[Bugfix] Fix dataloader pytorch cuda indexing (#4297)

* Modify to repro crash

* Revert to orig. scenario and add fix

* Update
Co-authored-by: Xin Yao <xiny@nvidia.com>

examples/pytorch/rgcn-hetero-ogbn-mag/main.py

@@ -39,7 +39,7 @@ def train(
         model_optimizer.zero_grad()
 
         in_nodes = {rel: nid.to(device) for rel, nid in in_nodes.items()}
-        out_nodes = out_nodes[predict_category].to(device)
+        out_nodes = out_nodes[predict_category].to(labels.device)
         blocks = [block.to(device) for block in blocks]
 
         batch_labels = labels[out_nodes].to(device)
@@ -102,7 +102,7 @@ def validate(
             for step, (in_nodes, out_nodes, blocks) in enumerate(dataloader):
                 in_nodes = {rel: nid.to(device)
                             for rel, nid in in_nodes.items()}
-                out_nodes = out_nodes[predict_category].to(device)
+                out_nodes = out_nodes[predict_category].to(labels.device)
                 blocks = [block.to(device) for block in blocks]
 
                 batch_labels = labels[out_nodes].to(device)

python/dgl/dataloading/dataloader.py

@@ -157,7 +157,7 @@ class TensorizedDataset(torch.utils.data.IterableDataset):
 
     def __iter__(self):
         indices = _divide_by_worker(self._indices, self.batch_size, self.drop_last)
-        id_tensor = self._id_tensor[indices.to(self._device)]
+        id_tensor = self._id_tensor[indices]
         return _TensorizedDatasetIter(
             id_tensor, self.batch_size, self.drop_last, self._mapping_keys, self._shuffle)
 
@@ -223,12 +223,7 @@ class DDPTensorizedDataset(torch.utils.data.IterableDataset):
         """Shuffles the dataset."""
         # Only rank 0 does the actual shuffling.  The other ranks wait for it.
         if self.rank == 0:
-            if self._device == torch.device('cpu'):
-                np.random.shuffle(self._indices[:self.num_indices].numpy())
-            else:
-                self._indices[:self.num_indices] = self._indices[
-                    torch.randperm(self.num_indices, device=self._indices.device)]
-
+            np.random.shuffle(self._indices[:self.num_indices].numpy())
             if not self.drop_last:
                 # pad extra
                 self._indices[self.num_indices:] = \
@@ -239,7 +234,7 @@ class DDPTensorizedDataset(torch.utils.data.IterableDataset):
         start = self.num_samples * self.rank
         end = self.num_samples * (self.rank + 1)
         indices = _divide_by_worker(self._indices[start:end], self.batch_size, self.drop_last)
-        id_tensor = self._id_tensor[indices.to(self._device)]
+        id_tensor = self._id_tensor[indices]
         return _TensorizedDatasetIter(
             id_tensor, self.batch_size, self.drop_last, self._mapping_keys, self._shuffle)