"""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, dgl_warning
from ..convert import to_homogeneous
from ..random import choice as random_choice
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 .graph_partition_book import BasicPartitionBook, RangePartitionBook
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 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.
Returns
-------
DGLGraph
The graph partition structure.
Dict[str, Tensor]
Node features.
Dict[str, Tensor]
Edge features.
GraphPartitionBook
The graph partition information.
str
The graph name
List[str]
The node types
List[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 '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."
assert 'part_graph' in part_files, "the partition does not contain graph structure."
node_feats = load_tensors(relative_to_config(part_files['node_feats']))
edge_feats = load_tensors(relative_to_config(part_files['edge_feats']))
graph = load_graphs(relative_to_config(part_files['part_graph']))[0][0]
# 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.
node_feats1 = {}
edge_feats1 = {}
for name in node_feats:
feat = node_feats[name]
if name.find('/') == -1:
name = '_N/' + name
node_feats1[name] = feat
for name in edge_feats:
feat = edge_feats[name]
if name.find('/') == -1:
name = '_E/' + name
edge_feats1[name] = feat
node_feats = node_feats1
edge_feats = edge_feats1
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, graph)
ntypes_list, etypes_list = [], []
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)), 'load a wrong partition'
assert np.all(F.asnumpy(partids2 == part_id)), 'load a wrong partition'
ntypes_list.append(ntype)
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)), 'load a wrong partition'
assert np.all(F.asnumpy(partids2 == part_id)), 'load a wrong partition'
etypes_list.append(etype)
return graph, node_feats, edge_feats, gpb, graph_name, ntypes_list, etypes_list
[docs]def load_partition_book(part_config, part_id, graph=None):
''' 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.
graph : DGLGraph
The graph structure
Returns
-------
GraphPartitionBook
The global partition information.
str
The graph name
dict
The node types
dict
The edge types
'''
with open(part_config) as conf_f:
part_metadata = json.load(conf_f)
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 = {'_N': node_map}
else:
is_range_part = False
if isinstance(edge_map, list):
edge_map = {'_E': edge_map}
ntypes = {'_N': 0}
etypes = {'_E': 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 is_range_part:
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
else:
node_map = np.load(node_map)
edge_map = np.load(edge_map)
return BasicPartitionBook(part_id, num_parts, node_map, edge_map, graph), \
part_metadata['graph_name'], ntypes, etypes
def _get_orig_ids(g, sim_g, reshuffle, 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.
reshuffle : bool
Whether the input graph is reshuffled during partitioning.
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 reshuffle and 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.etypes}
elif not reshuffle and not is_hetero:
orig_nids = F.arange(0, sim_g.number_of_nodes())
orig_eids = F.arange(0, sim_g.number_of_edges())
elif not reshuffle:
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.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 _, etype, dst_type in g.canonical_etypes:
# An edge is assigned to a partition based on its destination node.
trainer_id = F.gather_row(g.nodes[dst_type].data['trainer_id'], g.edges(etype=etype)[1])
g.edges[etype].data['trainer_id'] = trainer_id
[docs]def partition_graph(g, graph_name, num_parts, out_path, num_hops=1, part_method="metis",
reshuffle=True, balance_ntypes=None, balance_edges=False, return_mapping=False,
num_trainers_per_machine=1, objtype='cut'):
''' 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": {
"_U": [ [ 0, 1261310 ],
[ 1261310, 2449029 ] ]
},
"edge_map": {
"_V": [ [ 0, 62539528 ],
[ 62539528, 123718280 ] ]
},
"etypes": { "_V": 0 },
"ntypes": { "_U": 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.
If ``reshuffle=False``, node IDs and edge IDs of a partition do not fall into contiguous
ID ranges. In this case, DGL stores node/edge mappings (from
node/edge IDs to partition IDs) in separate files (node_map.npy and edge_map.npy).
The node/edge mappings are stored in numpy files.
.. warning::
this format is deprecated and will not be supported by the next release. In other words,
the future release will always shuffle node IDs and edge IDs when partitioning a graph.
If ``reshuffle=True``, ``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/edge types. 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 "orig_id") 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.
* "orig_id" exists when reshuffle=True. It indicates the original node IDs in the original
graph before reshuffling.
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".
reshuffle : bool, optional
Reshuffle nodes and edges so that nodes and edges in a partition are in
contiguous ID range. The default value is True. The argument is deprecated
and will be removed in the next release.
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
If `reshuffle=True`, this indicates 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.
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/', reshuffle=True,
... balance_ntypes=g.ndata['train_mask'],
... balance_edges=True)
>>> g, node_feats, edge_feats, gpb, graph_name = dgl.distributed.load_partition(
... 'output/test.json', 0)
'''
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 not reshuffle:
dgl_warning("The argument reshuffle will be deprecated in the next release. "
"For heterogeneous graphs, reshuffle must be enabled.")
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.
if reshuffle:
parts[0].ndata['orig_id'] = orig_nids
parts[0].edata['orig_id'] = orig_eids
if return_mapping:
orig_nids, orig_eids = _get_orig_ids(g, sim_g, False, orig_nids, orig_eids)
parts[0].ndata['inner_node'] = F.ones((sim_g.number_of_nodes(),), F.int8, F.cpu())
parts[0].edata['inner_edge'] = F.ones((sim_g.number_of_edges(),), F.int8, 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=reshuffle)
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, reshuffle, 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:
if reshuffle:
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, F.int32)
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, F.int32)
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.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))
else:
raise NotImplementedError('not shuffled case')
# Let's calculate edge assignment.
if not reshuffle:
start = time.time()
# We only optimize for reshuffled case. So it's fine to use int64 here.
edge_parts = np.zeros((g.number_of_edges(),), dtype=np.int64) - 1
for part_id in parts:
part = parts[part_id]
# To get the edges in the input graph, we should use original node IDs.
local_edges = F.boolean_mask(part.edata[EID], part.edata['inner_edge'])
edge_parts[F.asnumpy(local_edges)] = part_id
print('Calculate edge assignment: {:.3f} seconds'.format(time.time() - start))
os.makedirs(out_path, mode=0o775, exist_ok=True)
tot_num_inner_edges = 0
out_path = os.path.abspath(out_path)
# Without reshuffling, we have to store the entire node/edge mapping in a file.
if not reshuffle:
node_part_file = os.path.join(out_path, "node_map")
edge_part_file = os.path.join(out_path, "edge_map")
np.save(node_part_file, F.asnumpy(node_parts), allow_pickle=False)
np.save(edge_part_file, edge_parts, allow_pickle=False)
node_map_val = node_part_file + ".npy"
edge_map_val = edge_part_file + ".npy"
else:
# 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.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.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.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' if reshuffle else NID
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.etypes:
etype_id = g.get_etype_id(etype)
edata_name = 'orig_id' if reshuffle else EID
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 + '/' + name] = F.gather_row(g.edges[etype].data[name],
local_edges)
else:
for ntype in g.ntypes:
if reshuffle and 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)
elif reshuffle:
local_nodes = sim_g.ndata[NID]
for name in g.nodes[ntype].data:
if name in [NID, 'inner_node']:
continue
if reshuffle:
node_feats[ntype + '/' + name] = F.gather_row(g.nodes[ntype].data[name],
local_nodes)
else:
node_feats[ntype + '/' + name] = g.nodes[ntype].data[name]
for etype in g.etypes:
if reshuffle and 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)
elif reshuffle:
local_edges = sim_g.edata[EID]
for name in g.edges[etype].data:
if name in [EID, 'inner_edge']:
continue
if reshuffle:
edge_feats[etype + '/' + name] = F.gather_row(g.edges[etype].data[name],
local_edges)
else:
edge_feats[etype + '/' + name] = g.edges[etype].data[name]
# Some adjustment for heterogeneous graphs.
if not g.is_homogeneous:
part.ndata['orig_id'] = F.gather_row(sim_g.ndata[NID], part.ndata['orig_id'])
part.edata['orig_id'] = F.gather_row(sim_g.edata[EID], 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)
save_graphs(part_graph_file, [part])
print('Save partitions: {:.3f} seconds, peak memory: {:.3f} GB'.format(
time.time() - start, get_peak_mem()))
with open('{}/{}.json'.format(out_path, graph_name), 'w') as outfile:
json.dump(part_metadata, outfile, sort_keys=True, indent=4)
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