[Refactor][Graph] Merge DGLGraph and DGLHeteroGraph (#1862)

* Merge

* [Graph][CUDA] Graph on GPU and many refactoring (#1791)

* change edge_ids behavior and C++ impl

* fix unittests; remove utils.Index in edge_id

* pass mx and th tests

* pass tf test

* add aten::Scatter_

* Add nonzero; impl CSRGetDataAndIndices/CSRSliceMatrix

* CSRGetData and CSRGetDataAndIndices passed tests

* CSRSliceMatrix basic tests

* fix bug in empty slice

* CUDA CSRHasDuplicate

* has_node; has_edge_between

* predecessors, successors

* deprecate send/recv; fix send_and_recv

* deprecate send/recv; fix send_and_recv

* in_edges; out_edges; all_edges; apply_edges

* in deg/out deg

* subgraph/edge_subgraph

* adj

* in_subgraph/out_subgraph

* sample neighbors

* set/get_n/e_repr

* wip: working on refactoring all idtypes

* pass ndata/edata tests on gpu

* fix

* stash

* workaround nonzero issue

* stash

* nx conversion

* test_hetero_basics except update routines

* test_update_routines

* test_hetero_basics for pytorch

* more fixes

* WIP: flatten graph

* wip: flatten

* test_flatten

* test_to_device

* fix bug in to_homo

* fix bug in CSRSliceMatrix

* pass subgraph test

* fix send_and_recv

* fix filter

* test_heterograph

* passed all pytorch tests

* fix mx unittest

* fix pytorch test_nn

* fix all unittests for PyTorch

* passed all mxnet tests

* lint

* fix tf nn test

* pass all tf tests

* lint

* lint

* change deprecation

* try fix compile

* lint

* update METIDS

* fix utest

* fix

* fix utests

* try debug

* revert

* small fix

* fix utests

* upd

* upd

* upd

* fix

* upd

* upd

* upd

* upd

* upd

* trigger

* +1s

* [kernel] Use heterograph index instead of unitgraph index (#1813)

* upd

* upd

* upd

* fix

* upd

* upd

* upd

* upd

* upd

* trigger

* +1s

* [Graph] Mutation for Heterograph (#1818)

* mutation add_nodes and add_edges

* Add support for remove_edges, remove_nodes, add_selfloop, remove_selfloop

* Fix
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>

* upd

* upd

* upd

* fix

* [Transfom] Mutable transform (#1833)

* add nodesy

* All three

* Fix

* lint

* Add some test case

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* fix

* triger

* Fix

* fix
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>

* [Graph] Migrate Batch & Readout module to heterograph (#1836)

* dgl.batch

* unbatch

* fix to device

* reduce readout; segment reduce

* change batch_num_nodes|edges to function

* reduce readout/ softmax

* broadcast

* topk

* fix

* fix tf and mx

* fix some ci

* fix batch but unbatch differently

* new checkk

* upd

* upd

* upd

* idtype behavior; code reorg

* idtype behavior; code reorg

* wip: test_basics

* pass test_basics

* WIP: from nx/ to nx

* missing files

* upd

* pass test_basics:test_nx_conversion

* Fix test

* Fix inplace update

* WIP: fixing tests

* upd

* pass test_transform cpu

* pass gpu test_transform

* pass test_batched_graph

* GPU graph auto cast to int32

* missing file

* stash

* WIP: rgcn-hetero

* Fix two datasety

* upd

* weird

* Fix capsuley

* fuck you

* fuck matthias

* Fix dgmg

* fix bug in block degrees; pass rgcn-hetero

* rgcn

* gat and diffpool fix
also fix ppi and tu dataset

* Tree LSTM

* pointcloud

* rrn; wip: sgc

* resolve conflicts

* upd

* sgc and reddit dataset

* upd

* Fix deepwalk, gindt and gcn

* fix datasets and sign

* optimization

* optimization

* upd

* upd

* Fix GIN

* fix bug in add_nodes add_edges; tagcn

* adaptive sampling and gcmc

* upd

* upd

* fix geometric

* fix

* metapath2vec

* fix agnn

* fix pickling problem of block

* fix utests

* miss file

* linegraph

* upd

* upd

* upd

* graphsage

* stgcn_wave

* fix hgt

* on unittests

* Fix transformer

* Fix HAN

* passed pytorch unittests

* lint

* fix

* Fix cluster gcn

* cluster-gcn is ready

* on fixing block related codes

* 2nd order derivative

* Revert "2nd order derivative"

This reverts commit 523bf6c249bee61b51b1ad1babf42aad4167f206.

* passed torch utests again

* fix all mxnet unittests

* delete some useless tests

* pass all tf cpu tests

* disable

* disable distributed unittest

* fix

* fix

* lint

* fix

* fix

* fix script

* fix tutorial

* fix apply edges bug

* fix 2 basics

* fix tutorial
Co-authored-by: yzh119 <expye@outlook.com>
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-7-42.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-1-5.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-68-185.ec2.internal>

examples/pytorch/metapath2vec/sampler.py

@@ -74,11 +74,11 @@ def construct_graph():
     f_1.close()
     f_2.close()
 
-    pa = dgl.bipartite((paper_author_src, paper_author_dst), 'paper', 'pa', 'author')
-    ap = dgl.bipartite((paper_author_dst, paper_author_src), 'author', 'ap', 'paper')
-    pc = dgl.bipartite((paper_conf_src, paper_conf_dst), 'paper', 'pc', 'conf')
-    cp = dgl.bipartite((paper_conf_dst, paper_conf_src), 'conf', 'cp', 'paper')
-    hg = dgl.hetero_from_relations([pa, ap, pc, cp])
+    hg = dgl.heterograph({
+        ('paper', 'pa', 'author') : (paper_author_src, paper_author_dst),
+        ('author', 'ap', 'paper') : (paper_author_dst, paper_author_src),
+        ('paper', 'pc', 'conf') : (paper_conf_src, paper_conf_dst),
+        ('conf', 'cp', 'paper') : (paper_conf_dst, paper_conf_src)})
     return hg, author_names, conf_names, paper_names
 
 #"conference - paper - Author - paper - conference" metapath sampling

examples/pytorch/model_zoo/citation_network/README.md

@@ -24,7 +24,7 @@ citation network datasets.
 ## Usage
 
 ```
-python run.py [--gpu] --model MODEL_NAME --dataset DATASET_NAME [--self-loop]
+python run.py [--gpu GPU] --model MODEL_NAME --dataset DATASET_NAME [--self-loop]
 ```
 
 The hyperparameters might not be the optimal, you could specify them manually in `conf.py`.

examples/pytorch/model_zoo/citation_network/models.py

@@ -302,19 +302,19 @@ class ChebNet(nn.Module):
         self.g = g
         self.layers = nn.ModuleList()
         self.layers.append(
-            ChebConv(in_feats, n_hidden, k, bias)
+            ChebConv(in_feats, n_hidden, k, bias=bias)
         )
         for _ in range(n_layers - 1):
             self.layers.append(
-                ChebConv(n_hidden, n_hidden, k, bias)
+                ChebConv(n_hidden, n_hidden, k, bias=bias)
             )
 
         self.layers.append(
-            ChebConv(n_hidden, n_classes, k, bias)
+            ChebConv(n_hidden, n_classes, k, bias=bias)
         )
 
     def forward(self, features):
         h = features
         for layer in self.layers:
             h = layer(self.g, h, [2])
-        return h
+        return h
\ No newline at end of file

examples/pytorch/model_zoo/citation_network/run.py

@@ -3,7 +3,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from dgl import DGLGraph
+import dgl
 from dgl.data import register_data_args, load_data
 from models import *
 from conf import *
@@ -44,16 +44,17 @@ def evaluate(model, features, labels, mask):
 def main(args):
     # load and preprocess dataset
     data = load_data(args)
-    features = torch.FloatTensor(data.features)
-    labels = torch.LongTensor(data.labels)
-    if hasattr(torch, 'BoolTensor'):
-        train_mask = torch.BoolTensor(data.train_mask)
-        val_mask = torch.BoolTensor(data.val_mask)
-        test_mask = torch.BoolTensor(data.test_mask)
+    g = data[0]
+    if args.gpu < 0:
+        cuda = False
     else:
-        train_mask = torch.ByteTensor(data.train_mask)
-        val_mask = torch.ByteTensor(data.val_mask)
-        test_mask = torch.ByteTensor(data.test_mask)
+        cuda = True
+        g = g.to(args.gpu)
+    features = g.ndata['feat']
+    labels = g.ndata['label']
+    train_mask = g.ndata['train_mask']
+    val_mask = g.ndata['val_mask']
+    test_mask = g.ndata['test_mask']
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
@@ -68,31 +69,16 @@ def main(args):
               val_mask.int().sum().item(),
               test_mask.int().sum().item()))
 
-    if args.gpu < 0:
-        cuda = False
-    else:
-        cuda = True
-        torch.cuda.set_device(args.gpu)
-        features = features.cuda()
-        labels = labels.cuda()
-        train_mask = train_mask.cuda()
-        val_mask = val_mask.cuda()
-        test_mask = test_mask.cuda()
-
     # graph preprocess and calculate normalization factor
-    g = data.graph
     # add self loop
     if args.self_loop:
-        g.remove_edges_from(nx.selfloop_edges(g))
-        g.add_edges_from(zip(g.nodes(), g.nodes()))
-    g = DGLGraph(g)
+        g = g.remove_self_loop().add_self_loop()
     n_edges = g.number_of_edges()
+
     # normalization
     degs = g.in_degrees().float()
     norm = torch.pow(degs, -0.5)
     norm[torch.isinf(norm)] = 0
-    if cuda:
-        norm = norm.cuda()
     g.ndata['norm'] = norm.unsqueeze(1)
 
     # create GCN model
@@ -103,7 +89,7 @@ def main(args):
                 *config['extra_args'])
 
     if cuda:
-        model.cuda()
+        model = model.cuda()
 
     print(model)
 

examples/pytorch/model_zoo/geometric/mnist.py

@@ -8,7 +8,6 @@ import torch.nn.functional as F
 import dgl
 from torch.utils.data import DataLoader
 from torchvision import datasets, transforms
-from dgl import DGLGraph
 from dgl.data import register_data_args, load_data
 from dgl.nn.pytorch.conv import ChebConv, GMMConv
 from dgl.nn.pytorch.glob import MaxPooling
@@ -33,7 +32,7 @@ A = grid_graph(28, 8, metric)
 
 coarsening_levels = 4
 L, perm = coarsen(A, coarsening_levels)
-g_arr = [DGLGraph(csr) for csr in L]
+g_arr = [dgl.graph(csr) for csr in L]
 
 coordinate_arr = get_coordinates(g_arr, grid_side, coarsening_levels, perm)
 for g, coordinate_arr in zip(g_arr, coordinate_arr):
@@ -45,7 +44,7 @@ def batcher(batch):
     x_batch = []
     y_batch = []
     for x, y in batch:
-        x = torch.cat([x.view(-1), x.new_zeros(928 - 28 ** 2)], 0)
+        x = torch.cat([x.view(-1), x.new_zeros(len(perm) - 28 ** 2)], 0)
         x = x[perm]
         x_batch.append(x)
         y_batch.append(y)
@@ -97,9 +96,11 @@ class MoNet(nn.Module):
 
     def forward(self, g_arr, feat):
         for g, layer in zip(g_arr, self.layers):
-            u = g.edata['u'].to(feat.device)
+            u = g.edata['u']
             feat = self.pool(layer(g, feat, u).transpose(-1, -2).unsqueeze(0))\
                 .squeeze(0).transpose(-1, -2)
+            print(feat.shape)
+        print(g_arr[-1].batch_size)
         return self.cls(self.readout(g_arr[-1], feat))
 
 class ChebNet(nn.Module):
@@ -155,6 +156,7 @@ for epoch in range(10):
     for i, (g, x, y) in enumerate(train_loader):
         x = x.to(device)
         y = y.to(device)
+        g = [g_i.to(device) for g_i in g]
         out = model(g, x)
         hit += (out.max(-1)[1] == y).sum().item()
         tot += len(y)

examples/pytorch/monet/citation.py

@@ -65,16 +65,17 @@ def evaluate(model, features, pseudo, labels, mask):
 def main(args):
     # load and preprocess dataset
     data = load_data(args)
-    features = torch.FloatTensor(data.features)
-    labels = torch.LongTensor(data.labels)
-    if False: #hasattr(torch, 'BoolTensor'):
-        train_mask = torch.BoolTensor(data.train_mask)
-        val_mask = torch.BoolTensor(data.val_mask)
-        test_mask = torch.BoolTensor(data.test_mask)
+    g = data[0]
+    if args.gpu < 0:
+        cuda = False
     else:
-        train_mask = torch.ByteTensor(data.train_mask)
-        val_mask = torch.ByteTensor(data.val_mask)
-        test_mask = torch.ByteTensor(data.test_mask)
+        cuda = True
+        g = g.to(args.gpu)
+    features = g.ndata['feat']
+    labels = g.ndata['label']
+    train_mask = g.ndata['train_mask']
+    val_mask = g.ndata['val_mask']
+    test_mask = g.ndata['test_mask']
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
@@ -89,33 +90,12 @@ def main(args):
            val_mask.sum().item(),
            test_mask.sum().item()))
 
-    if args.gpu < 0:
-        cuda = False
-    else:
-        cuda = True
-        torch.cuda.set_device(args.gpu)
-        features = features.cuda()
-        labels = labels.cuda()
-        train_mask = train_mask.cuda()
-        val_mask = val_mask.cuda()
-        test_mask = test_mask.cuda()
-        print("use cuda:", args.gpu)
-
     # graph preprocess and calculate normalization factor
-    g = data.graph
-    g.remove_edges_from(nx.selfloop_edges(g))
-    g = DGLGraph(g)
+    g = g.remove_self_loop().add_self_loop()
     n_edges = g.number_of_edges()
-    us, vs = g.edges()
-    pseudo = []
-    for i in range(g.number_of_edges()):
-        pseudo.append([
-            1 / np.sqrt(g.in_degree(us[i])),
-            1 / np.sqrt(g.in_degree(vs[i]))
-        ])
-    pseudo = torch.Tensor(pseudo)
-    if cuda:
-        pseudo = pseudo.cuda()
+    us, vs = g.edges(order='eid')
+    udeg, vdeg = 1 / torch.sqrt(g.in_degrees(us).float()), 1 / torch.sqrt(g.in_degrees(vs).float())
+    pseudo = torch.cat([udeg.unsqueeze(1), vdeg.unsqueeze(1)], dim=1)
 
     # create GraphSAGE model
     model = MoNet(g,

examples/pytorch/ogb/ogbn-products/gat/main.py

@@ -51,10 +51,6 @@ class GAT(nn.Module):
                  activation,
                  dropout):
         super().__init__()
-        print(in_feats)
-        print(n_hidden)
-        print(n_classes)
-        print(n_layers)
         self.n_layers = n_layers
         self.n_hidden = n_hidden
         self.n_classes = n_classes
@@ -112,7 +108,7 @@ class GAT(nn.Module):
                 num_workers=args.num_workers)
 
             for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
-                block = blocks[0]
+                block = blocks[0].to(device)
 
                 h = x[input_nodes].to(device)
                 h_dst = h[:block.number_of_dst_nodes()]
@@ -205,6 +201,9 @@ def run(args, device, data):
         for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
             tic_step = time.time()
 
+            # copy block to gpu
+            blocks = [blk.to(device) for blk in blocks]
+
             # Load the input features as well as output labels
             batch_inputs, batch_labels = load_subtensor(g, labels, seeds, input_nodes, device)
 
@@ -272,16 +271,9 @@ if __name__ == '__main__':
     graph, labels = data[0]
     labels = labels[:, 0]
 
-    graph.readonly(False)
-    _, _, self_e = graph.edge_ids(th.arange(graph.number_of_nodes()), th.arange(graph.number_of_nodes()), return_uv=True)
     print('Total edges before adding self-loop {}'.format(graph.number_of_edges()))
-    # clean partial self-loop edges
-    graph.remove_edges(self_e)
-    # Add all self-loop edges
-    graph.add_edges(th.arange(graph.number_of_nodes()), th.arange(graph.number_of_nodes()))
+    graph = graph.remove_self_loop().add_self_loop()
     print('Total edges after adding self-loop {}'.format(graph.number_of_edges()))
-    graph.readonly(True)
-    graph = dgl.as_heterograph(graph)
 
     in_feats = graph.ndata['feat'].shape[1]
     n_classes = (labels.max() + 1).item()

examples/pytorch/ogb/ogbn-products/graphsage/main.py

@@ -81,7 +81,7 @@ class SAGE(nn.Module):
                 num_workers=args.num_workers)
 
             for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
-                block = blocks[0]
+                block = blocks[0].to(device)
 
                 h = x[input_nodes].to(device)
                 h_dst = h[:block.number_of_dst_nodes()]
@@ -174,6 +174,9 @@ def run(args, device, data):
         for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
             tic_step = time.time()
 
+            # copy block to gpu
+            blocks = [blk.to(device) for blk in blocks]
+
             # Load the input features as well as output labels
             batch_inputs, batch_labels = load_subtensor(g, labels, seeds, input_nodes, device)
 
@@ -240,8 +243,6 @@ if __name__ == '__main__':
     graph, labels = data[0]
     labels = labels[:, 0]
 
-    graph = dgl.as_heterograph(graph)
-
     in_feats = graph.ndata['feat'].shape[1]
     n_classes = (labels.max() + 1).item()
     prepare_mp(graph)

examples/pytorch/pinsage/sampler.py

@@ -54,7 +54,10 @@ class NeighborSampler(object):
                 if len(eids) > 0:
                     old_frontier = frontier
                     frontier = dgl.remove_edges(old_frontier, eids)
-                    frontier.edata['weights'] = old_frontier.edata['weights'][frontier.edata[dgl.EID]]
+                    #print(old_frontier)
+                    #print(frontier)
+                    #print(frontier.edata['weights'])
+                    #frontier.edata['weights'] = old_frontier.edata['weights'][frontier.edata[dgl.EID]]
             block = compact_and_copy(frontier, seeds)
             seeds = block.srcdata[dgl.NID]
             blocks.insert(0, block)

examples/pytorch/pointcloud/edgeconv/model.py

@@ -41,7 +41,7 @@ class Model(nn.Module):
         h = x
 
         for i in range(self.num_layers):
-            g = self.nng(h)
+            g = self.nng(h).to(h.device)
             h = h.view(batch_size * n_points, -1)
             h = self.conv[i](g, h)
             h = F.leaky_relu(h, 0.2)

examples/pytorch/pointcloud/pointnet/ShapeNet.py

@@ -18,7 +18,7 @@ class ShapeNet(object):
         if not os.path.exists(data_path):
             local_path = os.path.join(download_path, data_filename)
             if not os.path.exists(local_path):
-                download(SHAPENET_DOWNLOAD_URL, local_path)
+                download(SHAPENET_DOWNLOAD_URL, local_path, verify_ssl=False)
             with ZipFile(local_path) as z:
                 z.extractall(path=download_path)
 

examples/pytorch/pointcloud/pointnet/pointnet2.py

@@ -89,7 +89,7 @@ class FixedRadiusNNGraph(nn.Module):
             src_idx = inv_idx[:src.shape[0]]
             dst_idx = inv_idx[src.shape[0]:]
 
-            g = dgl.DGLGraph((src_idx.cpu(), dst_idx.cpu()), readonly=True)
+            g = dgl.graph((src_idx, dst_idx))
             g.ndata['pos'] = pos[i][uniq]
             g.ndata['center'] = center[uniq]
             if feat is not None:

examples/pytorch/pointcloud/pointnet/train_cls.py

@@ -37,7 +37,8 @@ download_path = os.path.join(get_download_dir(), data_filename)
 local_path = args.dataset_path or os.path.join(get_download_dir(), 'modelnet40_normal_resampled')
 
 if not os.path.exists(local_path):
-    download('https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip', download_path)
+    download('https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip',
+             download_path, verify_ssl=False)
     from zipfile import ZipFile
     with ZipFile(download_path) as z:
         z.extractall(path=get_download_dir())

examples/pytorch/rgcn-hetero/entity_classify.py

@@ -47,6 +47,7 @@ def main(args):
     use_cuda = args.gpu >= 0 and th.cuda.is_available()
     if use_cuda:
         th.cuda.set_device(args.gpu)
+        g = g.to('cuda:%d' % args.gpu)
         labels = labels.cuda()
         train_idx = train_idx.cuda()
         test_idx = test_idx.cuda()

examples/pytorch/rgcn-hetero/entity_classify_mb.py

@@ -23,18 +23,17 @@ def extract_embed(node_embed, input_nodes):
         emb[ntype] = node_embed[ntype][nid]
     return emb
 
-def evaluate(model, loader, node_embed, labels, category, use_cuda):
+def evaluate(model, loader, node_embed, labels, category, device):
     model.eval()
     total_loss = 0
     total_acc = 0
     count = 0
     for input_nodes, seeds, blocks in loader:
+        blocks = [blk.to(device) for blk in blocks]
         seeds = seeds[category]
         emb = extract_embed(node_embed, input_nodes)
-        lbl = labels[seeds]
-        if use_cuda:
-            emb = {k : e.cuda() for k, e in emb.items()}
-            lbl = lbl.cuda()
+        emb = {k : e.to(device) for k, e in emb.items()}
+        lbl = labels[seeds].to(device)
         logits = model(emb, blocks)[category]
         loss = F.cross_entropy(logits, lbl)
         acc = th.sum(logits.argmax(dim=1) == lbl).item()
@@ -71,9 +70,11 @@ def main(args):
         val_idx = train_idx
 
     # check cuda
+    device = 'cpu'
     use_cuda = args.gpu >= 0 and th.cuda.is_available()
     if use_cuda:
         th.cuda.set_device(args.gpu)
+        device = 'cuda:%d' % args.gpu
 
     train_label = labels[train_idx]
     val_label = labels[val_idx]
@@ -127,6 +128,7 @@ def main(args):
             t0 = time.time()
 
         for i, (input_nodes, seeds, blocks) in enumerate(loader):
+            blocks = [blk.to(device) for blk in blocks]
             seeds = seeds[category]     # we only predict the nodes with type "category"
             batch_tic = time.time()
             emb = extract_embed(node_embed, input_nodes)
@@ -146,7 +148,7 @@ def main(args):
         if epoch > 3:
             dur.append(time.time() - t0)
 
-        val_loss, val_acc = evaluate(model, val_loader, node_embed, labels, category, use_cuda)
+        val_loss, val_acc = evaluate(model, val_loader, node_embed, labels, category, device)
         print("Epoch {:05d} | Valid Acc: {:.4f} | Valid loss: {:.4f} | Time: {:.4f}".
               format(epoch, val_acc, val_loss, np.average(dur)))
     print()

examples/pytorch/rgcn-hetero/model.py

@@ -237,7 +237,7 @@ class EntityClassify(nn.Module):
                 num_workers=num_workers)
 
             for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
-                block = blocks[0]
+                block = blocks[0].to(device)
 
                 h = {k: x[k][input_nodes[k]].to(device) for k in input_nodes.keys()}
                 h = layer(block, h)

examples/pytorch/rgcn-hetero/test_classify.py

@@ -33,6 +33,7 @@ def main(args):
         th.cuda.set_device(args.gpu)
         labels = labels.cuda()
         test_idx = test_idx.cuda()
+        g = g.to('cuda:%d' % args.gpu)
 
     # create model
     model = EntityClassify(g,

examples/pytorch/rgcn/README.md

@@ -48,10 +48,6 @@ MUTAG: accuracy avg(5 runs) 67.06%, best 80.88% (DGL)
 python3 entity_classify_mp.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0 --batch-size 256 --use-self-loop --n-epochs 40 --dropout=0.3
 ```
 
-```
-python3 entity_classify_mp.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout 40 --n-epochs=40 --batch-size=128
-```
-
 BGS: accuracy avg(5 runs) 84.14%, best 89.66% (DGL)
 
 ```

examples/pytorch/rgcn/entity_classify.py

@@ -94,6 +94,7 @@ def main(args):
 
     if use_cuda:
         model.cuda()
+        g = g.to('cuda:%d' % args.gpu)
 
     # optimizer
     optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.l2norm)

examples/pytorch/rgcn/link_predict.py

@@ -161,6 +161,7 @@ def main(args):
             node_id, deg = node_id.cuda(), deg.cuda()
             edge_type, edge_norm = edge_type.cuda(), edge_norm.cuda()
             data, labels = data.cuda(), labels.cuda()
+            g = g.to(args.gpu)
 
         t0 = time.time()
         embed = model(g, node_id, edge_type, edge_norm)