[Test] Lower allclose precision (#129)

tests/pytorch/test_basics.py

@@ -2,6 +2,7 @@ import torch as th
 from torch.autograd import Variable
 import numpy as np
 from dgl.graph import DGLGraph
+import utils as U
 
 D = 5
 reduce_msg_shapes = set()
@@ -49,7 +50,7 @@ def test_batch_setter_getter():
     g = generate_graph()
     # set all nodes
     g.ndata['h'] = th.zeros((10, D))
-    assert th.allclose(g.ndata['h'], th.zeros((10, D)))
+    assert U.allclose(g.ndata['h'], th.zeros((10, D)))
     # pop nodes
     old_len = len(g.ndata)
     assert _pfc(g.pop_n_repr('h')) == [0.] * 10
@@ -175,12 +176,12 @@ def test_apply_edges():
     g.register_apply_edge_func(_upd)
     old = g.edata['w']
     g.apply_edges()
-    assert th.allclose(old * 2, g.edata['w'])
+    assert U.allclose(old * 2, g.edata['w'])
     u = th.tensor([0, 0, 0, 4, 5, 6])
     v = th.tensor([1, 2, 3, 9, 9, 9])
     g.apply_edges(lambda edges : {'w' : edges.data['w'] * 0.}, (u, v))
     eid = g.edge_ids(u, v)
-    assert th.allclose(g.edata['w'][eid], th.zeros((6, D)))
+    assert U.allclose(g.edata['w'][eid], th.zeros((6, D)))
 
 def test_update_routines():
     g = generate_graph()
@@ -232,8 +233,8 @@ def test_reduce_0deg():
     g.update_all(_message, _reduce)
     new_repr = g.ndata['h']
 
-    assert th.allclose(new_repr[1:], old_repr[1:])
-    assert th.allclose(new_repr[0], old_repr.sum(0), rtol=1e-3, atol=1e-3)
+    assert U.allclose(new_repr[1:], old_repr[1:])
+    assert U.allclose(new_repr[0], old_repr.sum(0))
 
 def test_pull_0deg():
     g = DGLGraph()
@@ -248,19 +249,19 @@ def test_pull_0deg():
 
     g.pull(0, _message, _reduce)
     new_repr = g.ndata['h']
-    assert th.allclose(new_repr[0], old_repr[0])
-    assert th.allclose(new_repr[1], old_repr[1])
+    assert U.allclose(new_repr[0], old_repr[0])
+    assert U.allclose(new_repr[1], old_repr[1])
 
     g.pull(1, _message, _reduce)
     new_repr = g.ndata['h']
-    assert th.allclose(new_repr[1], old_repr[0])
+    assert U.allclose(new_repr[1], old_repr[0])
 
     old_repr = th.randn(2, 5)
     g.ndata['h'] = old_repr
     g.pull([0, 1], _message, _reduce)
     new_repr = g.ndata['h']
-    assert th.allclose(new_repr[0], old_repr[0])
-    assert th.allclose(new_repr[1], old_repr[0])
+    assert U.allclose(new_repr[0], old_repr[0])
+    assert U.allclose(new_repr[1], old_repr[0])
 
 def _disabled_test_send_twice():
     # TODO(minjie): please re-enable this unittest after the send code problem is fixed.
@@ -281,14 +282,14 @@ def _disabled_test_send_twice():
     g.send((0, 1), _message_b)
     g.recv(1, _reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], old_repr[0] * 3)
+    assert U.allclose(new_repr[1], old_repr[0] * 3)
 
     g.ndata['a'] = old_repr
     g.send((0, 1), _message_a)
     g.send((2, 1), _message_b)
     g.recv(1, _reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], th.stack([old_repr[0], old_repr[2] * 3], 0).max(0)[0])
+    assert U.allclose(new_repr[1], th.stack([old_repr[0], old_repr[2] * 3], 0).max(0)[0])
 
 def test_send_multigraph():
     g = DGLGraph(multigraph=True)
@@ -315,14 +316,14 @@ def test_send_multigraph():
     g.send([0, 2], message_func=_message_a)
     g.recv(1, _reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], answer(old_repr[0], old_repr[2]))
+    assert U.allclose(new_repr[1], answer(old_repr[0], old_repr[2]))
 
     g.ndata['a'] = th.zeros(3, 5)
     g.edata['a'] = old_repr
     g.send([0, 2, 3], message_func=_message_a)
     g.recv(1, _reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], answer(old_repr[0], old_repr[2], old_repr[3]))
+    assert U.allclose(new_repr[1], answer(old_repr[0], old_repr[2], old_repr[3]))
 
     # send on multigraph
     g.ndata['a'] = th.zeros(3, 5)
@@ -330,7 +331,7 @@ def test_send_multigraph():
     g.send(([0, 2], [1, 1]), _message_a)
     g.recv(1, _reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], old_repr.max(0)[0])
+    assert U.allclose(new_repr[1], old_repr.max(0)[0])
 
     # consecutive send and send_on
     g.ndata['a'] = th.zeros(3, 5)
@@ -339,7 +340,7 @@ def test_send_multigraph():
     g.send([0, 1], message_func=_message_b)
     g.recv(1, _reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], answer(old_repr[0] * 3, old_repr[1] * 3, old_repr[3]))
+    assert U.allclose(new_repr[1], answer(old_repr[0] * 3, old_repr[1] * 3, old_repr[3]))
 
     # consecutive send_on
     g.ndata['a'] = th.zeros(3, 5)
@@ -348,15 +349,15 @@ def test_send_multigraph():
     g.send(1, message_func=_message_b)
     g.recv(1, _reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], answer(old_repr[0], old_repr[1] * 3))
+    assert U.allclose(new_repr[1], answer(old_repr[0], old_repr[1] * 3))
 
     # send_and_recv_on
     g.ndata['a'] = th.zeros(3, 5)
     g.edata['a'] = old_repr
     g.send_and_recv([0, 2, 3], message_func=_message_a, reduce_func=_reduce)
     new_repr = g.ndata['a']
-    assert th.allclose(new_repr[1], answer(old_repr[0], old_repr[2], old_repr[3]))
-    assert th.allclose(new_repr[[0, 2]], th.zeros(2, 5))
+    assert U.allclose(new_repr[1], answer(old_repr[0], old_repr[2], old_repr[3]))
+    assert U.allclose(new_repr[[0, 2]], th.zeros(2, 5))
 
 def test_dynamic_addition():
     N = 3

tests/pytorch/test_batched_graph.py

@@ -2,6 +2,7 @@ import networkx as nx
 import dgl
 import torch as th
 import numpy as np
+import utils as U
 
 def tree1():
     """Generate a tree
@@ -57,10 +58,10 @@ def test_batch_unbatch():
     assert bg.batch_num_edges == [4, 4]
 
     tt1, tt2 = dgl.unbatch(bg)
-    assert th.allclose(t1.ndata['h'], tt1.ndata['h'])
-    assert th.allclose(t1.edata['h'], tt1.edata['h'])
-    assert th.allclose(t2.ndata['h'], tt2.ndata['h'])
-    assert th.allclose(t2.edata['h'], tt2.edata['h'])
+    assert U.allclose(t1.ndata['h'], tt1.ndata['h'])
+    assert U.allclose(t1.edata['h'], tt1.edata['h'])
+    assert U.allclose(t2.ndata['h'], tt2.ndata['h'])
+    assert U.allclose(t2.edata['h'], tt2.edata['h'])
 
 def test_batch_unbatch1():
     t1 = tree1()
@@ -74,12 +75,12 @@ def test_batch_unbatch1():
     assert b2.batch_num_edges == [4, 4, 4]
 
     s1, s2, s3 = dgl.unbatch(b2)
-    assert th.allclose(t2.ndata['h'], s1.ndata['h'])
-    assert th.allclose(t2.edata['h'], s1.edata['h'])
-    assert th.allclose(t1.ndata['h'], s2.ndata['h'])
-    assert th.allclose(t1.edata['h'], s2.edata['h'])
-    assert th.allclose(t2.ndata['h'], s3.ndata['h'])
-    assert th.allclose(t2.edata['h'], s3.edata['h'])
+    assert U.allclose(t2.ndata['h'], s1.ndata['h'])
+    assert U.allclose(t2.edata['h'], s1.edata['h'])
+    assert U.allclose(t1.ndata['h'], s2.ndata['h'])
+    assert U.allclose(t1.edata['h'], s2.edata['h'])
+    assert U.allclose(t2.ndata['h'], s3.ndata['h'])
+    assert U.allclose(t2.edata['h'], s3.edata['h'])
 
 def test_batch_sendrecv():
     t1 = tree1()

tests/pytorch/test_filter.py

 import torch as th
 import numpy as np
 from dgl.graph import DGLGraph
+import utils as U
 
 def test_filter():
     g = DGLGraph()

tests/pytorch/test_frame.py

@@ -3,6 +3,7 @@ from torch.autograd import Variable
 import numpy as np
 from dgl.frame import Frame, FrameRef
 from dgl.utils import Index, toindex
+import utils as U
 
 N = 10
 D = 5
@@ -43,9 +44,9 @@ def test_column1():
     f = Frame(data)
     assert f.num_rows == N
     assert len(f) == 3
-    assert th.allclose(f['a1'].data, data['a1'].data)
+    assert U.allclose(f['a1'].data, data['a1'].data)
     f['a1'] = data['a2']
-    assert th.allclose(f['a2'].data, data['a2'].data)
+    assert U.allclose(f['a2'].data, data['a2'].data)
     # add a different length column should fail
     def failed_add_col():
         f['a4'] = th.zeros([N+1, D])
@@ -68,10 +69,10 @@ def test_column2():
     f = FrameRef(data, [3, 4, 5, 6, 7])
     assert f.num_rows == 5
     assert len(f) == 3
-    assert th.allclose(f['a1'], data['a1'].data[3:8])
+    assert U.allclose(f['a1'], data['a1'].data[3:8])
     # set column should reflect on the referenced data
     f['a1'] = th.zeros([5, D])
-    assert th.allclose(data['a1'].data[3:8], th.zeros([5, D]))
+    assert U.allclose(data['a1'].data[3:8], th.zeros([5, D]))
     # add new partial column should fail with error initializer
     f.set_initializer(lambda shape, dtype : assert_(False))
     def failed_add_col():
@@ -90,7 +91,7 @@ def test_append1():
     c1 = f1['a1']
     assert c1.data.shape == (2 * N, D)
     truth = th.cat([data['a1'], data['a1']])
-    assert th.allclose(truth, c1.data)
+    assert U.allclose(truth, c1.data)
     # append dict of different length columns should fail
     f3 = {'a1' : th.zeros((3, D)), 'a2' : th.zeros((3, D)), 'a3' : th.zeros((2, D))}
     def failed_append():
@@ -129,13 +130,13 @@ def test_row1():
     rows = f[rowid]
     for k, v in rows.items():
         assert v.shape == (len(rowid), D)
-        assert th.allclose(v, data[k][rowid])
+        assert U.allclose(v, data[k][rowid])
     # test duplicate keys
     rowid = Index(th.tensor([8, 2, 2, 1]))
     rows = f[rowid]
     for k, v in rows.items():
         assert v.shape == (len(rowid), D)
-        assert th.allclose(v, data[k][rowid])
+        assert U.allclose(v, data[k][rowid])
 
     # setter
     rowid = Index(th.tensor([0, 2, 4]))
@@ -145,7 +146,7 @@ def test_row1():
             }
     f[rowid] = vals
     for k, v in f[rowid].items():
-        assert th.allclose(v, th.zeros((len(rowid), D)))
+        assert U.allclose(v, th.zeros((len(rowid), D)))
 
     # setting rows with new column should raise error with error initializer
     f.set_initializer(lambda shape, dtype : assert_(False))
@@ -165,13 +166,13 @@ def test_row2():
     rowid = Index(th.tensor([0, 2]))
     rows = f[rowid]
     rows['a1'].backward(th.ones((len(rowid), D)))
-    assert th.allclose(c1.grad[:,0], th.tensor([1., 0., 1., 0., 0., 0., 0., 0., 0., 0.]))
+    assert U.allclose(c1.grad[:,0], th.tensor([1., 0., 1., 0., 0., 0., 0., 0., 0., 0.]))
     c1.grad.data.zero_()
     # test duplicate keys
     rowid = Index(th.tensor([8, 2, 2, 1]))
     rows = f[rowid]
     rows['a1'].backward(th.ones((len(rowid), D)))
-    assert th.allclose(c1.grad[:,0], th.tensor([0., 1., 2., 0., 0., 0., 0., 0., 1., 0.]))
+    assert U.allclose(c1.grad[:,0], th.tensor([0., 1., 2., 0., 0., 0., 0., 0., 1., 0.]))
     c1.grad.data.zero_()
 
     # setter
@@ -184,8 +185,8 @@ def test_row2():
     f[rowid] = vals
     c11 = f['a1']
     c11.backward(th.ones((N, D)))
-    assert th.allclose(c1.grad[:,0], th.tensor([0., 1., 0., 1., 0., 1., 1., 1., 1., 1.]))
-    assert th.allclose(vals['a1'].grad, th.ones((len(rowid), D)))
+    assert U.allclose(c1.grad[:,0], th.tensor([0., 1., 0., 1., 0., 1., 1., 1., 1., 1.]))
+    assert U.allclose(vals['a1'].grad, th.ones((len(rowid), D)))
     assert vals['a2'].grad is None
 
 def test_row3():
@@ -207,7 +208,7 @@ def test_row3():
     newidx.pop(2)
     newidx = toindex(newidx)
     for k, v in f.items():
-        assert th.allclose(v, data[k][newidx])
+        assert U.allclose(v, data[k][newidx])
 
 def test_sharing():
     data = Frame(create_test_data())
@@ -215,9 +216,9 @@ def test_sharing():
     f2 = FrameRef(data, index=[2, 3, 4, 5, 6])
     # test read
     for k, v in f1.items():
-        assert th.allclose(data[k].data[0:4], v)
+        assert U.allclose(data[k].data[0:4], v)
     for k, v in f2.items():
-        assert th.allclose(data[k].data[2:7], v)
+        assert U.allclose(data[k].data[2:7], v)
     f2_a1 = f2['a1'].data
     # test write
     # update own ref should not been seen by the other.
@@ -226,7 +227,7 @@ def test_sharing():
             'a2' : th.zeros([2, D]),
             'a3' : th.zeros([2, D]),
             }
-    assert th.allclose(f2['a1'], f2_a1)
+    assert U.allclose(f2['a1'], f2_a1)
     # update shared space should been seen by the other.
     f1[Index(th.tensor([2, 3]))] = {
             'a1' : th.ones([2, D]),
@@ -234,7 +235,7 @@ def test_sharing():
             'a3' : th.ones([2, D]),
             }
     f2_a1[0:2] = th.ones([2, D])
-    assert th.allclose(f2['a1'], f2_a1)
+    assert U.allclose(f2['a1'], f2_a1)
 
 def test_slicing():
     data = Frame(create_test_data(grad=True))
@@ -242,7 +243,7 @@ def test_slicing():
     f2 = FrameRef(data, index=slice(3, 8))
     # test read
     for k, v in f1.items():
-        assert th.allclose(data[k].data[1:5], v)
+        assert U.allclose(data[k].data[1:5], v)
     f2_a1 = f2['a1'].data
     # test write
     f1[Index(th.tensor([0, 1]))] = {
@@ -250,7 +251,7 @@ def test_slicing():
             'a2': th.zeros([2, D]),
             'a3': th.zeros([2, D]),
             }
-    assert th.allclose(f2['a1'], f2_a1)
+    assert U.allclose(f2['a1'], f2_a1)
     
     f1[Index(th.tensor([2, 3]))] = {
             'a1': th.ones([2, D]),
@@ -258,7 +259,7 @@ def test_slicing():
             'a3': th.ones([2, D]),
             }
     f2_a1[0:2] = 1
-    assert th.allclose(f2['a1'], f2_a1)
+    assert U.allclose(f2['a1'], f2_a1)
 
     f1[2:4] = {
             'a1': th.zeros([2, D]),
@@ -266,7 +267,7 @@ def test_slicing():
             'a3': th.zeros([2, D]),
             }
     f2_a1[0:2] = 0
-    assert th.allclose(f2['a1'], f2_a1)
+    assert U.allclose(f2['a1'], f2_a1)
 
 if __name__ == '__main__':
     test_create()

tests/pytorch/test_function.py

 import torch as th
 import dgl
 import dgl.function as fn
+import utils as U
 
 def generate_graph():
     g = dgl.DGLGraph()
@@ -27,7 +28,7 @@ def test_copy_src():
     g.register_message_func(fn.copy_src(src='h', out='m'))
     g.register_reduce_func(reducer_both)
     g.update_all()
-    assert th.allclose(g.ndata['h'],
+    assert U.allclose(g.ndata['h'],
             th.tensor([10., 1., 1., 1., 1., 1., 1., 1., 1., 44.]))
 
 def test_copy_edge():
@@ -36,7 +37,7 @@ def test_copy_edge():
     g.register_message_func(fn.copy_edge(edge='h', out='m'))
     g.register_reduce_func(reducer_both)
     g.update_all()
-    assert th.allclose(g.ndata['h'],
+    assert U.allclose(g.ndata['h'],
             th.tensor([10., 1., 1., 1., 1., 1., 1., 1., 1., 44.]))
 
 def test_src_mul_edge():
@@ -45,7 +46,7 @@ def test_src_mul_edge():
     g.register_message_func(fn.src_mul_edge(src='h', edge='h', out='m'))
     g.register_reduce_func(reducer_both)
     g.update_all()
-    assert th.allclose(g.ndata['h'],
+    assert U.allclose(g.ndata['h'],
             th.tensor([100., 1., 1., 1., 1., 1., 1., 1., 1., 284.]))
 
 if __name__ == '__main__':

tests/pytorch/test_index.py

@@ -4,6 +4,7 @@ from dgl.utils import toindex
 import numpy as np
 import torch as th
 from torch.utils import dlpack
+import utils as U
 
 def test_dlpack():
     # test dlpack conversion.

tests/pytorch/test_line_graph.py

@@ -2,6 +2,7 @@ import torch as th
 import networkx as nx
 import numpy as np
 import dgl
+import utils as U
 
 D = 5
 
@@ -19,13 +20,13 @@ def test_line_graph():
     v = [1, 2, 0, 0]
     eid = G.edge_ids(u, v)
     L.nodes[eid].data['h'] = th.zeros((4, D))
-    assert th.allclose(G.edges[u, v].data['h'], th.zeros((4, D)))
+    assert U.allclose(G.edges[u, v].data['h'], th.zeros((4, D)))
 
     # adding a new node feature on line graph should also reflect to a new
     # edge feature on original graph
     data = th.randn(n_edges, D)
     L.ndata['w'] = data
-    assert th.allclose(G.edata['w'], data)
+    assert U.allclose(G.edata['w'], data)
 
 def test_no_backtracking():
     N = 5

tests/pytorch/test_specialization.py

@@ -2,6 +2,7 @@ import torch as th
 import numpy as np
 import dgl
 import dgl.function as fn
+import utils as U
 
 D = 5
 
@@ -43,7 +44,7 @@ def test_update_all():
         g.set_n_repr({fld : v1})
         g.update_all(message_func, reduce_func, apply_func)
         v3 = g.ndata[fld]
-        assert th.allclose(v2, v3)
+        assert U.allclose(v2, v3)
         # update all with edge weights
         v1 = g.ndata[fld]
         g.update_all(fn.src_mul_edge(src=fld, edge='e1', out='m'),
@@ -56,8 +57,8 @@ def test_update_all():
         g.set_n_repr({fld : v1})
         g.update_all(message_func_edge, reduce_func, apply_func)
         v4 = g.ndata[fld]
-        assert th.allclose(v2, v3)
-        assert th.allclose(v3, v4)
+        assert U.allclose(v2, v3)
+        assert U.allclose(v3, v4)
     # test 1d node features
     _test('f1')
     # test 2d node features
@@ -90,7 +91,7 @@ def test_send_and_recv():
         g.set_n_repr({fld : v1})
         g.send_and_recv((u, v), message_func, reduce_func, apply_func)
         v3 = g.ndata[fld]
-        assert th.allclose(v2, v3)
+        assert U.allclose(v2, v3)
         # send and recv with edge weights
         v1 = g.ndata[fld]
         g.send_and_recv((u, v), fn.src_mul_edge(src=fld, edge='e1', out='m'),
@@ -103,8 +104,8 @@ def test_send_and_recv():
         g.set_n_repr({fld : v1})
         g.send_and_recv((u, v), message_func_edge, reduce_func, apply_func)
         v4 = g.ndata[fld]
-        assert th.allclose(v2, v3)
-        assert th.allclose(v3, v4)
+        assert U.allclose(v2, v3)
+        assert U.allclose(v3, v4)
     # test 1d node features
     _test('f1')
     # test 2d node features
@@ -129,19 +130,19 @@ def test_update_all_multi_fn():
                  None)
     v1 = g.ndata['v1']
     v2 = g.ndata['v2']
-    assert th.allclose(v1, v2)
+    assert U.allclose(v1, v2)
 
     # run builtin with single message and reduce
     g.update_all(fn.copy_src(src=fld, out='m'), fn.sum(msg='m', out='v1'), None)
     v1 = g.ndata['v1']
-    assert th.allclose(v1, v2)
+    assert U.allclose(v1, v2)
 
     # 1 message, 2 reduces
     g.update_all(fn.copy_src(src=fld, out='m'), [fn.sum(msg='m', out='v2'), fn.sum(msg='m', out='v3')], None)
     v2 = g.ndata['v2']
     v3 = g.ndata['v3']
-    assert th.allclose(v1, v2)
-    assert th.allclose(v1, v3)
+    assert U.allclose(v1, v2)
+    assert U.allclose(v1, v3)
 
     # update all with edge weights, 2 message, 3 reduces
     g.update_all([fn.src_mul_edge(src=fld, edge='e1', out='m1'), fn.src_mul_edge(src=fld, edge='e2', out='m2')],
@@ -150,13 +151,13 @@ def test_update_all_multi_fn():
     v1 = g.ndata['v1']
     v2 = g.ndata['v2']
     v3 = g.ndata['v3']
-    assert th.allclose(v1, v2)
-    assert th.allclose(v1, v3)
+    assert U.allclose(v1, v2)
+    assert U.allclose(v1, v3)
 
     # run UDF with single message and reduce
     g.update_all(message_func_edge, reduce_func, None)
     v2 = g.ndata['v2']
-    assert th.allclose(v1, v2)
+    assert U.allclose(v1, v2)
 
 def test_send_and_recv_multi_fn():
     u = th.tensor([0, 0, 0, 3, 4, 9])
@@ -183,13 +184,13 @@ def test_send_and_recv_multi_fn():
                     None)
     v1 = g.ndata['v1']
     v2 = g.ndata['v2']
-    assert th.allclose(v1, v2)
+    assert U.allclose(v1, v2)
 
     # run builtin with single message and reduce
     g.send_and_recv((u, v), fn.copy_src(src=fld, out='m'), fn.sum(msg='m', out='v1'),
                     None)
     v1 = g.ndata['v1']
-    assert th.allclose(v1, v2)
+    assert U.allclose(v1, v2)
 
     # 1 message, 2 reduces
     g.send_and_recv((u, v),
@@ -198,8 +199,8 @@ def test_send_and_recv_multi_fn():
             None)
     v2 = g.ndata['v2']
     v3 = g.ndata['v3']
-    assert th.allclose(v1, v2)
-    assert th.allclose(v1, v3)
+    assert U.allclose(v1, v2)
+    assert U.allclose(v1, v3)
 
     # send and recv with edge weights, 2 message, 3 reduces
     g.send_and_recv((u, v),
@@ -209,14 +210,14 @@ def test_send_and_recv_multi_fn():
     v1 = g.ndata['v1']
     v2 = g.ndata['v2']
     v3 = g.ndata['v3']
-    assert th.allclose(v1, v2)
-    assert th.allclose(v1, v3)
+    assert U.allclose(v1, v2)
+    assert U.allclose(v1, v3)
 
     # run UDF with single message and reduce
     g.send_and_recv((u, v), message_func_edge,
             reduce_func, None)
     v2 = g.ndata['v2']
-    assert th.allclose(v1, v2)
+    assert U.allclose(v1, v2)
 
 if __name__ == '__main__':
     test_update_all()

tests/pytorch/test_subgraph.py

@@ -2,6 +2,7 @@ import torch as th
 from torch.autograd import Variable
 import numpy as np
 from dgl.graph import DGLGraph
+import utils as U
 
 D = 5
 
@@ -37,7 +38,7 @@ def test_basics():
     assert len(sg.ndata) == 1
     assert len(sg.edata) == 1
     sh = sg.ndata['h']
-    assert th.allclose(h[nid], sh)
+    assert U.allclose(h[nid], sh)
     '''
     s, d, eid
     0, 1, 0
@@ -58,11 +59,11 @@ def test_basics():
     8, 9, 15      3
     9, 0, 16   1
     '''
-    assert th.allclose(l[eid], sg.edata['l'])
+    assert U.allclose(l[eid], sg.edata['l'])
     # update the node/edge features on the subgraph should NOT
     # reflect to the parent graph.
     sg.ndata['h'] = th.zeros((6, D))
-    assert th.allclose(h, g.ndata['h'])
+    assert U.allclose(h, g.ndata['h'])
 
 def test_merge():
     # FIXME: current impl cannot handle this case!!!
@@ -87,8 +88,8 @@ def test_merge():
 
     h = g.ndata['h'][:,0]
     l = g.edata['l'][:,0]
-    assert th.allclose(h, th.tensor([3., 0., 3., 3., 2., 0., 1., 1., 0., 1.]))
-    assert th.allclose(l,
+    assert U.allclose(h, th.tensor([3., 0., 3., 3., 2., 0., 1., 1., 0., 1.]))
+    assert U.allclose(l,
             th.tensor([0., 0., 1., 1., 1., 1., 0., 0., 0., 3., 1., 4., 1., 4., 0., 3., 1.]))
     """
 

tests/pytorch/test_traversal.py

@@ -7,6 +7,7 @@ import networkx as nx
 import numpy as np
 import scipy.sparse as sp
 import torch as th
+import utils as U
 
 np.random.seed(42)
 

tests/pytorch/utils.py

+import torch as th
+
+def allclose(a, b):
+    return th.allclose(a, b, rtol=1e-4, atol=1e-4)