[Distributed] Add GraphPartitionBook (dmlc#1496)

* Add graph-partition-book

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* skip test on GPU

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python/dgl/distributed/__init__.py

@@ -2,3 +2,4 @@
 
 from .dist_graph import DistGraphServer, DistGraph
 from .partition import partition_graph, load_partition
+from .graph_partition_book import GraphPartitionBook

python/dgl/distributed/graph_partition_book.py

@@ -0,0 +1,236 @@
+"""Define graph partition book."""
+
+import numpy as np
+
+from .. import backend as F
+from ..base import NID, EID
+
+class GraphPartitionBook:
+    """GraphPartitionBook is used to store parition information.
+
+    Parameters
+    ----------
+    part_id : int
+        partition id of current GraphPartitionBook
+    num_parts : int
+        number of total partitions
+    node_map : numpy array
+        global node id mapping to partition id
+    edge_map : numpy array
+        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 = part_id
+        self._num_partitions = num_parts
+        self._nid2partid = F.zerocopy_from_numpy(node_map)
+        self._eid2partid = F.zerocopy_from_numpy(edge_map)
+        self._graph = part_graph
+        # Get meta data of GraphPartitionBook
+        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'] = nid_count[partid]
+            part_info['num_edges'] = 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 = self._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 = self._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
+
+
+    def num_partitions(self):
+        """Return the number of partitions.
+
+        Returns
+        -------
+        int
+            number of partitions
+        """
+        return self._num_partitions
+
+
+    def metadata(self):
+        """Return the partition meta data.
+
+        The meta data includes:
+
+        * The machine ID.
+        * The machine IP address.
+        * 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.
+        """
+        return self._partition_meta_data
+
+
+    def nid2partid(self, nids):
+        """From global node IDs to partition IDs
+
+        Parameters
+        ----------
+        nids : tensor
+            global node IDs
+
+        Returns
+        -------
+        tensor
+            partition IDs
+        """
+        return F.gather_row(self._nid2partid, nids)
+
+
+    def eid2partid(self, eids):
+        """From global edge IDs to partition IDs
+
+        Parameters
+        ----------
+        eids : tensor
+            global edge IDs
+
+        Returns
+        -------
+        tensor
+            partition IDs
+        """
+        return F.gather_row(self._eid2partid, eids)
+
+
+    def partid2nids(self, partid):
+        """From partition id to node IDs
+
+        Parameters
+        ----------
+        partid : int
+            partition id
+
+        Returns
+        -------
+        tensor
+            node IDs
+        """
+        return self._partid2nids[partid]
+
+
+    def partid2eids(self, partid):
+        """From partition id to edge IDs
+
+        Parameters
+        ----------
+        partid : int
+            partition id
+
+        Returns
+        -------
+        tensor
+            edge IDs
+        """
+        return self._partid2eids[partid]
+
+
+    def nid2localnid(self, nids, partid):
+        """Get local node IDs within the given partition.
+
+        Parameters
+        ----------
+        nids : tensor
+            global node IDs
+        partid : int
+            partition ID
+
+        Returns
+        -------
+        tensor
+             local node IDs
+        """
+        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):
+        """Get the local edge ids within the given partition.
+
+        Parameters
+        ----------
+        eids : tensor
+            global edge ids
+        partid : int
+            partition ID
+
+        Returns
+        -------
+        tensor
+             local edge ids
+        """
+        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)
+
+
+    def get_partition(self, partid):
+        """Get the graph of one partition.
+
+        Parameters
+        ----------
+        partid : int
+            Partition ID.
+
+        Returns
+        -------
+        DGLGraph
+            The graph of the partition.
+        """
+        if partid != self._part_id:
+            raise RuntimeError('Now GraphPartitionBook does not support \
+                getting remote partitions.')
+
+        return self._graph

python/dgl/distributed/partition.py

@@ -25,7 +25,7 @@
 {
   "graph_name" : "test",
   "part_method" : "metis",
-  "num_parts" : 2
+  "num_parts" : 2,
   "halo_hops" : 1,
   "node_map" : "data_root_dir/node_map.npy",
   "edge_map" : "data_root_dir/edge_map.npy"
@@ -120,6 +120,8 @@ def load_partition(conf_file, part_id):
     '''
     with open(conf_file) as conf_f:
         part_metadata = json.load(conf_f)
+    assert 'num_parts' in part_metadata, 'num_parts does not exist.'
+    num_parts = part_metadata['num_parts']
     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."
@@ -134,7 +136,7 @@ def load_partition(conf_file, part_id):
     assert 'edge_map' in part_metadata, "cannot get the edge map."
     node_map = np.load(part_metadata['node_map'])
     edge_map = np.load(part_metadata['edge_map'])
-    meta = (part_metadata['num_nodes'], part_metadata['num_edges'], node_map, edge_map)
+    meta = (part_metadata['num_nodes'], part_metadata['num_edges'], node_map, edge_map, num_parts)
     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"
 

tests/distributed/test_graph_partition_book.py

@@ -0,0 +1,58 @@
+import dgl
+import sys
+import numpy as np
+from scipy import sparse as spsp
+from numpy.testing import assert_array_equal
+from dgl.graph_index import create_graph_index
+from dgl.distributed import partition_graph, load_partition, GraphPartitionBook
+import backend as F
+import unittest
+import pickle
+
+def create_ip_config():
+    ip_config = open("ip_config.txt", "w")
+    ip_config.write('192.168.9.12 30050 0\n')
+    ip_config.write('192.168.9.13 30050 1\n')
+    ip_config.write('192.168.9.14 30050 2\n')
+    ip_config.write('192.168.9.15 30050 3\n')
+    ip_config.close()
+
+def create_random_graph(n):
+    arr = (spsp.random(n, n, density=0.001, format='coo') != 0).astype(np.int64)
+    ig = create_graph_index(arr, readonly=True)
+    return dgl.DGLGraph(ig)
+
+@unittest.skipIf(F._default_context_str == 'gpu', reason="METIS doesn't support GPU")
+def test_graph_partition_book():
+    g = create_random_graph(10000)
+    g.ndata['labels'] = F.arange(0, g.number_of_nodes())
+    g.ndata['feats'] = F.tensor(np.random.randn(g.number_of_nodes(), 10))
+    num_parts = 4
+    num_hops = 2
+
+    create_ip_config()
+    partition_graph(g, 'test', num_parts, '/tmp', num_hops=num_hops, part_method='metis')
+
+    for i in range(num_parts):
+        part_g, node_feats, edge_feats, meta = load_partition('/tmp/test.json', i)
+        num_nodes, num_edges, node_map, edge_map, num_partitions = meta
+        gpb = GraphPartitionBook(part_id=i,
+                                 num_parts=num_partitions,
+                                 node_map=node_map,
+                                 edge_map=edge_map,
+                                 part_graph=part_g)
+        assert gpb.num_partitions() == num_parts
+        gpb_meta = gpb.metadata()
+        assert len(gpb_meta) == num_parts
+        assert np.all(F.asnumpy(gpb.nid2partid(F.arange(0, len(node_map)))) == node_map)
+        assert np.all(F.asnumpy(gpb.eid2partid(F.arange(0, len(edge_map)))) == edge_map)
+        assert len(gpb.partid2nids(i)) == gpb_meta[i]['num_nodes']
+        assert len(gpb.partid2eids(i)) == gpb_meta[i]['num_edges']
+        local_nid = gpb.nid2localnid(part_g.ndata[dgl.NID], i)
+        assert np.all(F.asnumpy(local_nid) == F.asnumpy(F.arange(0, len(local_nid))))
+        local_eid = gpb.eid2localeid(part_g.edata[dgl.EID], i)
+        assert np.all(F.asnumpy(local_eid) == F.asnumpy(F.arange(0, len(local_eid))))
+
+
+if __name__ == '__main__':
+    test_graph_partition_book()

tests/distributed/test_partition.py

@@ -24,11 +24,12 @@ def test_partition():
     partition_graph(g, 'test', num_parts, '/tmp', num_hops=num_hops, part_method='metis')
     for i in range(num_parts):
         part_g, node_feats, edge_feats, meta = load_partition('/tmp/test.json', i)
-        num_nodes, num_edges, node_map, edge_map = meta
+        num_nodes, num_edges, node_map, edge_map, num_partitions = meta
 
         # Check the metadata
         assert num_nodes == g.number_of_nodes()
         assert num_edges == g.number_of_edges()
+        assert num_partitions == num_parts
 
         # Check the node map.
         local_nodes = np.nonzero(node_map == i)[0]