Merge branch 'master' into 0.5.x

Author: BarclayII

examples/pytorch/rgcn/experimental/partition_graph.py

@@ -50,7 +50,7 @@ def load_ogb(dataset, global_norm):
             if ntype == category:
                 category_id = i
 
-        g = dgl.to_homo(hg)
+        g = dgl.to_homogeneous(hg, edata=['norm'])
         if global_norm:
             u, v, eid = g.all_edges(form='all')
             _, inverse_index, count = th.unique(v, return_inverse=True, return_counts=True)

python/dgl/dataloading/dataloader.py

@@ -8,6 +8,7 @@
 from .. import backend as F
 from .. import utils
 from ..convert import heterograph
+from ..distributed.dist_graph import DistGraph
 
 # pylint: disable=unused-argument
 def assign_block_eids(block, frontier):
@@ -244,6 +245,7 @@ def sample_blocks(self, g, seed_nodes, exclude_eids=None):
                 assign_block_eids(block, frontier)
 
             seed_nodes = {ntype: block.srcnodes[ntype].data[NID] for ntype in block.srctypes}
+
             # Pre-generate CSR format so that it can be used in training directly
             block.create_formats_()
             blocks.insert(0, block)
@@ -309,6 +311,7 @@ class NodeCollator(Collator):
     """
     def __init__(self, g, nids, block_sampler):
         self.g = g
+        self._is_distributed = isinstance(g, DistGraph)
         if not isinstance(nids, Mapping):
             assert len(g.ntypes) == 1, \
                 "nids should be a dict of node type and ids for graph with multiple node types"
@@ -352,6 +355,15 @@ def collate(self, items):
         if isinstance(items[0], tuple):
             # returns a list of pairs: group them by node types into a dict
             items = utils.group_as_dict(items)
+
+        # TODO(BarclayII) Because DistGraph doesn't have idtype and device implemented,
+        # this function does not work.  I'm again skipping this step as a workaround.
+        # We need to fix this.
+        if not self._is_distributed:
+            if isinstance(items, dict):
+                items = utils.prepare_tensor_dict(self.g, items, 'items')
+            else:
+                items = utils.prepare_tensor(self.g, items, 'items')
         blocks = self.block_sampler.sample_blocks(self.g, items)
         output_nodes = blocks[-1].dstdata[NID]
         input_nodes = blocks[0].srcdata[NID]
@@ -559,10 +571,11 @@ def dataset(self):
 
     def _collate(self, items):
         if isinstance(items[0], tuple):
+            # returns a list of pairs: group them by node types into a dict
             items = utils.group_as_dict(items)
-            items = {k: F.zerocopy_from_numpy(np.asarray(v)) for k, v in items.items()}
+            items = utils.prepare_tensor_dict(self.g_sampling, items, 'items')
         else:
-            items = F.zerocopy_from_numpy(np.asarray(items))
+            items = utils.prepare_tensor(self.g_sampling, items, 'items')
 
         pair_graph = self.g.edge_subgraph(items)
         seed_nodes = pair_graph.ndata[NID]
@@ -582,10 +595,11 @@ def _collate(self, items):
 
     def _collate_with_negative_sampling(self, items):
         if isinstance(items[0], tuple):
+            # returns a list of pairs: group them by node types into a dict
             items = utils.group_as_dict(items)
-            items = {k: F.zerocopy_from_numpy(np.asarray(v)) for k, v in items.items()}
+            items = utils.prepare_tensor_dict(self.g_sampling, items, 'items')
         else:
-            items = F.zerocopy_from_numpy(np.asarray(items))
+            items = utils.prepare_tensor(self.g_sampling, items, 'items')
 
         pair_graph = self.g.edge_subgraph(items, preserve_nodes=True)
         induced_edges = pair_graph.edata[EID]