Merge branch 'dist_part' of github.com:dmlc/dgl into dist_part

tools/distpartitioning/convert_partition.py

@@ -9,6 +9,7 @@ import pyarrow
 import pandas as pd
 import constants
 from pyarrow import csv
+from utils import read_json
 
 def create_dgl_object(graph_name, num_parts, \
                         schema, part_id, node_data, \
@@ -129,7 +130,8 @@ def create_dgl_object(graph_name, num_parts, \
     assert len(uniq_ids) == len(idx)
     # We get the edge list with their node IDs mapped to a contiguous ID range.
     part_local_src_id, part_local_dst_id = np.split(inverse_idx[:len(shuffle_global_src_id) * 2], 2)
-    compact_g = dgl.graph((part_local_src_id, part_local_dst_id))
+
+    compact_g = dgl.graph(data=(part_local_src_id, part_local_dst_id), num_nodes=len(idx))
     compact_g.edata['orig_id'] = th.as_tensor(global_edge_id)
     compact_g.edata[dgl.ETYPE] = th.as_tensor(etype_ids)
     compact_g.edata['inner_edge'] = th.ones(

tools/distpartitioning/globalids.py

@@ -50,7 +50,7 @@ def get_shuffle_global_nids(rank, world_size, global_nids_ranks, node_data):
     #form the outgoing message
     send_nodes = []
     for i in range(world_size):
-        send_nodes.append(torch.Tensor(global_nids_ranks[i]).type(dtype=torch.int64))
+        send_nodes.append(torch.from_numpy(global_nids_ranks[i]).type(dtype=torch.int64))
 
     #send-recieve messages
     alltoallv_cpu(rank, world_size, recv_nodes, send_nodes)