fix test dataloader (#3482)

Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

tests/pytorch/test_dataloader.py

@@ -217,8 +217,8 @@ def _check_device(data):
 def test_node_dataloader():
     sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
 
-    g1 = dgl.graph(([0, 0, 0, 1, 1], [1, 2, 3, 3, 4])).to(F.ctx())
-    g1.ndata['feat'] = F.copy_to(F.randn((5, 8)), F.ctx())
+    g1 = dgl.graph(([0, 0, 0, 1, 1], [1, 2, 3, 3, 4]))
+    g1.ndata['feat'] = F.copy_to(F.randn((5, 8)), F.cpu())
 
     dataloader = dgl.dataloading.NodeDataLoader(
         g1, g1.nodes(), sampler, device=F.ctx(), batch_size=g1.num_nodes())
@@ -232,9 +232,9 @@ def test_node_dataloader():
          ('user', 'followed-by', 'user'): ([1, 2, 3, 0, 2, 3, 0], [0, 0, 0, 1, 1, 1, 2]),
          ('user', 'play', 'game'): ([0, 1, 1, 3, 5], [0, 1, 2, 0, 2]),
          ('game', 'played-by', 'user'): ([0, 1, 2, 0, 2], [0, 1, 1, 3, 5])
-    }).to(F.ctx())
+    })
     for ntype in g2.ntypes:
-        g2.nodes[ntype].data['feat'] = F.copy_to(F.randn((g2.num_nodes(ntype), 8)), F.ctx())
+        g2.nodes[ntype].data['feat'] = F.copy_to(F.randn((g2.num_nodes(ntype), 8)), F.cpu())
     batch_size = max(g2.num_nodes(nty) for nty in g2.ntypes)
 
     dataloader = dgl.dataloading.NodeDataLoader(
@@ -250,8 +250,8 @@ def test_edge_dataloader():
     sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
     neg_sampler = dgl.dataloading.negative_sampler.Uniform(2)
 
-    g1 = dgl.graph(([0, 0, 0, 1, 1], [1, 2, 3, 3, 4])).to(F.ctx())
-    g1.ndata['feat'] = F.copy_to(F.randn((5, 8)), F.ctx())
+    g1 = dgl.graph(([0, 0, 0, 1, 1], [1, 2, 3, 3, 4]))
+    g1.ndata['feat'] = F.copy_to(F.randn((5, 8)), F.cpu())
 
     # no negative sampler
     dataloader = dgl.dataloading.EdgeDataLoader(
@@ -276,9 +276,9 @@ def test_edge_dataloader():
          ('user', 'followed-by', 'user'): ([1, 2, 3, 0, 2, 3, 0], [0, 0, 0, 1, 1, 1, 2]),
          ('user', 'play', 'game'): ([0, 1, 1, 3, 5], [0, 1, 2, 0, 2]),
          ('game', 'played-by', 'user'): ([0, 1, 2, 0, 2], [0, 1, 1, 3, 5])
-    }).to(F.ctx())
+    })
     for ntype in g2.ntypes:
-        g2.nodes[ntype].data['feat'] = F.copy_to(F.randn((g2.num_nodes(ntype), 8)), F.ctx())
+        g2.nodes[ntype].data['feat'] = F.copy_to(F.randn((g2.num_nodes(ntype), 8)), F.cpu())
     batch_size = max(g2.num_edges(ety) for ety in g2.canonical_etypes)
 
     # no negative sampler