rename create_format_ to create_formats_ (#2126)

docs/source/guide/minibatch-link.rst

@@ -240,7 +240,7 @@ source-destination array pairs. An example is given as follows:
     
     dataloader = dgl.dataloading.EdgeDataLoader(
         g, train_eid_dict, sampler,
-        negative_sampler=negative_sampler=NegativeSampler(g, 5),
+        negative_sampler=NegativeSampler(g, 5),
         batch_size=1024,
         shuffle=True,
         drop_last=False,

examples/pytorch/gcmc/train_sampling.py

@@ -403,8 +403,8 @@ if __name__ == '__main__':
         run(0, n_gpus, args, devices, dataset)
     # multi gpu
     else:
-        dataset.train_enc_graph.create_format_()
-        dataset.train_dec_graph.create_format_()
+        dataset.train_enc_graph.create_formats_()
+        dataset.train_dec_graph.create_formats_()
         procs = []
         for proc_id in range(n_gpus):
             p = mp.Process(target=thread_wrapped_func(run), args=(proc_id, n_gpus, args, devices, dataset))

examples/pytorch/graphsage/train_cv.py

@@ -317,7 +317,7 @@ if __name__ == '__main__':
     train_mask = g.ndata['train_mask']
     val_mask = g.ndata['val_mask']
     g.ndata['features'] = features
-    g.create_format_()
+    g.create_formats_()
     # Pack data
     data = train_mask, val_mask, in_feats, labels, n_classes, g
 

examples/pytorch/graphsage/train_cv_multi_gpu.py

@@ -384,7 +384,7 @@ if __name__ == '__main__':
     g.ndata['features'] = features.share_memory_()
     create_history_storage(g, args, n_classes)
 
-    g.create_format_()
+    g.create_formats_()
     # Pack data
     data = train_mask, val_mask, in_feats, labels, n_classes, g
 

examples/pytorch/graphsage/train_sampling.py

@@ -229,9 +229,9 @@ if __name__ == '__main__':
     else:
         train_g = val_g = test_g = g
 
-    train_g.create_format_()
-    val_g.create_format_()
-    test_g.create_format_()
+    train_g.create_formats_()
+    val_g.create_formats_()
+    test_g.create_formats_()
     # Pack data
     data = in_feats, n_classes, train_g, val_g, test_g
 

examples/pytorch/graphsage/train_sampling_multi_gpu.py

@@ -258,9 +258,9 @@ if __name__ == '__main__':
     else:
         train_g = val_g = test_g = g
 
-    train_g.create_format_()
-    val_g.create_format_()
-    test_g.create_format_()
+    train_g.create_formats_()
+    val_g.create_formats_()
+    test_g.create_formats_()
     # Pack data
     data = in_feats, n_classes, train_g, val_g, test_g
 

examples/pytorch/graphsage/train_sampling_unsupervised.py

@@ -298,7 +298,7 @@ def main(args, devices):
     val_mask = g.ndata['val_mask']
     test_mask = g.ndata['test_mask']
     g.ndata['features'] = features
-    g.create_format_()
+    g.create_formats_()
     # Pack data
     data = train_mask, val_mask, test_mask, in_feats, labels, n_classes, g
 

examples/pytorch/ogb/cluster-sage/sampler.py

@@ -67,5 +67,5 @@ def subgraph_collate_fn(g, batch):
     g1.ndata['feat'] = g.ndata['feat'][nid]
     g1.ndata['labels'] = g.ndata['labels'][nid]
     g1.ndata['train_mask'] = g.ndata['train_mask'][nid]
-    g1.create_format_()
+    g1.create_formats_()
     return g1

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

@@ -243,7 +243,7 @@ if __name__ == '__main__':
 
     in_feats = graph.ndata['feat'].shape[1]
     n_classes = (labels.max() + 1).item()
-    graph.create_format_()
+    graph.create_formats_()
     # Pack data
     data = train_idx, val_idx, test_idx, in_feats, labels, n_classes, graph, args.head
 

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

@@ -234,7 +234,7 @@ if __name__ == '__main__':
 
     in_feats = graph.ndata['feat'].shape[1]
     n_classes = (labels.max() + 1).item()
-    graph.create_format_()
+    graph.create_formats_()
     # Pack data
     data = train_idx, val_idx, test_idx, in_feats, labels, n_classes, graph
 

python/dgl/dataloading/dataloader.py

@@ -245,7 +245,7 @@ class BlockSampler(object):
 
             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_format_()
+            block.create_formats_()
             blocks.insert(0, block)
         return blocks
 

python/dgl/heterograph.py

@@ -5241,7 +5241,7 @@ class DGLHeteroGraph(object):
             ret._graph = self._graph.formats(formats)
             return ret
 
-    def create_format_(self):
+    def create_formats_(self):
         r"""Create all sparse matrices allowed for the graph.
 
         By default, we create sparse matrices for a graph only when necessary.
@@ -5261,7 +5261,7 @@ class DGLHeteroGraph(object):
         >>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
         >>> g.format()
         {'created': ['coo'], 'not created': ['csr', 'csc']}
-        >>> g.create_format_()
+        >>> g.create_formats_()
         >>> g.format()
         {'created': ['coo', 'csr', 'csc'], 'not created': []}
 
@@ -5275,14 +5275,14 @@ class DGLHeteroGraph(object):
         ...     })
         >>> g.format()
         {'created': ['coo'], 'not created': ['csr', 'csc']}
-        >>> g.create_format_()
+        >>> g.create_formats_()
         >>> g.format()
         {'created': ['coo', 'csr', 'csc'], 'not created': []}
         """
         if self.num_edges() == 0:
             return 0
 
-        return self._graph.create_format_()
+        return self._graph.create_formats_()
 
     def astype(self, idtype):
         """Cast this graph to use another ID type.

python/dgl/heterograph_index.py

@@ -909,7 +909,7 @@ class HeteroGraphIndex(ObjectBase):
                 formats = [formats]
             return _CAPI_DGLHeteroGetFormatGraph(self, formats)
 
-    def create_format_(self):
+    def create_formats_(self):
         """Create all sparse matrices allowed for the graph."""
         return _CAPI_DGLHeteroCreateFormat(self)
 

tests/compute/test_heterograph.py

@@ -1714,7 +1714,7 @@ def test_format(idtype):
     assert g.formats()['created'] == ['coo']
     g1 = g.formats(['coo', 'csr', 'csc'])
     assert len(g1.formats()['created']) + len(g1.formats()['not created']) == 3
-    g1.create_format_()
+    g1.create_formats_()
     assert len(g1.formats()['created']) == 3
     assert g.formats()['created'] == ['coo']