/
+/**
+ * @brief Heterogeneous uniform Neighborhood Sampling.
+ *
+ * This function traverses from a set of starting vertices, traversing outgoing edges and
+ * randomly selects from these outgoing neighbors to extract a subgraph.
+ *
+ * Output from this function is a tuple of vectors (src, dst, weight, edge_id, edge_type, hop,
+ * label, offsets), identifying the randomly selected edges. src is the source vertex, dst is the
+ * destination vertex, weight (optional) is the edge weight, edge_id (optional) identifies the edge
+ * id, edge_type (optional) identifies the edge type, hop identifies which hop the edge was
+ * encountered in. The label output (optional) identifes the vertex label. The offsets array
+ * (optional) will be described below and is dependent upon the input parameters.
+ *
+ * If @p starting_vertex_labels is not specified then no organization is applied to the output, the
+ * label and offsets values in the return set will be std::nullopt.
+ *
+ * If @p starting_vertex_labels is specified and @p label_to_output_comm_rank is not specified then
+ * the label output has values. This will also result in the output being sorted by vertex label.
+ * The offsets array in the return will be a CSR-style offsets array to identify the beginning of
+ * each label range in the data. `labels.size() == (offsets.size() - 1)`.
+ *
+ * If @p starting_vertex_labels is specified and @p label_to_output_comm_rank is specified then the
+ * label output has values. This will also result in the output being sorted by vertex label. The
+ * offsets array in the return will be a CSR-style offsets array to identify the beginning of each
+ * label range in the data. `labels.size() == (offsets.size() - 1)`. Additionally, the data will
+ * be shuffled so that all data with a particular label will be on the specified rank.
+ *
+ * @tparam vertex_t Type of vertex identifiers. Needs to be an integral type.
+ * @tparam edge_t Type of edge identifiers. Needs to be an integral type.
+ * @tparam weight_t Type of edge weights. Needs to be a floating point type.
+ * @tparam edge_type_t Type of edge type. Needs to be an integral type.
+ * @tparam label_t Type of label. Needs to be an integral type.
+ * @tparam store_transposed Flag indicating whether sources (if false) or destinations (if
+ * true) are major indices
+ * @tparam multi_gpu Flag indicating whether template instantiation should target single-GPU (false)
+ * @param handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, and
+ * handles to various CUDA libraries) to run graph algorithms.
+ * @param graph_view Graph View object to generate NBR Sampling on.
+ * @param edge_weight_view Optional view object holding edge weights for @p graph_view.
+ * @param edge_id_view Optional view object holding edge ids for @p graph_view.
+ * @param edge_type_view Optional view object holding edge types for @p graph_view.
+ * @param starting_vertices Device span of starting vertex IDs for the sampling.
+ * In a multi-gpu context the starting vertices should be local to this GPU.
+ * @param starting_vertex_labels Optional device span of labels associted with each starting vertex
+ * for the sampling.
+ * @param label_to_output_comm_rank Optional tuple of device spans mapping label to a particular
+ * output rank. Element 0 of the tuple identifes the label, Element 1 of the tuple identifies the
+ * output rank. The label span must be sorted in ascending order.
+ * @param fan_out Tuple of host spans mapping each edge type to fanout values. Element 0
+ * of the tuple defines the size of the fanout per edge type while element 1 defines the branching out
+ * (fan-out) degree per edge type for each level.
+ * @param rng_state A pre-initialized raft::RngState object for generating random numbers
+ * @param return_hops boolean flag specifying if the hop information should be returned
+ * @param prior_sources_behavior Enum type defining how to handle prior sources, (defaults to
+ * DEFAULT)
+ * @param dedupe_sources boolean flag, if true then if a vertex v appears as a destination in hop X
+ * multiple times with the same label, it will only be passed once (for each label) as a source
+ * for the next hop. Default is false.
+ * @param with_replacement boolean flag specifying if random sampling is done with replacement
+ * (true); or, without replacement (false); default = true;
+ * @param do_expensive_check A flag to run expensive checks for input arguments (if set to `true`).
+ * @return tuple device vectors (vertex_t source_vertex, vertex_t destination_vertex,
+ * optional weight_t weight, optional edge_t edge id, optional edge_type_t edge type,
+ * optional int32_t hop, optional label_t label, optional size_t offsets)
+ */
+
+// tuple with 3 elements. 1 - edge_type, 2- host span of size_t, 3 - fanout vector
+template <typename vertex_t,
+ typename edge_t,
+ typename weight_t,
+ typename edge_type_t,
+ typename label_t,
+ bool store_transposed,
+ bool multi_gpu>
+std::tuple<rmm::device_uvector<vertex_t>,
+ rmm::device_uvector<vertex_t>,
+ std::optional<rmm::device_uvector<weight_t>>,
+ std::optional<rmm::device_uvector<edge_t>>,
+ std::optional<rmm::device_uvector<edge_type_t>>,
+ std::optional<rmm::device_uvector<int32_t>>,
+ std::optional<rmm::device_uvector<label_t>>,
+ std::optional<rmm::device_uvector<size_t>>>
+heterogeneous_uniform_neighbor_sample(
+ raft::handle_t const& handle,
+ graph_view_t<vertex_t, edge_t, store_transposed, multi_gpu> const& graph_view,
+ std::optional<edge_property_view_t<edge_t, weight_t const*>> edge_weight_view,
+ std::optional<edge_property_view_t<edge_t, edge_t const*>> edge_id_view,
+ std::optional<edge_property_view_t<edge_t, edge_type_t const*>> edge_type_view,
+ raft::device_span<vertex_t const> starting_vertices,
+ std::optional<raft::device_span<label_t const>> starting_vertex_labels,
+ std::optional<std::tuple<raft::device_span<label_t const>, raft::device_span<int32_t const>>>
+ label_to_output_comm_rank,
+ std::tuple<raft::host_span<int32_t const>, raft::host_span<int32_t const>> fan_out,
+ raft::random::RngState& rng_state,
+ bool return_hops,
+ bool with_replacement = true,
+ prior_sources_behavior_t prior_sources_behavior = prior_sources_behavior_t::DEFAULT,
+ bool dedupe_sources = false,
+ bool do_expensive_check = false);
+#endif
+
 /**
  * @brief Biased Neighborhood Sampling.
  *
@@ -240,6 +350,118 @@ biased_neighbor_sample(
  bool dedupe_sources = false,
  bool do_expensive_check = false);
 
+#if 0
+/* FIXME:
+ There are two options to support heterogeneous fanout
+ 1) Create a new function 'heterogeneous_biased_neighbor_sample' which will take as input
+ only heterogeneous fanout type. Drawback: code redundancy
+ 2) Update 'biased_neighbor_sample' to support both fanout types
+*/
+/**
+ * @brief Biased Neighborhood Sampling.
+ *
+ * This function traverses from a set of starting vertices, traversing outgoing edges and
+ * randomly selects (with edge biases) from these outgoing neighbors to extract a subgraph.
+ *
+ * Output from this function is a tuple of vectors (src, dst, weight, edge_id, edge_type, hop,
+ * label, offsets), identifying the randomly selected edges. src is the source vertex, dst is the
+ * destination vertex, weight (optional) is the edge weight, edge_id (optional) identifies the edge
+ * id, edge_type (optional) identifies the edge type, hop identifies which hop the edge was
+ * encountered in. The label output (optional) identifes the vertex label. The offsets array
+ * (optional) will be described below and is dependent upon the input parameters.
+ *
+ * If @p starting_vertex_labels is not specified then no organization is applied to the output, the
+ * label and offsets values in the return set will be std::nullopt.
+ *
+ * If @p starting_vertex_labels is specified and @p label_to_output_comm_rank is not specified then
+ * the label output has values. This will also result in the output being sorted by vertex label.
+ * The offsets array in the return will be a CSR-style offsets array to identify the beginning of
+ * each label range in the data. `labels.size() == (offsets.size() - 1)`.
+ *
+ * If @p starting_vertex_labels is specified and @p label_to_output_comm_rank is specified then the
+ * label output has values. This will also result in the output being sorted by vertex label. The
+ * offsets array in the return will be a CSR-style offsets array to identify the beginning of each
+ * label range in the data. `labels.size() == (offsets.size() - 1)`. Additionally, the data will
+ * be shuffled so that all data with a particular label will be on the specified rank.
+ *
+ * @tparam vertex_t Type of vertex identifiers. Needs to be an integral type.
+ * @tparam edge_t Type of edge identifiers. Needs to be an integral type.
+ * @tparam weight_t Type of edge weights. Needs to be a floating point type.
+ * @tparam edge_type_t Type of edge type. Needs to be an integral type.
+ * @tparam label_t Type of label. Needs to be an integral type.
+ * @tparam store_transposed Flag indicating whether sources (if false) or destinations (if
+ * true) are major indices
+ * @tparam multi_gpu Flag indicating whether template instantiation should target single-GPU (false)
+ * @param handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, and
+ * handles to various CUDA libraries) to run graph algorithms.
+ * @param graph_view Graph View object to generate NBR Sampling on.
+ * @param edge_weight_view Optional view object holding edge weights for @p graph_view.
+ * @param edge_id_view Optional view object holding edge ids for @p graph_view.
+ * @param edge_type_view Optional view object holding edge types for @p graph_view.
+ * @param edge_bias_view View object holding edge biases (to be used in biased sampling) for @p
+ * graph_view. Bias values should be non-negative and the sum of edge bias values from any vertex
+ * should not exceed std::numeric_limits<bias_t>::max(). 0 bias value indicates that the
+ * corresponding edge can never be selected.
+ * @param starting_vertices Device span of starting vertex IDs for the sampling.
+ * In a multi-gpu context the starting vertices should be local to this GPU.
+ * @param starting_vertex_labels Optional device span of labels associted with each starting vertex
+ * for the sampling.
+ * @param label_to_output_comm_rank Optional tuple of device spans mapping label to a particular
+ * output rank. Element 0 of the tuple identifes the label, Element 1 of the tuple identifies the
+ * output rank. The label span must be sorted in ascending order.
+ * @param fan_out Tuple of host spans mapping each edge type to fanout values. Element 0
+ * of the tuple defines the size of the fanout per edge type while element 1 defines the branching out
+ * (fan-out) degree per edge type for each level.
+ * @param rng_state A pre-initialized raft::RngState object for generating random numbers
+ * @param return_hops boolean flag specifying if the hop information should be returned
+ * @param prior_sources_behavior Enum type defining how to handle prior sources, (defaults to
+ * DEFAULT)
+ * @param dedupe_sources boolean flag, if true then if a vertex v appears as a destination in hop X
+ * multiple times with the same label, it will only be passed once (for each label) as a source
+ * for the next hop. Default is false.
+ * @param with_replacement boolean flag specifying if random sampling is done with replacement
+ * (true); or, without replacement (false); default = true;
+ * @param do_expensive_check A flag to run expensive checks for input arguments (if set to `true`).
+ * @return tuple device vectors (vertex_t source_vertex, vertex_t destination_vertex,
+ * optional weight_t weight, optional edge_t edge id, optional edge_type_t edge type,
+ * optional int32_t hop, optional label_t label, optional size_t offsets)
+ */
+template <typename vertex_t,
+ typename edge_t,
+ typename weight_t,
+ typename edge_type_t,
+ typename bias_t,
+ typename label_t,
+ bool store_transposed,
+ bool multi_gpu>
+std::tuple<rmm::device_uvector<vertex_t>,
+ rmm::device_uvector<vertex_t>,
+ std::optional<rmm::device_uvector<weight_t>>,
+ std::optional<rmm::device_uvector<edge_t>>,
+ std::optional<rmm::device_uvector<edge_type_t>>,
+ std::optional<rmm::device_uvector<int32_t>>,
+ std::optional<rmm::device_uvector<label_t>>,
+ std::optional<rmm::device_uvector<size_t>>>
+heterogeneous_biased_neighbor_sample(
+ raft::handle_t const& handle,
+ graph_view_t<vertex_t, edge_t, store_transposed, multi_gpu> const& graph_view,
+ std::optional<edge_property_view_t<edge_t, weight_t const*>> edge_weight_view,
+ std::optional<edge_property_view_t<edge_t, edge_t const*>> edge_id_view,
+ std::optional<edge_property_view_t<edge_t, edge_type_t const*>> edge_type_view,
+ edge_property_view_t<edge_t, bias_t const*> edge_bias_view,
+ raft::device_span<vertex_t const> starting_vertices,
+ std::optional<raft::device_span<label_t const>> starting_vertex_labels,
+ std::optional<std::tuple<raft::device_span<label_t const>, raft::device_span<int32_t const>>>
+ label_to_output_comm_rank,
+ std::tuple<raft::host_span<int32_t const>, raft::host_span<int32_t const>> fan_out,
+ raft::random::RngState& rng_state,
+ bool return_hops,
+ bool with_replacement = true,
+ prior_sources_behavior_t prior_sources_behavior = prior_sources_behavior_t::DEFAULT,
+ bool dedupe_sources = false,
+ bool do_expensive_check = false);
+#endif
+
 /*
  * @brief renumber sampled edge list and compress to the (D)CSR|(D)CSC format.
  *

cpp/include/cugraph_c/sampling_algorithms.h

@@ -319,6 +319,48 @@ void cugraph_sampling_set_dedupe_sources(cugraph_sampling_options_t* options, bo
  */
 void cugraph_sampling_options_free(cugraph_sampling_options_t* options);
 
+/**
+ * @brief Opaque neighborhood sampling heterogeneous fanout type
+ */
+// FIXME: internal representation should be tuple instead of pairs - Make it more generic (tuple)
+// cugraph_device_tuple_t, host_device_tuple_t,
+// dictionary, key and array
+// translate dictionary to a tuple. Add to the draft PR the PLC layer.
+// Concatenate to build the 3 arrays from the PLC layer
+/// mimic
+typedef struct {
+ int32_t align_;
+} cugraph_sample_heterogeneous_fanout_t;
+
+/**
+ * @brief Create heterogeneous fanout
+ *
+ * Input data will be stored in the heterogenous_fanout.
+ *
+ * @param [in] handle Handle for accessing resources
+ * @param [in] graph Pointer to graph
+ * @param [in] edge_type_size Type erased array of edge type size
+ * @param [in] fanout Type erased array of fanout values
+ * @param [out] heterogeneous_fanout Opaque pointer to fanout_t
+ * @param [out] error Pointer to an error object storing details of any error. Will
+ * be populated if error code is not CUGRAPH_SUCCESS
+ * @return error code
+ */
+cugraph_error_code_t cugraph_create_heterogeneous_fanout(
+ const cugraph_resource_handle_t* handle,
+ cugraph_graph_t* graph,
+ const cugraph_type_erased_host_array_view_t* edge_type_size,
+ const cugraph_type_erased_host_array_view_t* fanout,
+ cugraph_sample_heterogeneous_fanout_t** heterogeneous_fanout,
+ cugraph_error_t** error);
+
+/**
+ * @brief Free edge type and fanout pairs
+ *
+ * @param [in] heterogeneous_fanout The edge type size and fanout values
+ */
+void cugraph_heterogeneous_fanout_free(cugraph_sample_heterogeneous_fanout_t* heterogeneous_fanout);
+
 /**
  * @brief Uniform Neighborhood Sampling
  *
@@ -368,6 +410,7 @@ cugraph_error_code_t cugraph_uniform_neighbor_sample(
  const cugraph_type_erased_device_array_view_t* label_to_comm_rank,
  const cugraph_type_erased_device_array_view_t* label_offsets,
  const cugraph_type_erased_host_array_view_t* fan_out,
+ const cugraph_sample_heterogeneous_fanout_t* heterogeneous_fanout,
  cugraph_rng_state_t* rng_state,
  const cugraph_sampling_options_t* options,
  bool_t do_expensive_check,
@@ -667,6 +710,7 @@ cugraph_error_code_t cugraph_test_uniform_neighborhood_sample_result_create(
  * not CUGRAPH_SUCCESS
  * @return error code
  */
+
 cugraph_error_code_t cugraph_select_random_vertices(const cugraph_resource_handle_t* handle,
  const cugraph_graph_t* graph,
  cugraph_rng_state_t* rng_state,

cpp/src/c_api/array.hpp

@@ -125,6 +125,18 @@ struct cugraph_type_erased_host_array_t {
  std::copy(vec.begin(), vec.end(), reinterpret_cast<T*>(data_.get()));
  }
 
+ template <typename T>
+ T* as_type()
+ {
+ return reinterpret_cast<T*>(data_.get());
+ }
+
+ template <typename T>
+ T const* as_type() const
+ {
+ return reinterpret_cast<T const*>(data_.get());
+ }
+
  auto view()
  {
  return new cugraph_type_erased_host_array_view_t{data_.get(), size_, num_bytes_, type_};

cpp/src/c_api/graph_functions.cpp

@@ -84,7 +84,7 @@ struct create_vertex_pairs_functor : public cugraph::c_api::abstract_functor {
  std::nullopt,
  std::nullopt);
  }
-
+ // std::tuple (template)
  result_ = new cugraph::c_api::cugraph_vertex_pairs_t{
  new cugraph::c_api::cugraph_type_erased_device_array_t(first_copy, graph_->vertex_type_),
  new cugraph::c_api::cugraph_type_erased_device_array_t(second_copy, graph_->vertex_type_)};