[Examples] Run all the examples (#623)

* all pytorch examples

* scan through mxnet examples

* change reddit data

* tweak numerical range for unittest

* fix ci

* fix ci

* fix

* add seed to workaround

CMakeLists.txt

@@ -23,6 +23,7 @@ endif()
 # and add set(OPTION VALUE) to override these build options.
 # Alernatively, use cmake -DOPTION=VALUE through command-line.
 dgl_option(USE_CUDA "Build with CUDA" OFF)
+dgl_option(USE_OPENMP "Build with OpenMP" ON)
 dgl_option(BUILD_CPP_TEST "Build cpp unittest executables" OFF)
 
 if(USE_CUDA)

examples/mxnet/sampling/train.py

@@ -52,7 +52,7 @@ def main(args):
               n_test_samples))
 
     # create GCN model
-    g = DGLGraph(data.graph, readonly=True)
+    g = data.graph
     g.ndata['features'] = features
     g.ndata['labels'] = labels
 

examples/mxnet/sse/README.md

@@ -23,7 +23,6 @@ DGLBACKEND=mxnet python3 sse_batch.py --graph-file ../../data/5_5_csr.nd \
                                       --lr 0.0005 \
                                       --batch-size 1024 \
                                       --use-spmv \
-                                      --dgl \
                                       --num-parallel-subgraphs 32 \
                                       --gpu 1 \
                                       --num-feats 100 \
@@ -38,7 +37,6 @@ DGLBACKEND=mxnet python3 sse_batch.py --dataset "pubmed" \
                                       --n-epochs 1000 \
                                       --lr 0.001 \
                                       --batch-size 30 \
-                                      --dgl \
                                       --use-spmv \
                                       --neigh-expand 8 \
                                       --n-hidden 16

examples/mxnet/tree_lstm/README.md

@@ -21,7 +21,7 @@ The script will download the [SST dataset] (http://nlp.stanford.edu/sentiment/in
 
 ## Usage
 ```
-python3 train.py --gpu 0
+DGLBACKEND=mxnet python3 train.py --gpu 0
 ```
 
 ## Speed Test

examples/pytorch/appnp/appnp.py

@@ -33,7 +33,7 @@ class GraphPropagation(nn.Module):
             self.g.ndata['h'] = h
             if self.edge_drop:
                 # performing edge dropout
-                ed = self.edge_drop(torch.ones((self.g.number_of_edges(), 1)))
+                ed = self.edge_drop(torch.ones((self.g.number_of_edges(), 1), device=h.device))
                 self.g.edata['d'] = ed
                 self.g.update_all(fn.src_mul_edge(src='h', edge='d', out='m'),
                                   fn.sum(msg='m', out='h'))

python/dgl/data/reddit.py

@@ -4,7 +4,6 @@ import scipy.sparse as sp
 import numpy as np
 import dgl
 import os, sys
-from ..graph_index import create_graph_index
 from .utils import download, extract_archive, get_download_dir, _get_dgl_url
 
 
@@ -20,7 +19,7 @@ class RedditDataset(object):
         extract_archive(zip_file_path, extract_dir)
         # graph
         coo_adj = sp.load_npz(os.path.join(extract_dir, "reddit{}_graph.npz".format(self_loop_str)))
-        self.graph = create_graph_index(coo_adj, readonly=True)
+        self.graph = dgl.DGLGraph(coo_adj, readonly=True)
         # features and labels
         reddit_data = np.load(os.path.join(extract_dir, "reddit_data.npz"))
         self.features = reddit_data["feature"]

tests/compute/test_kernel.py

 import dgl
 import dgl.function as fn
 import networkx as nx
+import numpy as np
 import backend as F
 from itertools import product
 
+np.random.seed(42)
 
 def udf_copy_src(edges):
     return {'m': edges.src['u']}
@@ -36,21 +38,21 @@ def generate_feature(g, broadcast='none'):
     nv = g.number_of_nodes()
     ne = g.number_of_edges()
     if broadcast == 'e':
-        u = F.randn((nv, D1, D2, D3))
-        e = F.randn((ne, D2, 1))
-        v = F.randn((nv, D1, D2, D3))
+        u = F.tensor(np.random.randn(nv, D1, D2, D3) + 1)
+        e = F.tensor(np.random.randn(ne, D2, 1) - 1)
+        v = F.tensor(np.random.randn(nv, D1, D2, D3))
     elif broadcast == 'u':
-        u = F.randn((nv, D2, 1))
-        e = F.randn((ne, D1, D2, D3))
-        v = F.randn((nv, D1, D2, D3))
+        u = F.tensor(np.random.randn(nv, D2, 1) + 1)
+        e = F.tensor(np.random.randn(ne, D1, D2, D3) - 1)
+        v = F.tensor(np.random.randn(nv, D1, D2, D3))
     elif broadcast == 'v':
-        u = F.randn((nv, D1, D2, D3))
-        e = F.randn((ne, D1, D2, D3))
-        v = F.randn((nv, D2, 1))
+        u = F.tensor(np.random.randn(nv, D1, D2, D3) + 1)
+        e = F.tensor(np.random.randn(ne, D1, D2, D3) - 1)
+        v = F.tensor(np.random.randn(nv, D2, 1))
     else:
-        u = F.randn((nv, D1, D2, D3))
-        e = F.randn((ne, D1, D2, D3))
-        v = F.randn((nv, D1, D2, D3))
+        u = F.tensor(np.random.randn(nv, D1, D2, D3) + 1)
+        e = F.tensor(np.random.randn(ne, D1, D2, D3) - 1)
+        v = F.tensor(np.random.randn(nv, D1, D2, D3))
     return u, v, e
 
 
@@ -175,16 +177,10 @@ def test_all_binary_builtins():
         with F.record_grad():
             g.update_all(mfunc, rfunc)
             r2 = g.ndata['r2']
-            F.backward(r2.sum())
+            F.backward(r2.sum(), F.tensor([1.]))
             lhs_grad_2 = F.grad(target_feature_switch(g, lhs))
             rhs_grad_2 = F.grad(target_feature_switch(g, rhs))
 
-        def _print_error(a, b):
-            print("Test {}_{}_{}_{} {}".
-                  format(lhs, binary_op, rhs, reducer, broadcast))
-            print(a)
-            print(b)
-
         if reducer == 'prod':
             rtol = 1e-2
             atol = 1e-2
@@ -192,13 +188,23 @@ def test_all_binary_builtins():
             rtol = 1e-4
             atol = 1e-4
 
+        def _print_error(a, b):
+            print("ERROR: Test {}_{}_{}_{} {}".
+                  format(lhs, binary_op, rhs, reducer, broadcast))
+            print(a, b)
+            for i, (x, y) in enumerate(zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())):
+                if not np.allclose(x, y, rtol, atol):
+                    print('@{} {} v.s. {}'.format(i, x, y))
+
         if not F.allclose(r1, r2, rtol, atol):
             _print_error(r1, r2)
         assert F.allclose(r1, r2, rtol, atol)
-        if not F.allclose(rhs_grad_1, rhs_grad_2, rtol, atol):
+
+        if not F.allclose(lhs_grad_1, lhs_grad_2, rtol, atol):
             print("left grad")
             _print_error(lhs_grad_1, lhs_grad_2)
         assert(F.allclose(lhs_grad_1, lhs_grad_2, rtol, atol))
+
         if not F.allclose(rhs_grad_1, rhs_grad_2, rtol, atol):
             print("right grad")
             _print_error(rhs_grad_1, rhs_grad_2)
@@ -224,7 +230,6 @@ def test_all_binary_builtins():
                 for broadcast in ["none", lhs, rhs]:
                     _test(g, lhs, rhs, binary_op, reducer)
 
-
 if __name__ == '__main__':
     test_copy_src_reduce()
     test_copy_edge_reduce()