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remove outdated codes
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@jnke2016
jnke2016 committed Aug 20, 2024
1 parent 8fc21f8 commit 01a57f3
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233 changes: 0 additions & 233 deletions cpp/include/cugraph/sampling_functions.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -140,114 +140,6 @@ uniform_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_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.
*
Expand Down Expand Up @@ -461,131 +353,6 @@ 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.
*
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