[Example] Fix some example after graph refactor. (#1884)

* Update cluster GCN README

We do not need to build metis right now.
We can use builtin metis.

* Fix gcmc mxnet

* Fix graphwriter
Co-authored-by: Ubuntu <ubuntu@ip-172-31-68-185.ec2.internal>

examples/mxnet/gcmc/data.py

@@ -261,7 +261,7 @@ class MovieLens(object):
                 x = x.asnumpy().astype('float32')
                 x[x == 0.] = np.inf
                 x = mx.nd.array(1. / np.sqrt(x))
-                return x.as_in_context(self._ctx).expand_dims(1)
+                return x.expand_dims(1)
             user_ci = []
             user_cj = []
             movie_ci = []
@@ -282,8 +282,8 @@ class MovieLens(object):
                 user_cj = _calc_norm(mx.nd.add_n(*user_cj))
                 movie_cj = _calc_norm(mx.nd.add_n(*movie_cj))
             else:
-                user_cj = mx.nd.ones((self.num_user,), ctx=self._ctx)
-                movie_cj = mx.nd.ones((self.num_movie,), ctx=self._ctx)
+                user_cj = mx.nd.ones((self.num_user,))
+                movie_cj = mx.nd.ones((self.num_movie,))
             graph.nodes['user'].data.update({'ci' : user_ci, 'cj' : user_cj})
             graph.nodes['movie'].data.update({'ci' : movie_ci, 'cj' : movie_cj})
 

examples/mxnet/gcmc/train.py

@@ -105,6 +105,13 @@ def train(args):
     count_num = 0
     count_loss = 0
 
+    dataset.train_enc_graph = dataset.train_enc_graph.to(args.ctx)
+    dataset.train_dec_graph = dataset.train_dec_graph.to(args.ctx)
+    dataset.valid_enc_graph = dataset.train_enc_graph
+    dataset.valid_dec_graph = dataset.valid_dec_graph.to(args.ctx)
+    dataset.test_enc_graph = dataset.test_enc_graph.to(args.ctx)
+    dataset.test_dec_graph = dataset.test_dec_graph.to(args.ctx)
+
     print("Start training ...")
     dur = []
     for iter_idx in range(1, args.train_max_iter):

examples/pytorch/cluster_gcn/README.md

@@ -8,36 +8,10 @@ This repo reproduce the reported speed and performance maximally on Reddit and P
 Dependencies
 ------------
 - Python 3.7+(for string formatting features)
-- PyTorch 1.1.0+
-- metis
+- PyTorch 1.5.0+
 - sklearn
 
 
-* install clustering toolkit: metis and its Python interface.
-
-  download and install metis: http://glaros.dtc.umn.edu/gkhome/metis/metis/download
-
-  METIS - Serial Graph Partitioning and Fill-reducing Matrix Ordering ([official website](http://glaros.dtc.umn.edu/gkhome/metis/metis/overview))
-
-```
-1) Download metis-5.1.0.tar.gz from http://glaros.dtc.umn.edu/gkhome/metis/metis/download and unpack it
-2) cd metis-5.1.0
-3) make config shared=1 prefix=~/.local/
-4) make install
-5) export METIS_DLL=~/.local/lib/libmetis.so
-6) `pip install metis`
-```
-
-quick test to see whether you install metis correctly:
-
-```
->>> import networkx as nx
->>> import metis
->>> G = metis.example_networkx()
->>> (edgecuts, parts) = metis.part_graph(G, 3)
-```
-
-
 ## Run Experiments.
 * For reddit data, you may run the following scripts
 

examples/pytorch/graphsage/train_sampling_multi_gpu.py

@@ -98,8 +98,8 @@ def prepare_mp(g):
 
     This is a workaround before full shared memory support on heterogeneous graphs.
     """
-    g.in_degree(0)
-    g.out_degree(0)
+    g.in_degrees(0)
+    g.out_degrees(0)
     g.find_edges([0])
 
 def compute_acc(pred, labels):

examples/pytorch/graphwriter/modules.py

@@ -148,6 +148,7 @@ class GraphTrans(nn.Module):
 
     def forward(self, ent, ent_mask, ent_len, rel, rel_mask, graphs):
         device = ent.device
+        graphs = graphs.to(device)
         ent_mask = (ent_mask==0) # reverse mask
         rel_mask = (rel_mask==0)
         init_h = []