[DataLoader] Allow batch_size=None for GraphDataLoader (#4483)

* overwrite default_collate_fn

* Update dataloader.py

* Update dataloader.py

* Update dataloader.py

* Update dataloader.py

* Update test_dataloader.py

* revert the test code being reverted in #4956

python/dgl/dataloading/dataloader.py

@@ -1124,17 +1124,15 @@ class GraphDataLoader(torch.utils.data.DataLoader):
             else:
                 dataloader_kwargs[k] = v
 
-        if collate_fn is None:
-            self.collate = GraphCollator(**collator_kwargs).collate
-        else:
-            self.collate = collate_fn
-
         self.use_ddp = use_ddp
         if use_ddp:
             self.dist_sampler = _create_dist_sampler(dataset, dataloader_kwargs, ddp_seed)
             dataloader_kwargs['sampler'] = self.dist_sampler
 
-        super().__init__(dataset=dataset, collate_fn=self.collate, **dataloader_kwargs)
+        if collate_fn is None and kwargs.get('batch_size', 1) is not None:
+            collate_fn = GraphCollator(**collator_kwargs).collate
+
+        super().__init__(dataset=dataset, collate_fn=collate_fn, **dataloader_kwargs)
 
     def set_epoch(self, epoch):
         """Sets the epoch number for the underlying sampler which ensures all replicas

tests/pytorch/test_dataloader.py

@@ -12,15 +12,21 @@ from test_utils import parametrize_idtype
 import pytest
 
 
-def test_graph_dataloader():
-    batch_size = 16
+@pytest.mark.parametrize('batch_size', [None, 16])
+def test_graph_dataloader(batch_size):
     num_batches = 2
-    minigc_dataset = dgl.data.MiniGCDataset(batch_size * num_batches, 10, 20)
+    num_samples = num_batches * (batch_size if batch_size is not None else 1)
+    minigc_dataset = dgl.data.MiniGCDataset(num_samples, 10, 20)
     data_loader = dgl.dataloading.GraphDataLoader(minigc_dataset, batch_size=batch_size, shuffle=True)
     assert isinstance(iter(data_loader), Iterator)
     for graph, label in data_loader:
         assert isinstance(graph, dgl.DGLGraph)
-        assert F.asnumpy(label).shape[0] == batch_size
+        if batch_size is not None:
+            assert F.asnumpy(label).shape[0] == batch_size
+        else:
+            # If batch size is None, the label element will be a single scalar following
+            # PyTorch's practice.
+            assert F.asnumpy(label).ndim == 0
 
 @unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
 @pytest.mark.parametrize('num_workers', [0, 4])

tests/pytorch/test_nn.py

@@ -1516,7 +1516,7 @@ def test_hgt(idtype, in_size, num_heads):
     sorted_y = m(sorted_g, sorted_x, sorted_ntype, sorted_etype, presorted=False)
     assert sorted_y.shape == (g.num_nodes(), head_size * num_heads)
     # mini-batch
-    train_idx = th.randint(0, 100, (10, ), dtype = idtype)
+    train_idx = th.randperm(100, dtype = idtype)[:10]
     sampler = dgl.dataloading.NeighborSampler([-1])
     train_loader = dgl.dataloading.DataLoader(g, train_idx.to(dev), sampler,
                                             batch_size=8, device=dev,