Source code for dgl.distributed.graph_partition_book

"""Define graph partition book."""

import pickle
from abc import ABC
import numpy as np

from .. import backend as F
from ..base import NID, EID
from .. import utils
from .shared_mem_utils import _to_shared_mem, _get_ndata_path, _get_edata_path, DTYPE_DICT
from .._ffi.ndarray import empty_shared_mem
from ..ndarray import exist_shared_mem_array
from .id_map import IdMap

def _move_metadata_to_shared_mem(graph_name, num_nodes, num_edges, part_id,
                                 num_partitions, node_map, edge_map, is_range_part):
    ''' Move all metadata of the partition book to the shared memory.

    These metadata will be used to construct graph partition book.

    Parameters
    ----------
    graph_name : str
        The name of the graph
    num_nodes : int
        The total number of nodes
    num_edges : int
        The total number of edges
    part_id : int
        The partition ID.
    num_partitions : int
        The number of physical partitions generated for the graph.
    node_map : Tensor
        It stores the mapping information from node IDs to partitions. With range partitioning,
        the tensor stores the serialized result of partition ranges.
    edge_map : Tensor
        It stores the mapping information from edge IDs to partitions. With range partitioning,
        the tensor stores the serialized result of partition ranges.
    is_range_part : bool
        Indicate that we use a range partition. This is important for us to deserialize data
        in node_map and edge_map.

    Returns
    -------
    (Tensor, Tensor, Tensor)
        The first tensor stores the serialized metadata, the second tensor stores the serialized
        node map and the third tensor stores the serialized edge map. All tensors are stored in
        shared memory.
    '''
    meta = _to_shared_mem(F.tensor([int(is_range_part), num_nodes, num_edges,
                                    num_partitions, part_id,
                                    len(node_map), len(edge_map)]),
                          _get_ndata_path(graph_name, 'meta'))
    node_map = _to_shared_mem(node_map, _get_ndata_path(graph_name, 'node_map'))
    edge_map = _to_shared_mem(edge_map, _get_edata_path(graph_name, 'edge_map'))
    return meta, node_map, edge_map

def _get_shared_mem_metadata(graph_name):
    ''' Get the metadata of the graph from shared memory.

    The server serializes the metadata of a graph and store them in shared memory.
    The client needs to deserialize the data in shared memory and get the metadata
    of the graph.

    Parameters
    ----------
    graph_name : str
        The name of the graph. We can use the graph name to find the shared memory name.

    Returns
    -------
    (bool, int, int, Tensor, Tensor)
        The first element indicates whether it is range partitioning;
        the second element is the partition ID;
        the third element is the number of partitions;
        the fourth element is the tensor that stores the serialized result of node maps;
        the fifth element is the tensor that stores the serialized result of edge maps.
    '''
    # The metadata has 7 elements: is_range_part, num_nodes, num_edges, num_partitions, part_id,
    # the length of node map and the length of the edge map.
    shape = (7,)
    dtype = F.int64
    dtype = DTYPE_DICT[dtype]
    data = empty_shared_mem(_get_ndata_path(graph_name, 'meta'), False, shape, dtype)
    dlpack = data.to_dlpack()
    meta = F.asnumpy(F.zerocopy_from_dlpack(dlpack))
    is_range_part, _, _, num_partitions, part_id, node_map_len, edge_map_len = meta

    # Load node map
    data = empty_shared_mem(_get_ndata_path(graph_name, 'node_map'), False, (node_map_len,), dtype)
    dlpack = data.to_dlpack()
    node_map = F.zerocopy_from_dlpack(dlpack)

    # Load edge_map
    data = empty_shared_mem(_get_edata_path(graph_name, 'edge_map'), False, (edge_map_len,), dtype)
    dlpack = data.to_dlpack()
    edge_map = F.zerocopy_from_dlpack(dlpack)

    return is_range_part, part_id, num_partitions, node_map, edge_map


def get_shared_mem_partition_book(graph_name, graph_part):
    '''Get a graph partition book from shared memory.

    A graph partition book of a specific graph can be serialized to shared memory.
    We can reconstruct a graph partition book from shared memory.

    Parameters
    ----------
    graph_name : str
        The name of the graph.
    graph_part : DGLGraph
        The graph structure of a partition.

    Returns
    -------
    GraphPartitionBook
        A graph partition book for a particular partition.
    '''
    if not exist_shared_mem_array(_get_ndata_path(graph_name, 'meta')):
        return None
    is_range_part, part_id, num_parts, node_map_data, edge_map_data = \
            _get_shared_mem_metadata(graph_name)
    if is_range_part == 1:
        # node ID ranges and edge ID ranges are stored in the order of node type IDs
        # and edge type IDs.
        node_map = {}
        ntypes = {}
        # node_map_data and edge_map_data were serialized with pickle and converted into
        # a list of bytes and then stored in a numpy array before being placed in shared
        # memory. To deserialize, we need to reverse the process.
        node_map_data = pickle.loads(bytes(F.asnumpy(node_map_data).tolist()))
        for i, (ntype, nid_range) in enumerate(node_map_data):
            ntypes[ntype] = i
            node_map[ntype] = nid_range

        edge_map = {}
        etypes = {}
        edge_map_data = pickle.loads(bytes(F.asnumpy(edge_map_data).tolist()))
        for i, (etype, eid_range) in enumerate(edge_map_data):
            etypes[etype] = i
            edge_map[etype] = eid_range
        return RangePartitionBook(part_id, num_parts, node_map, edge_map, ntypes, etypes)
    else:
        return BasicPartitionBook(part_id, num_parts, node_map_data, edge_map_data, graph_part)

[docs]class GraphPartitionBook(ABC): """ The base class of the graph partition book. For distributed training, a graph is partitioned into multiple parts and is loaded in multiple machines. The partition book contains all necessary information to locate nodes and edges in the cluster. The partition book contains various partition information, including * the number of partitions, * the partition ID that a node or edge belongs to, * the node IDs and the edge IDs that a partition has. * the local IDs of nodes and edges in a partition. Currently, there are two classes that implement ``GraphPartitionBook``: ``BasicGraphPartitionBook`` and ``RangePartitionBook``. ``BasicGraphPartitionBook`` stores the mappings between every individual node/edge ID and partition ID on every machine, which usually consumes a lot of memory, while ``RangePartitionBook`` calculates the mapping between node/edge IDs and partition IDs based on some small metadata because nodes/edges have been relabeled to have IDs in the same partition fall in a contiguous ID range. ``RangePartitionBook`` is usually a preferred way to provide mappings between node/edge IDs and partition IDs. A graph partition book is constructed automatically when a graph is partitioned. When a graph partition is loaded, a graph partition book is loaded as well. Please see :py:meth:`~dgl.distributed.partition.partition_graph`, :py:meth:`~dgl.distributed.partition.load_partition` and :py:meth:`~dgl.distributed.partition.load_partition_book` for more details. """
[docs] def shared_memory(self, graph_name): """Move the partition book to shared memory. Parameters ---------- graph_name : str The graph name. This name will be used to read the partition book from shared memory in another process. """
[docs] def num_partitions(self): """Return the number of partitions. Returns ------- int number of partitions """
[docs] def metadata(self): """Return the partition meta data. The meta data includes: * The machine ID. * Number of nodes and edges of each partition. Examples -------- >>> print(g.get_partition_book().metadata()) >>> [{'machine_id' : 0, 'num_nodes' : 3000, 'num_edges' : 5000}, ... {'machine_id' : 1, 'num_nodes' : 2000, 'num_edges' : 4888}, ... ...] Returns ------- list[dict[str, any]] Meta data of each partition. """
[docs] def nid2partid(self, nids, ntype): """From global node IDs to partition IDs Parameters ---------- nids : tensor global node IDs ntype : str The node type Returns ------- tensor partition IDs """
[docs] def eid2partid(self, eids, etype): """From global edge IDs to partition IDs Parameters ---------- eids : tensor global edge IDs etype : str The edge type Returns ------- tensor partition IDs """
[docs] def partid2nids(self, partid, ntype): """From partition id to global node IDs Parameters ---------- partid : int partition id ntype : str The node type Returns ------- tensor node IDs """
[docs] def partid2eids(self, partid, etype): """From partition id to global edge IDs Parameters ---------- partid : int partition id etype : str The edge type Returns ------- tensor edge IDs """
[docs] def nid2localnid(self, nids, partid, ntype): """Get local node IDs within the given partition. Parameters ---------- nids : tensor global node IDs partid : int partition ID ntype : str The node type Returns ------- tensor local node IDs """
[docs] def eid2localeid(self, eids, partid, etype): """Get the local edge ids within the given partition. Parameters ---------- eids : tensor global edge IDs partid : int partition ID etype : str The edge type Returns ------- tensor local edge IDs """
@property def partid(self): """Get the current partition ID Return ------ int The partition ID of current machine """ @property def ntypes(self): """Get the list of node types """ @property def etypes(self): """Get the list of edge types """
[docs] def map_to_per_ntype(self, ids): """Map homogeneous node IDs to type-wise IDs and node types. Parameters ---------- ids : tensor Homogeneous node IDs. Returns ------- (tensor, tensor) node type IDs and type-wise node IDs. """
[docs] def map_to_per_etype(self, ids): """Map homogeneous edge IDs to type-wise IDs and edge types. Parameters ---------- ids : tensor Homogeneous edge IDs. Returns ------- (tensor, tensor) edge type IDs and type-wise edge IDs. """
[docs] def map_to_homo_nid(self, ids, ntype): """Map type-wise node IDs and type IDs to homogeneous node IDs. Parameters ---------- ids : tensor Type-wise node Ids ntype : str node type Returns ------- Tensor Homogeneous node IDs. """
[docs] def map_to_homo_eid(self, ids, etype): """Map type-wise edge IDs and type IDs to homogeneous edge IDs. Parameters ---------- ids : tensor Type-wise edge Ids etype : str edge type Returns ------- Tensor Homogeneous edge IDs. """
class BasicPartitionBook(GraphPartitionBook): """This provides the most flexible way to store parition information. The partition book maintains the mapping of every single node IDs and edge IDs to partition IDs. This is very flexible at the coast of large memory consumption. On a large graph, the mapping consumes significant memory and this partition book is not recommended. Parameters ---------- part_id : int partition ID of current partition book num_parts : int number of total partitions node_map : tensor global node ID mapping to partition ID edge_map : tensor global edge ID mapping to partition ID part_graph : DGLGraph The graph partition structure. """ def __init__(self, part_id, num_parts, node_map, edge_map, part_graph): assert part_id >= 0, 'part_id cannot be a negative number.' assert num_parts > 0, 'num_parts must be greater than zero.' self._part_id = int(part_id) self._num_partitions = int(num_parts) self._nid2partid = F.tensor(node_map) assert F.dtype(self._nid2partid) == F.int64, \ 'the node map must be stored in an integer array' self._eid2partid = F.tensor(edge_map) assert F.dtype(self._eid2partid) == F.int64, \ 'the edge map must be stored in an integer array' # Get meta data of the partition book. self._partition_meta_data = [] _, nid_count = np.unique(F.asnumpy(self._nid2partid), return_counts=True) _, eid_count = np.unique(F.asnumpy(self._eid2partid), return_counts=True) for partid in range(self._num_partitions): part_info = {} part_info['machine_id'] = partid part_info['num_nodes'] = int(nid_count[partid]) part_info['num_edges'] = int(eid_count[partid]) self._partition_meta_data.append(part_info) # Get partid2nids self._partid2nids = [] sorted_nid = F.tensor(np.argsort(F.asnumpy(self._nid2partid))) start = 0 for offset in nid_count: part_nids = sorted_nid[start:start+offset] start += offset self._partid2nids.append(part_nids) # Get partid2eids self._partid2eids = [] sorted_eid = F.tensor(np.argsort(F.asnumpy(self._eid2partid))) start = 0 for offset in eid_count: part_eids = sorted_eid[start:start+offset] start += offset self._partid2eids.append(part_eids) # Get nidg2l self._nidg2l = [None] * self._num_partitions global_id = part_graph.ndata[NID] max_global_id = np.amax(F.asnumpy(global_id)) # TODO(chao): support int32 index g2l = F.zeros((max_global_id+1), F.int64, F.context(global_id)) g2l = F.scatter_row(g2l, global_id, F.arange(0, len(global_id))) self._nidg2l[self._part_id] = g2l # Get eidg2l self._eidg2l = [None] * self._num_partitions global_id = part_graph.edata[EID] max_global_id = np.amax(F.asnumpy(global_id)) # TODO(chao): support int32 index g2l = F.zeros((max_global_id+1), F.int64, F.context(global_id)) g2l = F.scatter_row(g2l, global_id, F.arange(0, len(global_id))) self._eidg2l[self._part_id] = g2l # node size and edge size self._edge_size = len(self.partid2eids(self._part_id)) self._node_size = len(self.partid2nids(self._part_id)) def shared_memory(self, graph_name): """Move data to shared memory. """ self._meta, self._nid2partid, self._eid2partid = _move_metadata_to_shared_mem( graph_name, self._num_nodes(), self._num_edges(), self._part_id, self._num_partitions, self._nid2partid, self._eid2partid, False) def num_partitions(self): """Return the number of partitions. """ return self._num_partitions def metadata(self): """Return the partition meta data. """ return self._partition_meta_data def _num_nodes(self, ntype='_N'): """ The total number of nodes """ assert ntype == '_N', 'Base partition book only supports homogeneous graph.' return len(self._nid2partid) def _num_edges(self, etype='_E'): """ The total number of edges """ assert etype == '_E', 'Base partition book only supports homogeneous graph.' return len(self._eid2partid) def map_to_per_ntype(self, ids): """Map global homogeneous node IDs to node type IDs. Returns type_ids, per_type_ids """ return F.zeros((len(ids),), F.int32, F.cpu()), ids def map_to_per_etype(self, ids): """Map global homogeneous edge IDs to edge type IDs. Returns type_ids, per_type_ids """ return F.zeros((len(ids),), F.int32, F.cpu()), ids def map_to_homo_nid(self, ids, ntype): """Map per-node-type IDs to global node IDs in the homogeneous format. """ assert ntype == '_N', 'Base partition book only supports homogeneous graph.' return ids def map_to_homo_eid(self, ids, etype): """Map per-edge-type IDs to global edge IDs in the homoenegeous format. """ assert etype == '_E', 'Base partition book only supports homogeneous graph.' return ids def nid2partid(self, nids, ntype='_N'): """From global node IDs to partition IDs """ assert ntype == '_N', 'Base partition book only supports homogeneous graph.' return F.gather_row(self._nid2partid, nids) def eid2partid(self, eids, etype='_E'): """From global edge IDs to partition IDs """ assert etype == '_E', 'Base partition book only supports homogeneous graph.' return F.gather_row(self._eid2partid, eids) def partid2nids(self, partid, ntype='_N'): """From partition id to global node IDs """ assert ntype == '_N', 'Base partition book only supports homogeneous graph.' return self._partid2nids[partid] def partid2eids(self, partid, etype='_E'): """From partition id to global edge IDs """ assert etype == '_E', 'Base partition book only supports homogeneous graph.' return self._partid2eids[partid] def nid2localnid(self, nids, partid, ntype='_N'): """Get local node IDs within the given partition. """ assert ntype == '_N', 'Base partition book only supports homogeneous graph.' if partid != self._part_id: raise RuntimeError('Now GraphPartitionBook does not support \ getting remote tensor of nid2localnid.') return F.gather_row(self._nidg2l[partid], nids) def eid2localeid(self, eids, partid, etype='_E'): """Get the local edge ids within the given partition. """ assert etype == '_E', 'Base partition book only supports homogeneous graph.' if partid != self._part_id: raise RuntimeError('Now GraphPartitionBook does not support \ getting remote tensor of eid2localeid.') return F.gather_row(self._eidg2l[partid], eids) @property def partid(self): """Get the current partition ID """ return self._part_id @property def ntypes(self): """Get the list of node types """ return ['_N'] @property def etypes(self): """Get the list of edge types """ return ['_E'] class RangePartitionBook(GraphPartitionBook): """This partition book supports more efficient storage of partition information. This partition book is used if the nodes and edges of a graph partition are assigned with contiguous IDs. It uses very small amount of memory to store the partition information. Parameters ---------- part_id : int partition ID of current partition book num_parts : int number of total partitions node_map : dict[str, Tensor] Global node ID ranges within partitions for each node type. The key is the node type name in string. The value is a tensor of shape :math:`(K, 2)`, where :math:`K` is the number of partitions. Each row has two integers: the starting and the ending IDs for a particular node type in a partition. For example, all nodes of type ``"T"`` in partition ``i`` has ID range ``node_map["T"][i][0]`` to ``node_map["T"][i][1]``. edge_map : dict[str, Tensor] Global edge ID ranges within partitions for each edge type. The key is the edge type name in string. The value is a tensor of shape :math:`(K, 2)`, where :math:`K` is the number of partitions. Each row has two integers: the starting and the ending IDs for a particular edge type in a partition. For example, all edges of type ``"T"`` in partition ``i`` has ID range ``edge_map["T"][i][0]`` to ``edge_map["T"][i][1]``. ntypes : dict[str, int] map ntype strings to ntype IDs. etypes : dict[str, int] map etype strings to etype IDs. """ def __init__(self, part_id, num_parts, node_map, edge_map, ntypes, etypes): assert part_id >= 0, 'part_id cannot be a negative number.' assert num_parts > 0, 'num_parts must be greater than zero.' self._partid = part_id self._num_partitions = num_parts self._ntypes = [None] * len(ntypes) self._etypes = [None] * len(etypes) for ntype in ntypes: ntype_id = ntypes[ntype] self._ntypes[ntype_id] = ntype assert all([ntype is not None for ntype in self._ntypes]), \ "The node types have invalid IDs." for etype in etypes: etype_id = etypes[etype] self._etypes[etype_id] = etype assert all([etype is not None for etype in self._etypes]), \ "The edge types have invalid IDs." # This stores the node ID ranges for each node type in each partition. # The key is the node type, the value is a NumPy matrix with two columns, in which # each row indicates the start and the end of the node ID range in a partition. # The node IDs are global node IDs in the homogeneous representation. self._typed_nid_range = {} # This stores the node ID map for per-node-type IDs in each partition. # The key is the node type, the value is a NumPy vector which indicates # the last node ID in a partition. self._typed_max_node_ids = {} max_node_map = np.zeros((num_parts,), dtype=np.int64) for key in node_map: if not isinstance(node_map[key], np.ndarray): node_map[key] = F.asnumpy(node_map[key]) assert node_map[key].shape == (num_parts, 2) self._typed_nid_range[key] = node_map[key] # This is used for per-node-type lookup. self._typed_max_node_ids[key] = np.cumsum(self._typed_nid_range[key][:, 1] - self._typed_nid_range[key][:, 0]) # This is used for homogeneous node ID lookup. max_node_map = np.maximum(self._typed_nid_range[key][:, 1], max_node_map) # This is a vector that indicates the last node ID in each partition. # The ID is the global ID in the homogeneous representation. self._max_node_ids = max_node_map # Similar to _typed_nid_range. self._typed_eid_range = {} # similar to _typed_max_node_ids. self._typed_max_edge_ids = {} max_edge_map = np.zeros((num_parts,), dtype=np.int64) for key in edge_map: if not isinstance(edge_map[key], np.ndarray): edge_map[key] = F.asnumpy(edge_map[key]) assert edge_map[key].shape == (num_parts, 2) self._typed_eid_range[key] = edge_map[key] # This is used for per-edge-type lookup. self._typed_max_edge_ids[key] = np.cumsum(self._typed_eid_range[key][:, 1] - self._typed_eid_range[key][:, 0]) # This is used for homogeneous edge ID lookup. max_edge_map = np.maximum(self._typed_eid_range[key][:, 1], max_edge_map) # Similar to _max_node_ids self._max_edge_ids = max_edge_map # These two are map functions that map node/edge IDs to node/edge type IDs. self._nid_map = IdMap(self._typed_nid_range) self._eid_map = IdMap(self._typed_eid_range) # Get meta data of the partition book self._partition_meta_data = [] for partid in range(self._num_partitions): nrange_start = max_node_map[partid - 1] if partid > 0 else 0 nrange_end = max_node_map[partid] num_nodes = nrange_end - nrange_start erange_start = max_edge_map[partid - 1] if partid > 0 else 0 erange_end = max_edge_map[partid] num_edges = erange_end - erange_start part_info = {} part_info['machine_id'] = partid part_info['num_nodes'] = int(num_nodes) part_info['num_edges'] = int(num_edges) self._partition_meta_data.append(part_info) def shared_memory(self, graph_name): """Move data to shared memory. """ # we need to store the nid ranges and eid ranges of different types in the order defined # by type IDs. nid_range = [None] * len(self.ntypes) for i, ntype in enumerate(self.ntypes): nid_range[i] = (ntype, self._typed_nid_range[ntype]) nid_range_pickle = pickle.dumps(nid_range) nid_range_pickle = [e for e in nid_range_pickle] eid_range = [None] * len(self.etypes) for i, etype in enumerate(self.etypes): eid_range[i] = (etype, self._typed_eid_range[etype]) eid_range_pickle = pickle.dumps(eid_range) eid_range_pickle = [e for e in eid_range_pickle] self._meta = _move_metadata_to_shared_mem(graph_name, 0, # We don't need to provide the number of nodes 0, # We don't need to provide the number of edges self._partid, self._num_partitions, F.tensor(nid_range_pickle), F.tensor(eid_range_pickle), True) def num_partitions(self): """Return the number of partitions. """ return self._num_partitions def _num_nodes(self, ntype='_N'): """ The total number of nodes """ if ntype == '_N': return int(self._max_node_ids[-1]) else: return int(self._typed_max_node_ids[ntype][-1]) def _num_edges(self, etype='_E'): """ The total number of edges """ if etype == '_E': return int(self._max_edge_ids[-1]) else: return int(self._typed_max_edge_ids[etype][-1]) def metadata(self): """Return the partition meta data. """ return self._partition_meta_data def map_to_per_ntype(self, ids): """Map global homogeneous node IDs to node type IDs. Returns type_ids, per_type_ids """ return self._nid_map(ids) def map_to_per_etype(self, ids): """Map global homogeneous edge IDs to edge type IDs. Returns type_ids, per_type_ids """ return self._eid_map(ids) def map_to_homo_nid(self, ids, ntype): """Map per-node-type IDs to global node IDs in the homogeneous format. """ ids = utils.toindex(ids).tousertensor() partids = self.nid2partid(ids, ntype) end_diff = F.tensor(self._typed_max_node_ids[ntype])[partids] - ids return F.tensor(self._typed_nid_range[ntype][:, 1])[partids] - end_diff def map_to_homo_eid(self, ids, etype): """Map per-edge-type IDs to global edge IDs in the homoenegeous format. """ ids = utils.toindex(ids).tousertensor() partids = self.eid2partid(ids, etype) end_diff = F.tensor(self._typed_max_edge_ids[etype][partids]) - ids return F.tensor(self._typed_eid_range[etype][:, 1])[partids] - end_diff def nid2partid(self, nids, ntype='_N'): """From global node IDs to partition IDs """ nids = utils.toindex(nids) if ntype == '_N': ret = np.searchsorted(self._max_node_ids, nids.tonumpy(), side='right') else: ret = np.searchsorted(self._typed_max_node_ids[ntype], nids.tonumpy(), side='right') ret = utils.toindex(ret) return ret.tousertensor() def eid2partid(self, eids, etype='_E'): """From global edge IDs to partition IDs """ eids = utils.toindex(eids) if etype == '_E': ret = np.searchsorted(self._max_edge_ids, eids.tonumpy(), side='right') else: ret = np.searchsorted(self._typed_max_edge_ids[etype], eids.tonumpy(), side='right') ret = utils.toindex(ret) return ret.tousertensor() def partid2nids(self, partid, ntype='_N'): """From partition ID to global node IDs """ # TODO do we need to cache it? if ntype == '_N': start = self._max_node_ids[partid - 1] if partid > 0 else 0 end = self._max_node_ids[partid] return F.arange(start, end) else: start = self._typed_max_node_ids[ntype][partid - 1] if partid > 0 else 0 end = self._typed_max_node_ids[ntype][partid] return F.arange(start, end) def partid2eids(self, partid, etype='_E'): """From partition ID to global edge IDs """ # TODO do we need to cache it? if etype == '_E': start = self._max_edge_ids[partid - 1] if partid > 0 else 0 end = self._max_edge_ids[partid] return F.arange(start, end) else: start = self._typed_max_edge_ids[etype][partid - 1] if partid > 0 else 0 end = self._typed_max_edge_ids[etype][partid] return F.arange(start, end) def nid2localnid(self, nids, partid, ntype='_N'): """Get local node IDs within the given partition. """ if partid != self._partid: raise RuntimeError('Now RangePartitionBook does not support \ getting remote tensor of nid2localnid.') nids = utils.toindex(nids) nids = nids.tousertensor() if ntype == '_N': start = self._max_node_ids[partid - 1] if partid > 0 else 0 else: start = self._typed_max_node_ids[ntype][partid - 1] if partid > 0 else 0 return nids - int(start) def eid2localeid(self, eids, partid, etype='_E'): """Get the local edge IDs within the given partition. """ if partid != self._partid: raise RuntimeError('Now RangePartitionBook does not support \ getting remote tensor of eid2localeid.') eids = utils.toindex(eids) eids = eids.tousertensor() if etype == '_E': start = self._max_edge_ids[partid - 1] if partid > 0 else 0 else: start = self._typed_max_edge_ids[etype][partid - 1] if partid > 0 else 0 return eids - int(start) @property def partid(self): """Get the current partition ID. """ return self._partid @property def ntypes(self): """Get the list of node types """ return self._ntypes @property def etypes(self): """Get the list of edge types """ return self._etypes NODE_PART_POLICY = 'node' EDGE_PART_POLICY = 'edge'
[docs]class PartitionPolicy(object): """This defines a partition policy for a distributed tensor or distributed embedding. When DGL shards tensors and stores them in a cluster of machines, it requires partition policies that map rows of the tensors to machines in the cluster. Although an arbitrary partition policy can be defined, DGL currently supports two partition policies for mapping nodes and edges to machines. To define a partition policy from a graph partition book, users need to specify the policy name ('node' or 'edge'). Parameters ---------- policy_str : str Partition policy name, e.g., 'edge:_E' or 'node:_N'. partition_book : GraphPartitionBook A graph partition book """ def __init__(self, policy_str, partition_book): splits = policy_str.split(':') if len(splits) == 1: assert policy_str in (EDGE_PART_POLICY, NODE_PART_POLICY), \ 'policy_str must contain \'edge\' or \'node\'.' if NODE_PART_POLICY == policy_str: policy_str = NODE_PART_POLICY + ":_N" else: policy_str = EDGE_PART_POLICY + ":_E" self._policy_str = policy_str self._part_id = partition_book.partid self._partition_book = partition_book @property def policy_str(self): """Get the policy name Returns ------- str The name of the partition policy. """ return self._policy_str @property def part_id(self): """Get partition ID Returns ------- int The partition ID """ return self._part_id @property def partition_book(self): """Get partition book Returns ------- GraphPartitionBook The graph partition book """ return self._partition_book def get_data_name(self, name): """Get HeteroDataName """ is_node = NODE_PART_POLICY in self._policy_str return HeteroDataName(is_node, self._policy_str[5:], name)
[docs] def to_local(self, id_tensor): """Mapping global ID to local ID. Parameters ---------- id_tensor : tensor Gloabl ID tensor Return ------ tensor local ID tensor """ if EDGE_PART_POLICY in self._policy_str: return self._partition_book.eid2localeid(id_tensor, self._part_id, self._policy_str[5:]) elif NODE_PART_POLICY in self._policy_str: return self._partition_book.nid2localnid(id_tensor, self._part_id, self._policy_str[5:]) else: raise RuntimeError('Cannot support policy: %s ' % self._policy_str)
[docs] def to_partid(self, id_tensor): """Mapping global ID to partition ID. Parameters ---------- id_tensor : tensor Global ID tensor Return ------ tensor partition ID """ if EDGE_PART_POLICY in self._policy_str: return self._partition_book.eid2partid(id_tensor, self._policy_str[5:]) elif NODE_PART_POLICY in self._policy_str: return self._partition_book.nid2partid(id_tensor, self._policy_str[5:]) else: raise RuntimeError('Cannot support policy: %s ' % self._policy_str)
[docs] def get_part_size(self): """Get data size of current partition. Returns ------- int data size """ if EDGE_PART_POLICY in self._policy_str: return len(self._partition_book.partid2eids(self._part_id, self._policy_str[5:])) elif NODE_PART_POLICY in self._policy_str: return len(self._partition_book.partid2nids(self._part_id, self._policy_str[5:])) else: raise RuntimeError('Cannot support policy: %s ' % self._policy_str)
[docs] def get_size(self): """Get the full size of the data. Returns ------- int data size """ if EDGE_PART_POLICY in self._policy_str: return self._partition_book._num_edges(self._policy_str[5:]) elif NODE_PART_POLICY in self._policy_str: return self._partition_book._num_nodes(self._policy_str[5:]) else: raise RuntimeError('Cannot support policy: %s ' % self._policy_str)
class NodePartitionPolicy(PartitionPolicy): '''Partition policy for nodes. ''' def __init__(self, partition_book, ntype='_N'): super(NodePartitionPolicy, self).__init__(NODE_PART_POLICY + ':' + ntype, partition_book) class EdgePartitionPolicy(PartitionPolicy): '''Partition policy for edges. ''' def __init__(self, partition_book, etype='_E'): super(EdgePartitionPolicy, self).__init__(EDGE_PART_POLICY + ':' + etype, partition_book) class HeteroDataName(object): ''' The data name in a heterogeneous graph. A unique data name has three components: * indicate it's node data or edge data. * indicate the node/edge type. * the name of the data. Parameters ---------- is_node : bool Indicate whether it's node data or edge data. entity_type : str The type of the node/edge. data_name : str The name of the data. ''' def __init__(self, is_node, entity_type, data_name): self.policy_str = NODE_PART_POLICY if is_node else EDGE_PART_POLICY self.policy_str = self.policy_str + ':' + entity_type self.data_name = data_name def is_node(self): ''' Is this the name of node data ''' return NODE_PART_POLICY in self.policy_str def is_edge(self): ''' Is this the name of edge data ''' return EDGE_PART_POLICY in self.policy_str def get_type(self): ''' The type of the node/edge. This is only meaningful in a heterogeneous graph. In homogeneous graph, type is '_N' for a node and '_E' for an edge. ''' return self.policy_str[5:] def get_name(self): ''' The name of the data. ''' return self.data_name def __str__(self): ''' The full name of the data. The full name is used as the key in the KVStore. ''' return self.policy_str + ':' + self.data_name def parse_hetero_data_name(name): '''Parse data name and create HeteroDataName. The data name has a specialized format. We can parse the name to determine if it's node data or edge data, node/edge type and its actual name. The data name has three fields and they are separated by ":". Parameters ---------- name : str The data name Returns ------- HeteroDataName ''' names = name.split(':') assert len(names) == 3, '{} is not a valid heterograph data name'.format(name) assert names[0] in (NODE_PART_POLICY, EDGE_PART_POLICY), \ '{} is not a valid heterograph data name'.format(name) return HeteroDataName(names[0] == NODE_PART_POLICY, names[1], names[2])