"""A set of graph services of getting subgraphs from DistGraph""" import os from collections import namedtuple import numpy as np import torch from .. import backend as F, graphbolt as gb from ..base import EID, ETYPE, NID from ..convert import graph, heterograph from ..sampling import ( sample_etype_neighbors as local_sample_etype_neighbors, sample_neighbors as local_sample_neighbors, ) from ..subgraph import in_subgraph as local_in_subgraph from ..utils import toindex from .constants import DGL2GB_EID, GB_DST_ID from .rpc import ( recv_responses, register_service, Request, Response, send_requests_to_machine, ) __all__ = [ "sample_neighbors", "sample_etype_neighbors", "in_subgraph", "find_edges", ] SAMPLING_SERVICE_ID = 6657 INSUBGRAPH_SERVICE_ID = 6658 EDGES_SERVICE_ID = 6659 OUTDEGREE_SERVICE_ID = 6660 INDEGREE_SERVICE_ID = 6661 ETYPE_SAMPLING_SERVICE_ID = 6662 class SubgraphResponse(Response): """The response for sampling and in_subgraph""" def __init__( self, global_src, global_dst, *, global_eids=None, etype_ids=None ): self.global_src = global_src self.global_dst = global_dst self.global_eids = global_eids self.etype_ids = etype_ids def __setstate__(self, state): ( self.global_src, self.global_dst, self.global_eids, self.etype_ids, ) = state def __getstate__(self): return ( self.global_src, self.global_dst, self.global_eids, self.etype_ids, ) class FindEdgeResponse(Response): """The response for sampling and in_subgraph""" def __init__(self, global_src, global_dst, order_id): self.global_src = global_src self.global_dst = global_dst self.order_id = order_id def __setstate__(self, state): self.global_src, self.global_dst, self.order_id = state def __getstate__(self): return self.global_src, self.global_dst, self.order_id def _sample_neighbors_graphbolt( g, gpb, nodes, fanout, edge_dir="in", prob=None, exclude_edges=None, replace=False, ): """Sample from local partition via graphbolt. The input nodes use global IDs. We need to map the global node IDs to local node IDs, perform sampling and map the sampled results to the global IDs space again. The sampled results are stored in three vectors that store source nodes, destination nodes, etype IDs and edge IDs. Parameters ---------- g : FusedCSCSamplingGraph The local partition. gpb : GraphPartitionBook The graph partition book. nodes : tensor The nodes to sample neighbors from. fanout : tensor or int The number of edges to be sampled for each node. edge_dir : str, optional Determines whether to sample inbound or outbound edges. prob : tensor, optional The probability associated with each neighboring edge of a node. exclude_edges : tensor, optional The edges to exclude when sampling. replace : bool, optional If True, sample with replacement. Returns ------- tensor The source node ID array. tensor The destination node ID array. tensor The edge ID array. tensor The edge type ID array. """ assert ( edge_dir == "in" ), f"GraphBolt only supports inbound edge sampling but got {edge_dir}." assert exclude_edges is None, "GraphBolt does not support excluding edges." # 1. Map global node IDs to local node IDs. nodes = gpb.nid2localnid(nodes, gpb.partid) # Local partition may be saved in torch.int32 even though the global graph # is in torch.int64. nodes = nodes.to(dtype=g.indices.dtype) # 2. Perform sampling. probs_or_mask = None if prob is not None: probs_or_mask = g.edge_attributes[prob] # Sanity checks. assert isinstance( g, gb.FusedCSCSamplingGraph ), "Expect a FusedCSCSamplingGraph." assert isinstance(nodes, torch.Tensor), "Expect a tensor of nodes." if isinstance(fanout, int): fanout = torch.LongTensor([fanout]) assert isinstance(fanout, torch.Tensor), "Expect a tensor of fanout." subgraph = g._sample_neighbors( nodes, None, fanout, replace=replace, probs_or_mask=probs_or_mask, ) # 3. Map local node IDs to global node IDs. local_src = subgraph.indices local_dst = gb.expand_indptr( subgraph.indptr, dtype=local_src.dtype, node_ids=subgraph.original_column_node_ids, output_size=local_src.shape[0], ) global_nid_mapping = g.node_attributes[NID] global_src = global_nid_mapping[local_src] global_dst = global_nid_mapping[local_dst] global_eids = None if g.edge_attributes is not None and EID in g.edge_attributes: global_eids = g.edge_attributes[EID][subgraph.original_edge_ids] return LocalSampledGraph( global_src, global_dst, global_eids, subgraph.type_per_edge ) def _sample_neighbors_dgl( local_g, partition_book, seed_nodes, fan_out, edge_dir="in", prob=None, exclude_edges=None, replace=False, ): """Sample from local partition. The input nodes use global IDs. We need to map the global node IDs to local node IDs, perform sampling and map the sampled results to the global IDs space again. The sampled results are stored in three vectors that store source nodes, destination nodes and edge IDs. """ local_ids = partition_book.nid2localnid(seed_nodes, partition_book.partid) local_ids = F.astype(local_ids, local_g.idtype) # local_ids = self.seed_nodes sampled_graph = local_sample_neighbors( local_g, local_ids, fan_out, edge_dir=edge_dir, prob=prob, exclude_edges=exclude_edges, replace=replace, _dist_training=True, ) global_nid_mapping = local_g.ndata[NID] src, dst = sampled_graph.edges() global_src, global_dst = F.gather_row( global_nid_mapping, src ), F.gather_row(global_nid_mapping, dst) global_eids = F.gather_row(local_g.edata[EID], sampled_graph.edata[EID]) return LocalSampledGraph(global_src, global_dst, global_eids) def _sample_neighbors(use_graphbolt, *args, **kwargs): """Wrapper for sampling neighbors. The actual sampling function depends on whether to use GraphBolt. Parameters ---------- use_graphbolt : bool Whether to use GraphBolt for sampling. args : list The arguments for the sampling function. kwargs : dict The keyword arguments for the sampling function. Returns ------- tensor The source node ID array. tensor The destination node ID array. tensor The edge ID array. tensor The edge type ID array. """ func = ( _sample_neighbors_graphbolt if use_graphbolt else _sample_neighbors_dgl ) return func(*args, **kwargs) def _sample_etype_neighbors_dgl( local_g, partition_book, seed_nodes, fan_out, edge_dir="in", prob=None, exclude_edges=None, replace=False, etype_offset=None, etype_sorted=False, ): """Sample from local partition. The input nodes use global IDs. We need to map the global node IDs to local node IDs, perform sampling and map the sampled results to the global IDs space again. The sampled results are stored in three vectors that store source nodes, destination nodes and edge IDs. """ assert etype_offset is not None, "The etype offset is not provided." local_ids = partition_book.nid2localnid(seed_nodes, partition_book.partid) local_ids = F.astype(local_ids, local_g.idtype) sampled_graph = local_sample_etype_neighbors( local_g, local_ids, etype_offset, fan_out, edge_dir=edge_dir, prob=prob, exclude_edges=exclude_edges, replace=replace, etype_sorted=etype_sorted, _dist_training=True, ) global_nid_mapping = local_g.ndata[NID] src, dst = sampled_graph.edges() global_src, global_dst = F.gather_row( global_nid_mapping, src ), F.gather_row(global_nid_mapping, dst) global_eids = F.gather_row(local_g.edata[EID], sampled_graph.edata[EID]) return LocalSampledGraph(global_src, global_dst, global_eids) def _sample_etype_neighbors(use_graphbolt, *args, **kwargs): """Wrapper for sampling etype neighbors. The actual sampling function depends on whether to use GraphBolt. Parameters ---------- use_graphbolt : bool Whether to use GraphBolt for sampling. args : list The arguments for the sampling function. kwargs : dict The keyword arguments for the sampling function. Returns ------- tensor The source node ID array. tensor The destination node ID array. tensor The edge ID array. tensor The edge type ID array. """ func = ( _sample_neighbors_graphbolt if use_graphbolt else _sample_etype_neighbors_dgl ) if use_graphbolt: # GraphBolt does not require `etype_offset` and `etype_sorted`. kwargs.pop("etype_offset", None) kwargs.pop("etype_sorted", None) return func(*args, **kwargs) def _find_edges(local_g, partition_book, seed_edges): """Given an edge ID array, return the source and destination node ID array ``s`` and ``d`` in the local partition. """ local_eids = partition_book.eid2localeid(seed_edges, partition_book.partid) if isinstance(local_g, gb.FusedCSCSamplingGraph): # When converting from DGLGraph to FusedCSCSamplingGraph, the edge IDs # are re-ordered. In order to find the correct node pairs, we need to # map the DGL edge IDs back to GraphBolt edge IDs. if ( DGL2GB_EID not in local_g.edge_attributes or GB_DST_ID not in local_g.edge_attributes ): raise ValueError( "The edge attributes DGL2GB_EID and GB_DST_ID are not found. " "Please make sure `coo` format is available when generating " "partitions in GraphBolt format." ) local_eids = local_g.edge_attributes[DGL2GB_EID][local_eids] local_src = local_g.indices[local_eids] local_dst = local_g.edge_attributes[GB_DST_ID][local_eids] global_nid_mapping = local_g.node_attributes[NID] else: local_eids = F.astype(local_eids, local_g.idtype) local_src, local_dst = local_g.find_edges(local_eids) global_nid_mapping = local_g.ndata[NID] global_src = global_nid_mapping[local_src] global_dst = global_nid_mapping[local_dst] return global_src, global_dst def _in_degrees(local_g, partition_book, n): """Get in-degree of the nodes in the local partition.""" local_nids = partition_book.nid2localnid(n, partition_book.partid) local_nids = F.astype(local_nids, local_g.idtype) return local_g.in_degrees(local_nids) def _out_degrees(local_g, partition_book, n): """Get out-degree of the nodes in the local partition.""" local_nids = partition_book.nid2localnid(n, partition_book.partid) local_nids = F.astype(local_nids, local_g.idtype) return local_g.out_degrees(local_nids) def _in_subgraph(local_g, partition_book, seed_nodes): """Get in subgraph from local partition. The input nodes use global IDs. We need to map the global node IDs to local node IDs, get in-subgraph and map the sampled results to the global IDs space again. The results are stored in three vectors that store source nodes, destination nodes and edge IDs. """ local_ids = partition_book.nid2localnid(seed_nodes, partition_book.partid) local_ids = F.astype(local_ids, local_g.idtype) # local_ids = self.seed_nodes sampled_graph = local_in_subgraph(local_g, local_ids) global_nid_mapping = local_g.ndata[NID] src, dst = sampled_graph.edges() global_src, global_dst = global_nid_mapping[src], global_nid_mapping[dst] global_eids = F.gather_row(local_g.edata[EID], sampled_graph.edata[EID]) return LocalSampledGraph(global_src, global_dst, global_eids) # --- NOTE 1 --- # (BarclayII) # If the sampling algorithm needs node and edge data, ideally the # algorithm should query the underlying feature storage to get what it # just needs to complete the job. For instance, with # sample_etype_neighbors, we only need the probability of the seed nodes' # neighbors. # # However, right now we are reusing the existing subgraph sampling # interfaces of DGLGraph (i.e. single machine solution), which needs # the data of *all* the nodes/edges. Going distributed, we now need # the node/edge data of the *entire* local graph partition. # # If the sampling algorithm only use edge data, the current design works # because the local graph partition contains all the in-edges of the # assigned nodes as well as the data. This is the case for # sample_etype_neighbors. # # However, if the sampling algorithm requires data of the neighbor nodes # (e.g. sample_neighbors_biased which performs biased sampling based on the # type of the neighbor nodes), the current design will fail because the # neighbor nodes (hence the data) may not belong to the current partition. # This is a limitation of the current DistDGL design. We should improve it # later. class SamplingRequest(Request): """Sampling Request""" def __init__( self, nodes, fan_out, edge_dir="in", prob=None, exclude_edges=None, replace=False, use_graphbolt=False, ): self.seed_nodes = nodes self.edge_dir = edge_dir self.prob = prob self.exclude_edges = exclude_edges self.replace = replace self.fan_out = fan_out self.use_graphbolt = use_graphbolt def __setstate__(self, state): ( self.seed_nodes, self.edge_dir, self.prob, self.exclude_edges, self.replace, self.fan_out, self.use_graphbolt, ) = state def __getstate__(self): return ( self.seed_nodes, self.edge_dir, self.prob, self.exclude_edges, self.replace, self.fan_out, self.use_graphbolt, ) def process_request(self, server_state): local_g = server_state.graph partition_book = server_state.partition_book kv_store = server_state.kv_store if self.prob is not None and (not self.use_graphbolt): prob = [kv_store.data_store[self.prob]] else: prob = self.prob res = _sample_neighbors( self.use_graphbolt, local_g, partition_book, self.seed_nodes, self.fan_out, edge_dir=self.edge_dir, prob=prob, exclude_edges=self.exclude_edges, replace=self.replace, ) return SubgraphResponse( res.global_src, res.global_dst, global_eids=res.global_eids, etype_ids=res.etype_ids, ) class SamplingRequestEtype(Request): """Sampling Request""" def __init__( self, nodes, fan_out, edge_dir="in", prob=None, exclude_edges=None, replace=False, etype_sorted=True, use_graphbolt=False, ): self.seed_nodes = nodes self.edge_dir = edge_dir self.prob = prob self.exclude_edges = exclude_edges self.replace = replace self.fan_out = fan_out self.etype_sorted = etype_sorted self.use_graphbolt = use_graphbolt def __setstate__(self, state): ( self.seed_nodes, self.edge_dir, self.prob, self.exclude_edges, self.replace, self.fan_out, self.etype_sorted, self.use_graphbolt, ) = state def __getstate__(self): return ( self.seed_nodes, self.edge_dir, self.prob, self.exclude_edges, self.replace, self.fan_out, self.etype_sorted, self.use_graphbolt, ) def process_request(self, server_state): local_g = server_state.graph partition_book = server_state.partition_book kv_store = server_state.kv_store etype_offset = partition_book.local_etype_offset # See NOTE 1 if self.prob is not None and (not self.use_graphbolt): probs = [ kv_store.data_store[key] if key != "" else None for key in self.prob ] else: probs = self.prob res = _sample_etype_neighbors( self.use_graphbolt, local_g, partition_book, self.seed_nodes, self.fan_out, edge_dir=self.edge_dir, prob=probs, exclude_edges=self.exclude_edges, replace=self.replace, etype_offset=etype_offset, etype_sorted=self.etype_sorted, ) return SubgraphResponse( res.global_src, res.global_dst, global_eids=res.global_eids, etype_ids=res.etype_ids, ) class EdgesRequest(Request): """Edges Request""" def __init__(self, edge_ids, order_id): self.edge_ids = edge_ids self.order_id = order_id def __setstate__(self, state): self.edge_ids, self.order_id = state def __getstate__(self): return self.edge_ids, self.order_id def process_request(self, server_state): local_g = server_state.graph partition_book = server_state.partition_book global_src, global_dst = _find_edges( local_g, partition_book, self.edge_ids ) return FindEdgeResponse(global_src, global_dst, self.order_id) class InDegreeRequest(Request): """In-degree Request""" def __init__(self, n, order_id): self.n = n self.order_id = order_id def __setstate__(self, state): self.n, self.order_id = state def __getstate__(self): return self.n, self.order_id def process_request(self, server_state): local_g = server_state.graph partition_book = server_state.partition_book deg = _in_degrees(local_g, partition_book, self.n) return InDegreeResponse(deg, self.order_id) class InDegreeResponse(Response): """The response for in-degree""" def __init__(self, deg, order_id): self.val = deg self.order_id = order_id def __setstate__(self, state): self.val, self.order_id = state def __getstate__(self): return self.val, self.order_id class OutDegreeRequest(Request): """Out-degree Request""" def __init__(self, n, order_id): self.n = n self.order_id = order_id def __setstate__(self, state): self.n, self.order_id = state def __getstate__(self): return self.n, self.order_id def process_request(self, server_state): local_g = server_state.graph partition_book = server_state.partition_book deg = _out_degrees(local_g, partition_book, self.n) return OutDegreeResponse(deg, self.order_id) class OutDegreeResponse(Response): """The response for out-degree""" def __init__(self, deg, order_id): self.val = deg self.order_id = order_id def __setstate__(self, state): self.val, self.order_id = state def __getstate__(self): return self.val, self.order_id class InSubgraphRequest(Request): """InSubgraph Request""" def __init__(self, nodes): self.seed_nodes = nodes def __setstate__(self, state): self.seed_nodes = state def __getstate__(self): return self.seed_nodes def process_request(self, server_state): local_g = server_state.graph partition_book = server_state.partition_book global_src, global_dst, global_eids = _in_subgraph( local_g, partition_book, self.seed_nodes ) return SubgraphResponse(global_src, global_dst, global_eids=global_eids) def merge_graphs(res_list, num_nodes, exclude_edges=None): """Merge request from multiple servers""" if len(res_list) > 1: srcs = [] dsts = [] eids = [] etype_ids = [] for res in res_list: srcs.append(res.global_src) dsts.append(res.global_dst) eids.append(res.global_eids) etype_ids.append(res.etype_ids) src_tensor = F.cat(srcs, 0) dst_tensor = F.cat(dsts, 0) eid_tensor = None if eids[0] is None else F.cat(eids, 0) etype_id_tensor = None if etype_ids[0] is None else F.cat(etype_ids, 0) else: src_tensor = res_list[0].global_src dst_tensor = res_list[0].global_dst eid_tensor = res_list[0].global_eids etype_id_tensor = res_list[0].etype_ids if exclude_edges is not None: mask = torch.isin( eid_tensor, exclude_edges, assume_unique=True, invert=True ) src_tensor = src_tensor[mask] dst_tensor = dst_tensor[mask] eid_tensor = eid_tensor[mask] if etype_id_tensor is not None: etype_id_tensor = etype_id_tensor[mask] g = graph((src_tensor, dst_tensor), num_nodes=num_nodes) if eid_tensor is not None: g.edata[EID] = eid_tensor if etype_id_tensor is not None: g.edata[ETYPE] = etype_id_tensor return g LocalSampledGraph = namedtuple( # pylint: disable=unexpected-keyword-arg "LocalSampledGraph", "global_src global_dst global_eids etype_ids", defaults=(None, None, None, None), ) def _distributed_access( g, nodes, issue_remote_req, local_access, exclude_edges=None ): """A routine that fetches local neighborhood of nodes from the distributed graph. The local neighborhood of some nodes are stored in the local machine and the other nodes have their neighborhood on remote machines. This code will issue remote access requests first before fetching data from the local machine. In the end, we combine the data from the local machine and remote machines. In this way, we can hide the latency of accessing data on remote machines. Parameters ---------- g : DistGraph The distributed graph nodes : tensor The nodes whose neighborhood are to be fetched. issue_remote_req : callable The function that issues requests to access remote data. local_access : callable The function that reads data on the local machine. exclude_edges : tensor The edges to exclude after sampling. Returns ------- DGLGraph The subgraph that contains the neighborhoods of all input nodes. """ req_list = [] partition_book = g.get_partition_book() if not isinstance(nodes, torch.Tensor): nodes = toindex(nodes).tousertensor() partition_id = partition_book.nid2partid(nodes) local_nids = None for pid in range(partition_book.num_partitions()): node_id = F.boolean_mask(nodes, partition_id == pid) # We optimize the sampling on a local partition if the server and the client # run on the same machine. With a good partitioning, most of the seed nodes # should reside in the local partition. If the server and the client # are not co-located, the client doesn't have a local partition. if pid == partition_book.partid and g.local_partition is not None: assert local_nids is None local_nids = node_id elif len(node_id) != 0: req = issue_remote_req(node_id) req_list.append((pid, req)) # send requests to the remote machine. msgseq2pos = None if len(req_list) > 0: msgseq2pos = send_requests_to_machine(req_list) # sample neighbors for the nodes in the local partition. res_list = [] if local_nids is not None: res = local_access(g.local_partition, partition_book, local_nids) res_list.append(res) # receive responses from remote machines. if msgseq2pos is not None: results = recv_responses(msgseq2pos) res_list.extend(results) sampled_graph = merge_graphs( res_list, g.num_nodes(), exclude_edges=exclude_edges ) return sampled_graph def _frontier_to_heterogeneous_graph(g, frontier, gpb): # We need to handle empty frontiers correctly. if frontier.num_edges() == 0: data_dict = { etype: (np.zeros(0), np.zeros(0)) for etype in g.canonical_etypes } return heterograph( data_dict, {ntype: g.num_nodes(ntype) for ntype in g.ntypes}, idtype=g.idtype, ) # For DGL partitions, the global edge IDs are always stored in the edata. # For GraphBolt partitions, the edge type IDs are always stored in the # edata. As for the edge IDs, they are stored in the edata if the graph is # partitioned with `store_eids=True`. Otherwise, the edge IDs are not # stored. etype_ids, type_wise_eids = ( gpb.map_to_per_etype(frontier.edata[EID]) if EID in frontier.edata else (frontier.edata[ETYPE], None) ) etype_ids, idx = F.sort_1d(etype_ids) if type_wise_eids is not None: type_wise_eids = F.gather_row(type_wise_eids, idx) # Sort the edges by their edge types. src, dst = frontier.edges() src, dst = F.gather_row(src, idx), F.gather_row(dst, idx) src_ntype_ids, src = gpb.map_to_per_ntype(src) dst_ntype_ids, dst = gpb.map_to_per_ntype(dst) data_dict = dict() edge_ids = {} for etid, etype in enumerate(g.canonical_etypes): src_ntype, _, dst_ntype = etype src_ntype_id = g.get_ntype_id(src_ntype) dst_ntype_id = g.get_ntype_id(dst_ntype) type_idx = etype_ids == etid data_dict[etype] = ( F.boolean_mask(src, type_idx), F.boolean_mask(dst, type_idx), ) if "DGL_DIST_DEBUG" in os.environ: assert torch.all( src_ntype_id == src_ntype_ids[type_idx] ), "source ntype is is not expected." assert torch.all( dst_ntype_id == dst_ntype_ids[type_idx] ), "destination ntype is is not expected." if type_wise_eids is not None: edge_ids[etype] = F.boolean_mask(type_wise_eids, type_idx) hg = heterograph( data_dict, {ntype: g.num_nodes(ntype) for ntype in g.ntypes}, idtype=g.idtype, ) for etype in edge_ids: hg.edges[etype].data[EID] = edge_ids[etype] return hg def sample_etype_neighbors( g, nodes, fanout, edge_dir="in", prob=None, exclude_edges=None, replace=False, etype_sorted=True, use_graphbolt=False, ): """Sample from the neighbors of the given nodes from a distributed graph. For each node, a number of inbound (or outbound when ``edge_dir == 'out'``) edges will be randomly chosen. The returned graph will contain all the nodes in the original graph, but only the sampled edges. Node/edge features are not preserved. The original IDs of the sampled edges are stored as the `dgl.EID` feature in the returned graph. This function assumes the input is a homogeneous ``DGLGraph`` with the edges ordered by their edge types. The sampled subgraph is also stored in the homogeneous graph format. That is, all nodes and edges are assigned with unique IDs (in contrast, we typically use a type name and a node/edge ID to identify a node or an edge in ``DGLGraph``). We refer to this type of IDs as *homogeneous ID*. Users can use :func:`dgl.distributed.GraphPartitionBook.map_to_per_ntype` and :func:`dgl.distributed.GraphPartitionBook.map_to_per_etype` to identify their node/edge types and node/edge IDs of that type. Parameters ---------- g : DistGraph The distributed graph.. nodes : tensor or dict Node IDs to sample neighbors from. If it's a dict, it should contain only one key-value pair to make this API consistent with dgl.sampling.sample_neighbors. fanout : int or dict[etype, int] The number of edges to be sampled for each node per edge type. If an integer is given, DGL assumes that the same fanout is applied to every edge type. If -1 is given, all of the neighbors will be selected. edge_dir : str, optional Determines whether to sample inbound or outbound edges. Can take either ``in`` for inbound edges or ``out`` for outbound edges. prob : str, optional Feature name used as the (unnormalized) probabilities associated with each neighboring edge of a node. The feature must have only one element for each edge. The features must be non-negative floats, and the sum of the features of inbound/outbound edges for every node must be positive (though they don't have to sum up to one). Otherwise, the result will be undefined. exclude_edges : tensor, optional The edges to exclude when sampling. Homogeneous edge IDs are used. replace : bool, optional If True, sample with replacement. When sampling with replacement, the sampled subgraph could have parallel edges. For sampling without replacement, if fanout > the number of neighbors, all the neighbors are sampled. If fanout == -1, all neighbors are collected. etype_sorted : bool, optional Indicates whether etypes are sorted. use_graphbolt : bool, optional Whether to use GraphBolt for sampling. Returns ------- DGLGraph A sampled subgraph containing only the sampled neighboring edges. It is on CPU. """ if isinstance(fanout, int): fanout = F.full_1d(len(g.canonical_etypes), fanout, F.int64, F.cpu()) else: etype_ids = {etype: i for i, etype in enumerate(g.canonical_etypes)} fanout_array = [None] * len(g.canonical_etypes) for etype, v in fanout.items(): c_etype = g.to_canonical_etype(etype) fanout_array[etype_ids[c_etype]] = v assert all(v is not None for v in fanout_array), ( "Not all etypes have valid fanout. Please make sure passed-in " "fanout in dict includes all the etypes in graph. Passed-in " f"fanout: {fanout}, graph etypes: {g.canonical_etypes}." ) fanout = F.tensor(fanout_array, dtype=F.int64) gpb = g.get_partition_book() if isinstance(nodes, dict): homo_nids = [] for ntype in nodes.keys(): assert ( ntype in g.ntypes ), "The sampled node type {} does not exist in the input graph".format( ntype ) if F.is_tensor(nodes[ntype]): typed_nodes = nodes[ntype] else: typed_nodes = toindex(nodes[ntype]).tousertensor() homo_nids.append(gpb.map_to_homo_nid(typed_nodes, ntype)) nodes = F.cat(homo_nids, 0) def issue_remote_req(node_ids): if prob is not None and (not use_graphbolt): # See NOTE 1 _prob = [ ( # NOTE (BarclayII) # Currently DistGraph.edges[] does not accept canonical etype. g.edges[etype].data[prob].kvstore_key if prob in g.edges[etype].data else "" ) for etype in g.canonical_etypes ] else: _prob = prob return SamplingRequestEtype( node_ids, fanout, edge_dir=edge_dir, prob=_prob, exclude_edges=None, replace=replace, etype_sorted=etype_sorted, use_graphbolt=use_graphbolt, ) def local_access(local_g, partition_book, local_nids): etype_offset = gpb.local_etype_offset # See NOTE 1 if prob is not None and (not use_graphbolt): _prob = [ ( g.edges[etype].data[prob].local_partition if prob in g.edges[etype].data else None ) for etype in g.canonical_etypes ] else: _prob = prob return _sample_etype_neighbors( use_graphbolt, local_g, partition_book, local_nids, fanout, edge_dir=edge_dir, prob=_prob, exclude_edges=None, replace=replace, etype_offset=etype_offset, etype_sorted=etype_sorted, ) frontier = _distributed_access( g, nodes, issue_remote_req, local_access, exclude_edges=exclude_edges ) if not gpb.is_homogeneous: return _frontier_to_heterogeneous_graph(g, frontier, gpb) else: return frontier def sample_neighbors( g, nodes, fanout, edge_dir="in", prob=None, exclude_edges=None, replace=False, use_graphbolt=False, ): """Sample from the neighbors of the given nodes from a distributed graph. For each node, a number of inbound (or outbound when ``edge_dir == 'out'``) edges will be randomly chosen. The returned graph will contain all the nodes in the original graph, but only the sampled edges. Node/edge features are not preserved. The original IDs of the sampled edges are stored as the `dgl.EID` feature in the returned graph. For heterogeneous graphs, ``nodes`` is a dictionary whose key is node type and the value is type-specific node IDs. Parameters ---------- g : DistGraph The distributed graph.. nodes : tensor or dict Node IDs to sample neighbors from. If it's a dict, it should contain only one key-value pair to make this API consistent with dgl.sampling.sample_neighbors. fanout : int The number of edges to be sampled for each node. If -1 is given, all of the neighbors will be selected. edge_dir : str, optional Determines whether to sample inbound or outbound edges. Can take either ``in`` for inbound edges or ``out`` for outbound edges. prob : str, optional Feature name used as the (unnormalized) probabilities associated with each neighboring edge of a node. The feature must have only one element for each edge. The features must be non-negative floats, and the sum of the features of inbound/outbound edges for every node must be positive (though they don't have to sum up to one). Otherwise, the result will be undefined. exclude_edges: tensor or dict, optional Edge IDs to exclude during sampling neighbors for the seed nodes. This argument can take a single ID tensor or a dictionary of edge types and ID tensors. If a single tensor is given, the graph must only have one type of nodes. replace : bool, optional If True, sample with replacement. When sampling with replacement, the sampled subgraph could have parallel edges. For sampling without replacement, if fanout > the number of neighbors, all the neighbors are sampled. If fanout == -1, all neighbors are collected. use_graphbolt : bool, optional Whether to use GraphBolt for sampling. Returns ------- DGLGraph A sampled subgraph containing only the sampled neighboring edges. It is on CPU. """ gpb = g.get_partition_book() if not gpb.is_homogeneous: assert isinstance(nodes, dict) homo_nids = [] for ntype in nodes: assert ( ntype in g.ntypes ), "The sampled node type does not exist in the input graph" if F.is_tensor(nodes[ntype]): typed_nodes = nodes[ntype] else: typed_nodes = toindex(nodes[ntype]).tousertensor() homo_nids.append(gpb.map_to_homo_nid(typed_nodes, ntype)) nodes = F.cat(homo_nids, 0) elif isinstance(nodes, dict): assert len(nodes) == 1 nodes = list(nodes.values())[0] def issue_remote_req(node_ids): if prob is not None and (not use_graphbolt): # See NOTE 1 _prob = g.edata[prob].kvstore_key else: _prob = prob return SamplingRequest( node_ids, fanout, edge_dir=edge_dir, prob=_prob, exclude_edges=None, replace=replace, use_graphbolt=use_graphbolt, ) def local_access(local_g, partition_book, local_nids): # See NOTE 1 _prob = ( [g.edata[prob].local_partition] if prob is not None and (not use_graphbolt) else prob ) return _sample_neighbors( use_graphbolt, local_g, partition_book, local_nids, fanout, edge_dir=edge_dir, prob=_prob, exclude_edges=None, replace=replace, ) frontier = _distributed_access( g, nodes, issue_remote_req, local_access, exclude_edges=exclude_edges ) if not gpb.is_homogeneous: return _frontier_to_heterogeneous_graph(g, frontier, gpb) else: return frontier def _distributed_edge_access(g, edges, issue_remote_req, local_access): """A routine that fetches local edges from distributed graph. The source and destination nodes of local edges are stored in the local machine and others are stored on remote machines. This code will issue remote access requests first before fetching data from the local machine. In the end, we combine the data from the local machine and remote machines. Parameters ---------- g : DistGraph The distributed graph edges : tensor The edges to find their source and destination nodes. issue_remote_req : callable The function that issues requests to access remote data. local_access : callable The function that reads data on the local machine. Returns ------- tensor The source node ID array. tensor The destination node ID array. """ req_list = [] partition_book = g.get_partition_book() edges = toindex(edges).tousertensor() partition_id = partition_book.eid2partid(edges) local_eids = None reorder_idx = [] for pid in range(partition_book.num_partitions()): mask = partition_id == pid edge_id = F.boolean_mask(edges, mask) reorder_idx.append(F.nonzero_1d(mask)) if pid == partition_book.partid and g.local_partition is not None: assert local_eids is None local_eids = edge_id elif len(edge_id) != 0: req = issue_remote_req(edge_id, pid) req_list.append((pid, req)) # send requests to the remote machine. msgseq2pos = None if len(req_list) > 0: msgseq2pos = send_requests_to_machine(req_list) # handle edges in local partition. src_ids = F.zeros_like(edges) dst_ids = F.zeros_like(edges) if local_eids is not None: src, dst = local_access(g.local_partition, partition_book, local_eids) src_ids = F.scatter_row( src_ids, reorder_idx[partition_book.partid], src ) dst_ids = F.scatter_row( dst_ids, reorder_idx[partition_book.partid], dst ) # receive responses from remote machines. if msgseq2pos is not None: results = recv_responses(msgseq2pos) for result in results: src = result.global_src dst = result.global_dst src_ids = F.scatter_row(src_ids, reorder_idx[result.order_id], src) dst_ids = F.scatter_row(dst_ids, reorder_idx[result.order_id], dst) return src_ids, dst_ids def find_edges(g, edge_ids): """Given an edge ID array, return the source and destination node ID array ``s`` and ``d`` from a distributed graph. ``s[i]`` and ``d[i]`` are source and destination node ID for edge ``eid[i]``. Parameters ---------- g : DistGraph The distributed graph. edges : tensor The edge ID array. Returns ------- tensor The source node ID array. tensor The destination node ID array. """ def issue_remote_req(edge_ids, order_id): return EdgesRequest(edge_ids, order_id) def local_access(local_g, partition_book, edge_ids): return _find_edges(local_g, partition_book, edge_ids) return _distributed_edge_access(g, edge_ids, issue_remote_req, local_access) def in_subgraph(g, nodes): """Return the subgraph induced on the inbound edges of the given nodes. The subgraph keeps the same type schema and all the nodes are preserved regardless of whether they have an edge or not. Node/edge features are not preserved. The original IDs of the extracted edges are stored as the `dgl.EID` feature in the returned graph. For now, we only support the input graph with one node type and one edge type. Parameters ---------- g : DistGraph The distributed graph structure. nodes : tensor or dict Node ids to sample neighbors from. Returns ------- DGLGraph The subgraph. One can retrieve the mapping from subgraph edge ID to parent edge ID via ``dgl.EID`` edge features of the subgraph. """ if isinstance(nodes, dict): assert ( len(nodes) == 1 ), "The distributed in_subgraph only supports one node type for now." nodes = list(nodes.values())[0] def issue_remote_req(node_ids): return InSubgraphRequest(node_ids) def local_access(local_g, partition_book, local_nids): return _in_subgraph(local_g, partition_book, local_nids) return _distributed_access(g, nodes, issue_remote_req, local_access) def _distributed_get_node_property(g, n, issue_remote_req, local_access): req_list = [] partition_book = g.get_partition_book() n = toindex(n).tousertensor() partition_id = partition_book.nid2partid(n) local_nids = None reorder_idx = [] for pid in range(partition_book.num_partitions()): mask = partition_id == pid nid = F.boolean_mask(n, mask) reorder_idx.append(F.nonzero_1d(mask)) if pid == partition_book.partid and g.local_partition is not None: assert local_nids is None local_nids = nid elif len(nid) != 0: req = issue_remote_req(nid, pid) req_list.append((pid, req)) # send requests to the remote machine. msgseq2pos = None if len(req_list) > 0: msgseq2pos = send_requests_to_machine(req_list) # handle edges in local partition. vals = None if local_nids is not None: local_vals = local_access(g.local_partition, partition_book, local_nids) shape = list(F.shape(local_vals)) shape[0] = len(n) vals = F.zeros(shape, F.dtype(local_vals), F.cpu()) vals = F.scatter_row( vals, reorder_idx[partition_book.partid], local_vals ) # receive responses from remote machines. if msgseq2pos is not None: results = recv_responses(msgseq2pos) if len(results) > 0 and vals is None: shape = list(F.shape(results[0].val)) shape[0] = len(n) vals = F.zeros(shape, F.dtype(results[0].val), F.cpu()) for result in results: val = result.val vals = F.scatter_row(vals, reorder_idx[result.order_id], val) return vals def in_degrees(g, v): """Get in-degrees""" def issue_remote_req(v, order_id): return InDegreeRequest(v, order_id) def local_access(local_g, partition_book, v): return _in_degrees(local_g, partition_book, v) return _distributed_get_node_property(g, v, issue_remote_req, local_access) def out_degrees(g, u): """Get out-degrees""" def issue_remote_req(u, order_id): return OutDegreeRequest(u, order_id) def local_access(local_g, partition_book, u): return _out_degrees(local_g, partition_book, u) return _distributed_get_node_property(g, u, issue_remote_req, local_access) register_service(SAMPLING_SERVICE_ID, SamplingRequest, SubgraphResponse) register_service(EDGES_SERVICE_ID, EdgesRequest, FindEdgeResponse) register_service(INSUBGRAPH_SERVICE_ID, InSubgraphRequest, SubgraphResponse) register_service(OUTDEGREE_SERVICE_ID, OutDegreeRequest, OutDegreeResponse) register_service(INDEGREE_SERVICE_ID, InDegreeRequest, InDegreeResponse) register_service( ETYPE_SAMPLING_SERVICE_ID, SamplingRequestEtype, SubgraphResponse )