Source code for dgl.distributed.partition

"""Functions for partitions. """

import json
import os
import time
import numpy as np

from .. import backend as F
from ..base import NID, EID, NTYPE, ETYPE, DGLError
from ..convert import to_homogeneous
from ..random import choice as random_choice
from ..transforms import sort_csr_by_tag, sort_csc_by_tag
from ..data.utils import load_graphs, save_graphs, load_tensors, save_tensors
from ..partition import (
    metis_partition_assignment,
    partition_graph_with_halo,
    get_peak_mem,
)
from .constants import DEFAULT_ETYPE, DEFAULT_NTYPE
from .graph_partition_book import (
    RangePartitionBook,
    _etype_tuple_to_str,
    _etype_str_to_tuple,
)

RESERVED_FIELD_DTYPE = {
    'inner_node': F.uint8,    # A flag indicates whether the node is inside a partition.
    'inner_edge': F.uint8,    # A flag indicates whether the edge is inside a partition.
    NID: F.int64,
    EID: F.int64,
    NTYPE: F.int16,
    # `sort_csr_by_tag` and `sort_csc_by_tag` works on int32/64 only.
    ETYPE: F.int32
    }

def _format_part_metadata(part_metadata, formatter):
    '''Format etypes with specified formatter.
    '''
    for key in ['edge_map', 'etypes']:
        if key not in part_metadata:
            continue
        orig_data = part_metadata[key]
        if not isinstance(orig_data, dict):
            continue
        new_data = {}
        for etype, data in orig_data.items():
            etype = formatter(etype)
            new_data[etype] = data
        part_metadata[key] = new_data
    return part_metadata

def _load_part_config(part_config):
    '''Load part config and format.
    '''
    try:
        with open(part_config) as f:
            part_metadata = _format_part_metadata(json.load(f),
                _etype_str_to_tuple)
    except AssertionError as e:
        raise DGLError(f"Failed to load partition config due to {e}. "
            "Probably caused by outdated config. If so, please refer to "
            "https://github.com/dmlc/dgl/tree/master/tools#change-edge-"
            "type-to-canonical-edge-type-for-partition-configuration-json")
    return part_metadata

def _dump_part_config(part_config, part_metadata):
    '''Format and dump part config.
    '''
    part_metadata = _format_part_metadata(part_metadata, _etype_tuple_to_str)
    with open(part_config, 'w') as outfile:
        json.dump(part_metadata, outfile, sort_keys=True, indent=4)

def _save_graphs(filename, g_list, formats=None, sort_etypes=False):
    '''Preprocess partitions before saving:
    1. format data types.
    2. sort csc/csr by tag.
    '''
    for g in g_list:
        for k, dtype in RESERVED_FIELD_DTYPE.items():
            if k in g.ndata:
                g.ndata[k] = F.astype(g.ndata[k], dtype)
            if k in g.edata:
                g.edata[k] = F.astype(g.edata[k], dtype)
    for g in g_list:
        if (not sort_etypes) or (formats is None):
            continue
        if 'csr' in formats:
            g = sort_csr_by_tag(g, tag=g.edata[ETYPE], tag_type='edge')
        if 'csc' in formats:
            g = sort_csc_by_tag(g, tag=g.edata[ETYPE], tag_type='edge')
    save_graphs(filename , g_list, formats=formats)

def _get_inner_node_mask(graph, ntype_id):
    if NTYPE in graph.ndata:
        dtype = F.dtype(graph.ndata['inner_node'])
        return graph.ndata['inner_node'] * F.astype(graph.ndata[NTYPE] == ntype_id, dtype) == 1
    else:
        return graph.ndata['inner_node'] == 1

def _get_inner_edge_mask(graph, etype_id):
    if ETYPE in graph.edata:
        dtype = F.dtype(graph.edata['inner_edge'])
        return graph.edata['inner_edge'] * F.astype(graph.edata[ETYPE] == etype_id, dtype) == 1
    else:
        return graph.edata['inner_edge'] == 1

def _get_part_ranges(id_ranges):
    res = {}
    for key in id_ranges:
        # Normally, each element has two values that represent the starting ID and the ending ID
        # of the ID range in a partition.
        # If not, the data is probably still in the old format, in which only the ending ID is
        # stored. We need to convert it to the format we expect.
        if not isinstance(id_ranges[key][0], list):
            start = 0
            for i, end in enumerate(id_ranges[key]):
                id_ranges[key][i] = [start, end]
                start = end
        res[key] = np.concatenate([np.array(l) for l in id_ranges[key]]).reshape(-1, 2)
    return res

[docs]def load_partition(part_config, part_id, load_feats=True): ''' Load data of a partition from the data path. A partition data includes a graph structure of the partition, a dict of node tensors, a dict of edge tensors and some metadata. The partition may contain the HALO nodes, which are the nodes replicated from other partitions. However, the dict of node tensors only contains the node data that belongs to the local partition. Similarly, edge tensors only contains the edge data that belongs to the local partition. The metadata include the information of the global graph (not the local partition), which includes the number of nodes, the number of edges as well as the node assignment of the global graph. The function currently loads data through the local filesystem interface. Parameters ---------- part_config : str The path of the partition config file. part_id : int The partition ID. load_feats : bool, optional Whether to load node/edge feats. If False, the returned node/edge feature dictionaries will be empty. Default: True. Returns ------- DGLGraph The graph partition structure. Dict[str, Tensor] Node features. Dict[(str, str, str), Tensor] Edge features. GraphPartitionBook The graph partition information. str The graph name List[str] The node types List[(str, str, str)] The edge types ''' config_path = os.path.dirname(part_config) relative_to_config = lambda path: os.path.join(config_path, path) with open(part_config) as conf_f: part_metadata = json.load(conf_f) assert 'part-{}'.format(part_id) in part_metadata, "part-{} does not exist".format(part_id) part_files = part_metadata['part-{}'.format(part_id)] assert 'part_graph' in part_files, "the partition does not contain graph structure." graph = load_graphs(relative_to_config(part_files['part_graph']))[0][0] assert NID in graph.ndata, "the partition graph should contain node mapping to global node ID" assert EID in graph.edata, "the partition graph should contain edge mapping to global edge ID" gpb, graph_name, ntypes, etypes = load_partition_book(part_config, part_id) ntypes_list = list(ntypes.keys()) etypes_list = list(etypes.keys()) if 'DGL_DIST_DEBUG' in os.environ: for ntype in ntypes: ntype_id = ntypes[ntype] # graph.ndata[NID] are global homogeneous node IDs. nids = F.boolean_mask(graph.ndata[NID], _get_inner_node_mask(graph, ntype_id)) partids1 = gpb.nid2partid(nids) _, per_type_nids = gpb.map_to_per_ntype(nids) partids2 = gpb.nid2partid(per_type_nids, ntype) assert np.all(F.asnumpy(partids1 == part_id)), \ 'Unexpected partition IDs are found in the loaded partition ' \ 'while querying via global homogeneous node IDs.' assert np.all(F.asnumpy(partids2 == part_id)), \ 'Unexpected partition IDs are found in the loaded partition ' \ 'while querying via type-wise node IDs.' for etype in etypes: etype_id = etypes[etype] # graph.edata[EID] are global homogeneous edge IDs. eids = F.boolean_mask(graph.edata[EID], _get_inner_edge_mask(graph, etype_id)) partids1 = gpb.eid2partid(eids) _, per_type_eids = gpb.map_to_per_etype(eids) partids2 = gpb.eid2partid(per_type_eids, etype) assert np.all(F.asnumpy(partids1 == part_id)), \ 'Unexpected partition IDs are found in the loaded partition ' \ 'while querying via global homogeneous edge IDs.' assert np.all(F.asnumpy(partids2 == part_id)), \ 'Unexpected partition IDs are found in the loaded partition ' \ 'while querying via type-wise edge IDs.' node_feats = {} edge_feats = {} if load_feats: node_feats, edge_feats = load_partition_feats(part_config, part_id) return graph, node_feats, edge_feats, gpb, graph_name, ntypes_list, etypes_list
[docs]def load_partition_feats(part_config, part_id, load_nodes=True, load_edges=True): '''Load node/edge feature data from a partition. Parameters ---------- part_config : str The path of the partition config file. part_id : int The partition ID. load_nodes : bool, optional Whether to load node features. If ``False``, ``None`` is returned. load_edges : bool, optional Whether to load edge features. If ``False``, ``None`` is returned. Returns ------- Dict[str, Tensor] or None Node features. Dict[str, Tensor] or None Edge features. ''' config_path = os.path.dirname(part_config) relative_to_config = lambda path: os.path.join(config_path, path) with open(part_config) as conf_f: part_metadata = json.load(conf_f) assert 'part-{}'.format(part_id) in part_metadata, "part-{} does not exist".format(part_id) part_files = part_metadata['part-{}'.format(part_id)] assert 'node_feats' in part_files, "the partition does not contain node features." assert 'edge_feats' in part_files, "the partition does not contain edge feature." node_feats = None if load_nodes: node_feats = load_tensors(relative_to_config(part_files['node_feats'])) edge_feats = None if load_edges: edge_feats = load_tensors(relative_to_config(part_files['edge_feats'])) # In the old format, the feature name doesn't contain node/edge type. # For compatibility, let's add node/edge types to the feature names. if node_feats is not None: new_feats = {} for name in node_feats: feat = node_feats[name] if name.find('/') == -1: name = DEFAULT_NTYPE + '/' + name new_feats[name] = feat node_feats = new_feats if edge_feats is not None: new_feats = {} for name in edge_feats: feat = edge_feats[name] if name.find('/') == -1: name = _etype_tuple_to_str(DEFAULT_ETYPE) + '/' + name new_feats[name] = feat edge_feats = new_feats return node_feats, edge_feats
[docs]def load_partition_book(part_config, part_id): '''Load a graph partition book from the partition config file. Parameters ---------- part_config : str The path of the partition config file. part_id : int The partition ID. Returns ------- GraphPartitionBook The global partition information. str The graph name dict The node types dict The edge types ''' part_metadata = _load_part_config(part_config) assert 'num_parts' in part_metadata, 'num_parts does not exist.' assert part_metadata['num_parts'] > part_id, \ 'part {} is out of range (#parts: {})'.format(part_id, part_metadata['num_parts']) num_parts = part_metadata['num_parts'] assert 'num_nodes' in part_metadata, "cannot get the number of nodes of the global graph." assert 'num_edges' in part_metadata, "cannot get the number of edges of the global graph." assert 'node_map' in part_metadata, "cannot get the node map." assert 'edge_map' in part_metadata, "cannot get the edge map." assert 'graph_name' in part_metadata, "cannot get the graph name" # If this is a range partitioning, node_map actually stores a list, whose elements # indicate the boundary of range partitioning. Otherwise, node_map stores a filename # that contains node map in a NumPy array. node_map = part_metadata['node_map'] edge_map = part_metadata['edge_map'] if isinstance(node_map, dict): for key in node_map: is_range_part = isinstance(node_map[key], list) break elif isinstance(node_map, list): is_range_part = True node_map = {DEFAULT_NTYPE: node_map} else: is_range_part = False if isinstance(edge_map, list): edge_map = {DEFAULT_ETYPE: edge_map} ntypes = {DEFAULT_NTYPE: 0} etypes = {DEFAULT_ETYPE: 0} if 'ntypes' in part_metadata: ntypes = part_metadata['ntypes'] if 'etypes' in part_metadata: etypes = part_metadata['etypes'] if isinstance(node_map, dict): for key in node_map: assert key in ntypes, 'The node type {} is invalid'.format(key) if isinstance(edge_map, dict): for key in edge_map: assert key in etypes, 'The edge type {} is invalid'.format(key) if not is_range_part: raise TypeError("Only RangePartitionBook is supported currently.") node_map = _get_part_ranges(node_map) edge_map = _get_part_ranges(edge_map) return RangePartitionBook(part_id, num_parts, node_map, edge_map, ntypes, etypes), \ part_metadata['graph_name'], ntypes, etypes
def _get_orig_ids(g, sim_g, orig_nids, orig_eids): '''Convert/construct the original node IDs and edge IDs. It handles multiple cases: * If the graph has been reshuffled and it's a homogeneous graph, we just return the original node IDs and edge IDs in the inputs. * If the graph has been reshuffled and it's a heterogeneous graph, we need to split the original node IDs and edge IDs in the inputs based on the node types and edge types. * If the graph is not shuffled, the original node IDs and edge IDs don't change. Parameters ---------- g : DGLGraph The input graph for partitioning. sim_g : DGLGraph The homogeneous version of the input graph. orig_nids : tensor or None The original node IDs after the input graph is reshuffled. orig_eids : tensor or None The original edge IDs after the input graph is reshuffled. Returns ------- tensor or dict of tensors, tensor or dict of tensors ''' is_hetero = not g.is_homogeneous if is_hetero: # Get the type IDs orig_ntype = F.gather_row(sim_g.ndata[NTYPE], orig_nids) orig_etype = F.gather_row(sim_g.edata[ETYPE], orig_eids) # Mapping between shuffled global IDs to original per-type IDs orig_nids = F.gather_row(sim_g.ndata[NID], orig_nids) orig_eids = F.gather_row(sim_g.edata[EID], orig_eids) orig_nids = {ntype: F.boolean_mask(orig_nids, orig_ntype == g.get_ntype_id(ntype)) \ for ntype in g.ntypes} orig_eids = {etype: F.boolean_mask(orig_eids, orig_etype == g.get_etype_id(etype)) \ for etype in g.canonical_etypes} return orig_nids, orig_eids def _set_trainer_ids(g, sim_g, node_parts): '''Set the trainer IDs for each node and edge on the input graph. The trainer IDs will be stored as node data and edge data in the input graph. Parameters ---------- g : DGLGraph The input graph for partitioning. sim_g : DGLGraph The homogeneous version of the input graph. node_parts : tensor The node partition ID for each node in `sim_g`. ''' if g.is_homogeneous: g.ndata['trainer_id'] = node_parts # An edge is assigned to a partition based on its destination node. g.edata['trainer_id'] = F.gather_row(node_parts, g.edges()[1]) else: for ntype_id, ntype in enumerate(g.ntypes): type_idx = sim_g.ndata[NTYPE] == ntype_id orig_nid = F.boolean_mask(sim_g.ndata[NID], type_idx) trainer_id = F.zeros((len(orig_nid),), F.dtype(node_parts), F.cpu()) F.scatter_row_inplace(trainer_id, orig_nid, F.boolean_mask(node_parts, type_idx)) g.nodes[ntype].data['trainer_id'] = trainer_id for c_etype in g.canonical_etypes: # An edge is assigned to a partition based on its destination node. _, _, dst_type = c_etype trainer_id = F.gather_row(g.nodes[dst_type].data['trainer_id'], g.edges(etype=c_etype)[1]) g.edges[c_etype].data['trainer_id'] = trainer_id
[docs]def partition_graph(g, graph_name, num_parts, out_path, num_hops=1, part_method="metis", balance_ntypes=None, balance_edges=False, return_mapping=False, num_trainers_per_machine=1, objtype='cut', graph_formats=None): ''' Partition a graph for distributed training and store the partitions on files. The partitioning occurs in three steps: 1) run a partition algorithm (e.g., Metis) to assign nodes to partitions; 2) construct partition graph structure based on the node assignment; 3) split the node features and edge features based on the partition result. When a graph is partitioned, each partition can contain *HALO* nodes, which are assigned to other partitions but are included in this partition for efficiency purpose. In this document, *local nodes/edges* refers to the nodes and edges that truly belong to a partition. The rest are "HALO nodes/edges". The partitioned data is stored into multiple files organized as follows: .. code-block:: none data_root_dir/ |-- graph_name.json # partition configuration file in JSON |-- node_map.npy # partition id of each node stored in a numpy array (optional) |-- edge_map.npy # partition id of each edge stored in a numpy array (optional) |-- part0/ # data for partition 0 |-- node_feats.dgl # node features stored in binary format |-- edge_feats.dgl # edge features stored in binary format |-- graph.dgl # graph structure of this partition stored in binary format |-- part1/ # data for partition 1 |-- node_feats.dgl |-- edge_feats.dgl |-- graph.dgl First, the metadata of the original graph and the partitioning is stored in a JSON file named after ``graph_name``. This JSON file contains the information of the original graph as well as the path of the files that store each partition. Below show an example. .. code-block:: none { "graph_name" : "test", "part_method" : "metis", "num_parts" : 2, "halo_hops" : 1, "node_map": { "_N": [ [ 0, 1261310 ], [ 1261310, 2449029 ] ] }, "edge_map": { "_N:_E:_N": [ [ 0, 62539528 ], [ 62539528, 123718280 ] ] }, "etypes": { "_N:_E:_N": 0 }, "ntypes": { "_N": 0 }, "num_nodes" : 1000000, "num_edges" : 52000000, "part-0" : { "node_feats" : "data_root_dir/part0/node_feats.dgl", "edge_feats" : "data_root_dir/part0/edge_feats.dgl", "part_graph" : "data_root_dir/part0/graph.dgl", }, "part-1" : { "node_feats" : "data_root_dir/part1/node_feats.dgl", "edge_feats" : "data_root_dir/part1/edge_feats.dgl", "part_graph" : "data_root_dir/part1/graph.dgl", }, } Here are the definition of the fields in the partition configuration file: * ``graph_name`` is the name of the graph given by a user. * ``part_method`` is the method used to assign nodes to partitions. Currently, it supports "random" and "metis". * ``num_parts`` is the number of partitions. * ``halo_hops`` is the number of hops of nodes we include in a partition as HALO nodes. * ``node_map`` is the node assignment map, which tells the partition ID a node is assigned to. The format of ``node_map`` is described below. * ``edge_map`` is the edge assignment map, which tells the partition ID an edge is assigned to. * ``num_nodes`` is the number of nodes in the global graph. * ``num_edges`` is the number of edges in the global graph. * `part-*` stores the data of a partition. As node/edge IDs are reshuffled, ``node_map`` and ``edge_map`` contains the information for mapping between global node/edge IDs to partition-local node/edge IDs. For heterogeneous graphs, the information in ``node_map`` and ``edge_map`` can also be used to compute node types and edge types. The format of the data in ``node_map`` and ``edge_map`` is as follows: .. code-block:: none { "node_type": [ [ part1_start, part1_end ], [ part2_start, part2_end ], ... ], ... }, Essentially, ``node_map`` and ``edge_map`` are dictionaries. The keys are node etypes and canonical edge types respectively. The values are lists of pairs containing the start and end of the ID range for the corresponding types in a partition. The length of the list is the number of partitions; each element in the list is a tuple that stores the start and the end of an ID range for a particular node/edge type in the partition. The graph structure of a partition is stored in a file with the DGLGraph format. Nodes in each partition is *relabeled* to always start with zero. We call the node ID in the original graph, *global ID*, while the relabeled ID in each partition, *local ID*. Each partition graph has an integer node data tensor stored under name `dgl.NID` and each value is the node's global ID. Similarly, edges are relabeled too and the mapping from local ID to global ID is stored as an integer edge data tensor under name `dgl.EID`. For a heterogeneous graph, the DGLGraph also contains a node data `dgl.NTYPE` for node type and an edge data `dgl.ETYPE` for the edge type. The partition graph contains additional node data ("inner_node") and edge data ("inner_edge"): * "inner_node" indicates whether a node belongs to a partition. * "inner_edge" indicates whether an edge belongs to a partition. Node and edge features are splitted and stored together with each graph partition. All node/edge features in a partition are stored in a file with DGL format. The node/edge features are stored in dictionaries, in which the key is the node/edge data name and the value is a tensor. We do not store features of HALO nodes and edges. When performing Metis partitioning, we can put some constraint on the partitioning. Current, it supports two constrants to balance the partitioning. By default, Metis always tries to balance the number of nodes in each partition. * ``balance_ntypes`` balances the number of nodes of different types in each partition. * ``balance_edges`` balances the number of edges in each partition. To balance the node types, a user needs to pass a vector of N elements to indicate the type of each node. N is the number of nodes in the input graph. Parameters ---------- g : DGLGraph The input graph to partition graph_name : str The name of the graph. The name will be used to construct :py:meth:`~dgl.distributed.DistGraph`. num_parts : int The number of partitions out_path : str The path to store the files for all partitioned data. num_hops : int, optional The number of hops of HALO nodes we construct on a partition graph structure. The default value is 1. part_method : str, optional The partition method. It supports "random" and "metis". The default value is "metis". balance_ntypes : tensor, optional Node type of each node. This is a 1D-array of integers. Its values indicates the node type of each node. This argument is used by Metis partition. When the argument is specified, the Metis algorithm will try to partition the input graph into partitions where each partition has roughly the same number of nodes for each node type. The default value is None, which means Metis partitions the graph to only balance the number of nodes. balance_edges : bool Indicate whether to balance the edges in each partition. This argument is used by the Metis algorithm. return_mapping : bool Indicate whether to return the mapping between shuffled node/edge IDs and the original node/edge IDs. num_trainers_per_machine : int, optional The number of trainers per machine. If is not 1, the whole graph will be first partitioned to each trainer, that is num_parts*num_trainers_per_machine parts. And the trainer ids of each node will be stored in the node feature 'trainer_id'. Then the partitions of trainers on the same machine will be coalesced into one larger partition. The final number of partitions is `num_part`. objtype : str, "cut" or "vol" Set the objective as edge-cut minimization or communication volume minimization. This argument is used by the Metis algorithm. graph_formats : str or list[str] Save partitions in specified formats. It could be any combination of ``coo``, ``csc`` and ``csr``. If not specified, save one format only according to what format is available. If multiple formats are available, selection priority from high to low is ``coo``, ``csc``, ``csr``. Returns ------- Tensor or dict of tensors, optional If `return_mapping=True`, return a 1D tensor that indicates the mapping between shuffled node IDs and the original node IDs for a homogeneous graph; return a dict of 1D tensors whose key is the node type and value is a 1D tensor mapping between shuffled node IDs and the original node IDs for each node type for a heterogeneous graph. Tensor or dict of tensors, optional If `return_mapping=True`, return a 1D tensor that indicates the mapping between shuffled edge IDs and the original edge IDs for a homogeneous graph; return a dict of 1D tensors whose key is the edge type and value is a 1D tensor mapping between shuffled edge IDs and the original edge IDs for each edge type for a heterogeneous graph. Examples -------- >>> dgl.distributed.partition_graph(g, 'test', 4, num_hops=1, part_method='metis', ... out_path='output/', ... balance_ntypes=g.ndata['train_mask'], ... balance_edges=True) >>> ( ... g, node_feats, edge_feats, gpb, graph_name, ntypes_list, etypes_list, ... ) = dgl.distributed.load_partition('output/test.json', 0) ''' # 'coo' is required for partition assert 'coo' in np.concatenate(list(g.formats().values())), \ "'coo' format should be allowed for partitioning graph." def get_homogeneous(g, balance_ntypes): if g.is_homogeneous: sim_g = to_homogeneous(g) if isinstance(balance_ntypes, dict): assert len(balance_ntypes) == 1 bal_ntypes = list(balance_ntypes.values())[0] else: bal_ntypes = balance_ntypes elif isinstance(balance_ntypes, dict): # Here we assign node types for load balancing. # The new node types includes the ones provided by users. num_ntypes = 0 for key in g.ntypes: if key in balance_ntypes: g.nodes[key].data['bal_ntype'] = F.astype(balance_ntypes[key], F.int32) + num_ntypes uniq_ntypes = F.unique(balance_ntypes[key]) assert np.all(F.asnumpy(uniq_ntypes) == np.arange(len(uniq_ntypes))) num_ntypes += len(uniq_ntypes) else: g.nodes[key].data['bal_ntype'] = F.ones((g.number_of_nodes(key),), F.int32, F.cpu()) * num_ntypes num_ntypes += 1 sim_g = to_homogeneous(g, ndata=['bal_ntype']) bal_ntypes = sim_g.ndata['bal_ntype'] print('The graph has {} node types and balance among {} types'.format( len(g.ntypes), len(F.unique(bal_ntypes)))) # We now no longer need them. for key in g.ntypes: del g.nodes[key].data['bal_ntype'] del sim_g.ndata['bal_ntype'] else: sim_g = to_homogeneous(g) bal_ntypes = sim_g.ndata[NTYPE] return sim_g, bal_ntypes if objtype not in ['cut', 'vol']: raise ValueError if num_parts == 1: start = time.time() sim_g, balance_ntypes = get_homogeneous(g, balance_ntypes) print('Converting to homogeneous graph takes {:.3f}s, peak mem: {:.3f} GB'.format( time.time() - start, get_peak_mem())) assert num_trainers_per_machine >= 1 if num_trainers_per_machine > 1: # First partition the whole graph to each trainer and save the trainer ids in # the node feature "trainer_id". start = time.time() node_parts = metis_partition_assignment( sim_g, num_parts * num_trainers_per_machine, balance_ntypes=balance_ntypes, balance_edges=balance_edges, mode='k-way') _set_trainer_ids(g, sim_g, node_parts) print('Assigning nodes to METIS partitions takes {:.3f}s, peak mem: {:.3f} GB'.format( time.time() - start, get_peak_mem())) node_parts = F.zeros((sim_g.number_of_nodes(),), F.int64, F.cpu()) parts = {0: sim_g.clone()} orig_nids = parts[0].ndata[NID] = F.arange(0, sim_g.number_of_nodes()) orig_eids = parts[0].edata[EID] = F.arange(0, sim_g.number_of_edges()) # For one partition, we don't really shuffle nodes and edges. We just need to simulate # it and set node data and edge data of orig_id. parts[0].ndata['orig_id'] = orig_nids parts[0].edata['orig_id'] = orig_eids if return_mapping: if g.is_homogeneous: orig_nids = F.arange(0, sim_g.number_of_nodes()) orig_eids = F.arange(0, sim_g.number_of_edges()) else: orig_nids = {ntype: F.arange(0, g.number_of_nodes(ntype)) for ntype in g.ntypes} orig_eids = {etype: F.arange(0, g.number_of_edges(etype)) for etype in g.canonical_etypes} parts[0].ndata['inner_node'] = F.ones((sim_g.number_of_nodes(),), RESERVED_FIELD_DTYPE['inner_node'], F.cpu()) parts[0].edata['inner_edge'] = F.ones((sim_g.number_of_edges(),), RESERVED_FIELD_DTYPE['inner_edge'], F.cpu()) elif part_method in ('metis', 'random'): start = time.time() sim_g, balance_ntypes = get_homogeneous(g, balance_ntypes) print('Converting to homogeneous graph takes {:.3f}s, peak mem: {:.3f} GB'.format( time.time() - start, get_peak_mem())) if part_method == 'metis': assert num_trainers_per_machine >= 1 start = time.time() if num_trainers_per_machine > 1: # First partition the whole graph to each trainer and save the trainer ids in # the node feature "trainer_id". node_parts = metis_partition_assignment( sim_g, num_parts * num_trainers_per_machine, balance_ntypes=balance_ntypes, balance_edges=balance_edges, mode='k-way', objtype=objtype) _set_trainer_ids(g, sim_g, node_parts) # And then coalesce the partitions of trainers on the same machine into one # larger partition. node_parts = F.floor_div(node_parts, num_trainers_per_machine) else: node_parts = metis_partition_assignment(sim_g, num_parts, balance_ntypes=balance_ntypes, balance_edges=balance_edges, objtype=objtype) print('Assigning nodes to METIS partitions takes {:.3f}s, peak mem: {:.3f} GB'.format( time.time() - start, get_peak_mem())) else: node_parts = random_choice(num_parts, sim_g.number_of_nodes()) start = time.time() parts, orig_nids, orig_eids = partition_graph_with_halo(sim_g, node_parts, num_hops, reshuffle=True) print('Splitting the graph into partitions takes {:.3f}s, peak mem: {:.3f} GB'.format( time.time() - start, get_peak_mem())) if return_mapping: orig_nids, orig_eids = _get_orig_ids(g, sim_g, orig_nids, orig_eids) else: raise Exception('Unknown partitioning method: ' + part_method) # If the input is a heterogeneous graph, get the original node types and original node IDs. # `part' has three types of node data at this point. # NTYPE: the node type. # orig_id: the global node IDs in the homogeneous version of input graph. # NID: the global node IDs in the reshuffled homogeneous version of the input graph. if not g.is_homogeneous: for name in parts: orig_ids = parts[name].ndata['orig_id'] ntype = F.gather_row(sim_g.ndata[NTYPE], orig_ids) parts[name].ndata[NTYPE] = F.astype(ntype, RESERVED_FIELD_DTYPE[NTYPE]) assert np.all(F.asnumpy(ntype) == F.asnumpy(parts[name].ndata[NTYPE])) # Get the original edge types and original edge IDs. orig_ids = parts[name].edata['orig_id'] etype = F.gather_row(sim_g.edata[ETYPE], orig_ids) parts[name].edata[ETYPE] = F.astype(etype, RESERVED_FIELD_DTYPE[ETYPE]) assert np.all(F.asnumpy(etype) == F.asnumpy(parts[name].edata[ETYPE])) # Calculate the global node IDs to per-node IDs mapping. inner_ntype = F.boolean_mask(parts[name].ndata[NTYPE], parts[name].ndata['inner_node'] == 1) inner_nids = F.boolean_mask(parts[name].ndata[NID], parts[name].ndata['inner_node'] == 1) for ntype in g.ntypes: inner_ntype_mask = inner_ntype == g.get_ntype_id(ntype) typed_nids = F.boolean_mask(inner_nids, inner_ntype_mask) # inner node IDs are in a contiguous ID range. expected_range = np.arange(int(F.as_scalar(typed_nids[0])), int(F.as_scalar(typed_nids[-1])) + 1) assert np.all(F.asnumpy(typed_nids) == expected_range) # Calculate the global edge IDs to per-edge IDs mapping. inner_etype = F.boolean_mask(parts[name].edata[ETYPE], parts[name].edata['inner_edge'] == 1) inner_eids = F.boolean_mask(parts[name].edata[EID], parts[name].edata['inner_edge'] == 1) for etype in g.canonical_etypes: inner_etype_mask = inner_etype == g.get_etype_id(etype) typed_eids = np.sort(F.asnumpy(F.boolean_mask(inner_eids, inner_etype_mask))) assert np.all(typed_eids == np.arange(int(typed_eids[0]), int(typed_eids[-1]) + 1)) os.makedirs(out_path, mode=0o775, exist_ok=True) tot_num_inner_edges = 0 out_path = os.path.abspath(out_path) # With reshuffling, we can ensure that all nodes and edges are reshuffled # and are in contiguous ID space. if num_parts > 1: node_map_val = {} edge_map_val = {} for ntype in g.ntypes: ntype_id = g.get_ntype_id(ntype) val = [] node_map_val[ntype] = [] for i in parts: inner_node_mask = _get_inner_node_mask(parts[i], ntype_id) val.append(F.as_scalar(F.sum(F.astype(inner_node_mask, F.int64), 0))) inner_nids = F.boolean_mask(parts[i].ndata[NID], inner_node_mask) node_map_val[ntype].append([int(F.as_scalar(inner_nids[0])), int(F.as_scalar(inner_nids[-1])) + 1]) val = np.cumsum(val).tolist() assert val[-1] == g.number_of_nodes(ntype) for etype in g.canonical_etypes: etype_id = g.get_etype_id(etype) val = [] edge_map_val[etype] = [] for i in parts: inner_edge_mask = _get_inner_edge_mask(parts[i], etype_id) val.append(F.as_scalar(F.sum(F.astype(inner_edge_mask, F.int64), 0))) inner_eids = np.sort(F.asnumpy(F.boolean_mask(parts[i].edata[EID], inner_edge_mask))) edge_map_val[etype].append([int(inner_eids[0]), int(inner_eids[-1]) + 1]) val = np.cumsum(val).tolist() assert val[-1] == g.number_of_edges(etype) else: node_map_val = {} edge_map_val = {} for ntype in g.ntypes: ntype_id = g.get_ntype_id(ntype) inner_node_mask = _get_inner_node_mask(parts[0], ntype_id) inner_nids = F.boolean_mask(parts[0].ndata[NID], inner_node_mask) node_map_val[ntype] = [[int(F.as_scalar(inner_nids[0])), int(F.as_scalar(inner_nids[-1])) + 1]] for etype in g.canonical_etypes: etype_id = g.get_etype_id(etype) inner_edge_mask = _get_inner_edge_mask(parts[0], etype_id) inner_eids = F.boolean_mask(parts[0].edata[EID], inner_edge_mask) edge_map_val[etype] = [[int(F.as_scalar(inner_eids[0])), int(F.as_scalar(inner_eids[-1])) + 1]] # Double check that the node IDs in the global ID space are sorted. for ntype in node_map_val: val = np.concatenate([np.array(l) for l in node_map_val[ntype]]) assert np.all(val[:-1] <= val[1:]) for etype in edge_map_val: val = np.concatenate([np.array(l) for l in edge_map_val[etype]]) assert np.all(val[:-1] <= val[1:]) start = time.time() ntypes = {ntype:g.get_ntype_id(ntype) for ntype in g.ntypes} etypes = {etype:g.get_etype_id(etype) for etype in g.canonical_etypes} part_metadata = {'graph_name': graph_name, 'num_nodes': g.number_of_nodes(), 'num_edges': g.number_of_edges(), 'part_method': part_method, 'num_parts': num_parts, 'halo_hops': num_hops, 'node_map': node_map_val, 'edge_map': edge_map_val, 'ntypes': ntypes, 'etypes': etypes} for part_id in range(num_parts): part = parts[part_id] # Get the node/edge features of each partition. node_feats = {} edge_feats = {} if num_parts > 1: for ntype in g.ntypes: ntype_id = g.get_ntype_id(ntype) # To get the edges in the input graph, we should use original node IDs. # Both orig_id and NID stores the per-node-type IDs. ndata_name = 'orig_id' inner_node_mask = _get_inner_node_mask(part, ntype_id) # This is global node IDs. local_nodes = F.boolean_mask(part.ndata[ndata_name], inner_node_mask) if len(g.ntypes) > 1: # If the input is a heterogeneous graph. local_nodes = F.gather_row(sim_g.ndata[NID], local_nodes) print('part {} has {} nodes of type {} and {} are inside the partition'.format( part_id, F.as_scalar(F.sum(part.ndata[NTYPE] == ntype_id, 0)), ntype, len(local_nodes))) else: print('part {} has {} nodes and {} are inside the partition'.format( part_id, part.number_of_nodes(), len(local_nodes))) for name in g.nodes[ntype].data: if name in [NID, 'inner_node']: continue node_feats[ntype + '/' + name] = F.gather_row(g.nodes[ntype].data[name], local_nodes) for etype in g.canonical_etypes: etype_id = g.get_etype_id(etype) edata_name = 'orig_id' inner_edge_mask = _get_inner_edge_mask(part, etype_id) # This is global edge IDs. local_edges = F.boolean_mask(part.edata[edata_name], inner_edge_mask) if not g.is_homogeneous: local_edges = F.gather_row(sim_g.edata[EID], local_edges) print('part {} has {} edges of type {} and {} are inside the partition'.format( part_id, F.as_scalar(F.sum(part.edata[ETYPE] == etype_id, 0)), etype, len(local_edges))) else: print('part {} has {} edges and {} are inside the partition'.format( part_id, part.number_of_edges(), len(local_edges))) tot_num_inner_edges += len(local_edges) for name in g.edges[etype].data: if name in [EID, 'inner_edge']: continue edge_feats[_etype_tuple_to_str(etype) + '/' + name] = F.gather_row( g.edges[etype].data[name], local_edges) else: for ntype in g.ntypes: if len(g.ntypes) > 1: ndata_name = 'orig_id' ntype_id = g.get_ntype_id(ntype) inner_node_mask = _get_inner_node_mask(part, ntype_id) # This is global node IDs. local_nodes = F.boolean_mask(part.ndata[ndata_name], inner_node_mask) local_nodes = F.gather_row(sim_g.ndata[NID], local_nodes) else: local_nodes = sim_g.ndata[NID] for name in g.nodes[ntype].data: if name in [NID, 'inner_node']: continue node_feats[ntype + '/' + name] = F.gather_row(g.nodes[ntype].data[name], local_nodes) for etype in g.canonical_etypes: if not g.is_homogeneous: edata_name = 'orig_id' etype_id = g.get_etype_id(etype) inner_edge_mask = _get_inner_edge_mask(part, etype_id) # This is global edge IDs. local_edges = F.boolean_mask(part.edata[edata_name], inner_edge_mask) local_edges = F.gather_row(sim_g.edata[EID], local_edges) else: local_edges = sim_g.edata[EID] for name in g.edges[etype].data: if name in [EID, 'inner_edge']: continue edge_feats[_etype_tuple_to_str(etype) + '/' + name] = F.gather_row( g.edges[etype].data[name], local_edges) # delete `orig_id` from ndata/edata del part.ndata['orig_id'] del part.edata['orig_id'] part_dir = os.path.join(out_path, "part" + str(part_id)) node_feat_file = os.path.join(part_dir, "node_feat.dgl") edge_feat_file = os.path.join(part_dir, "edge_feat.dgl") part_graph_file = os.path.join(part_dir, "graph.dgl") part_metadata['part-{}'.format(part_id)] = { 'node_feats': os.path.relpath(node_feat_file, out_path), 'edge_feats': os.path.relpath(edge_feat_file, out_path), 'part_graph': os.path.relpath(part_graph_file, out_path)} os.makedirs(part_dir, mode=0o775, exist_ok=True) save_tensors(node_feat_file, node_feats) save_tensors(edge_feat_file, edge_feats) sort_etypes = len(g.etypes) > 1 _save_graphs(part_graph_file, [part], formats=graph_formats, sort_etypes=sort_etypes) print('Save partitions: {:.3f} seconds, peak memory: {:.3f} GB'.format( time.time() - start, get_peak_mem())) _dump_part_config(f'{out_path}/{graph_name}.json', part_metadata) num_cuts = sim_g.number_of_edges() - tot_num_inner_edges if num_parts == 1: num_cuts = 0 print('There are {} edges in the graph and {} edge cuts for {} partitions.'.format( g.number_of_edges(), num_cuts, num_parts)) if return_mapping: return orig_nids, orig_eids