dmlc--dgl
Quan (Andy) Gan
660 行
22 KiB
Python
660 行
22 KiB
Python
import backend as F
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import dgl
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import numpy as np
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import scipy.sparse as ssp
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import networkx as nx
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from dgl import DGLGraph
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from collections import defaultdict as ddict
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import unittest
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from test_utils import parametrize_dtype
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D = 5
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reduce_msg_shapes = set()
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def message_func(edges):
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assert F.ndim(edges.src['h']) == 2
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assert F.shape(edges.src['h'])[1] == D
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return {'m' : edges.src['h']}
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def reduce_func(nodes):
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msgs = nodes.mailbox['m']
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reduce_msg_shapes.add(tuple(msgs.shape))
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assert F.ndim(msgs) == 3
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assert F.shape(msgs)[2] == D
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return {'accum' : F.sum(msgs, 1)}
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def apply_node_func(nodes):
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return {'h' : nodes.data['h'] + nodes.data['accum']}
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def generate_graph_old(grad=False):
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g = DGLGraph()
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g.add_nodes(10) # 10 nodes
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# create a graph where 0 is the source and 9 is the sink
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# 17 edges
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for i in range(1, 9):
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g.add_edge(0, i)
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g.add_edge(i, 9)
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# add a back flow from 9 to 0
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g.add_edge(9, 0)
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g = g.to(F.ctx())
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ncol = F.randn((10, D))
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ecol = F.randn((17, D))
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if grad:
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ncol = F.attach_grad(ncol)
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ecol = F.attach_grad(ecol)
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g.ndata['h'] = ncol
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g.edata['w'] = ecol
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g.set_n_initializer(dgl.init.zero_initializer)
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g.set_e_initializer(dgl.init.zero_initializer)
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return g
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def generate_graph(idtype, grad=False):
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'''
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s, d, eid
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0, 1, 0
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1, 9, 1
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0, 2, 2
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2, 9, 3
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0, 3, 4
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3, 9, 5
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0, 4, 6
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4, 9, 7
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0, 5, 8
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5, 9, 9
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0, 6, 10
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6, 9, 11
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0, 7, 12
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7, 9, 13
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0, 8, 14
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8, 9, 15
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9, 0, 16
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'''
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u = F.tensor([0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 9])
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v = F.tensor([1, 9, 2, 9, 3, 9, 4, 9, 5, 9, 6, 9, 7, 9, 8, 9, 0])
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g = dgl.graph((u, v), idtype=idtype)
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assert g.device == F.ctx()
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ncol = F.randn((10, D))
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ecol = F.randn((17, D))
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if grad:
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ncol = F.attach_grad(ncol)
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ecol = F.attach_grad(ecol)
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g.ndata['h'] = ncol
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g.edata['w'] = ecol
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g.set_n_initializer(dgl.init.zero_initializer)
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g.set_e_initializer(dgl.init.zero_initializer)
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return g
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def test_compatible():
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g = generate_graph_old()
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@parametrize_dtype
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def test_batch_setter_getter(idtype):
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def _pfc(x):
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return list(F.zerocopy_to_numpy(x)[:,0])
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g = generate_graph(idtype)
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# set all nodes
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g.ndata['h'] = F.zeros((10, D))
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assert F.allclose(g.ndata['h'], F.zeros((10, D)))
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# pop nodes
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old_len = len(g.ndata)
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g.ndata.pop('h')
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assert len(g.ndata) == old_len - 1
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g.ndata['h'] = F.zeros((10, D))
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# set partial nodes
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u = F.tensor([1, 3, 5], g.idtype)
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g.nodes[u].data['h'] = F.ones((3, D))
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assert _pfc(g.ndata['h']) == [0., 1., 0., 1., 0., 1., 0., 0., 0., 0.]
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# get partial nodes
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u = F.tensor([1, 2, 3], g.idtype)
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assert _pfc(g.nodes[u].data['h']) == [1., 0., 1.]
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'''
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s, d, eid
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0, 1, 0
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1, 9, 1
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0, 2, 2
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2, 9, 3
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0, 3, 4
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3, 9, 5
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0, 4, 6
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4, 9, 7
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0, 5, 8
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5, 9, 9
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0, 6, 10
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6, 9, 11
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0, 7, 12
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7, 9, 13
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0, 8, 14
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8, 9, 15
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9, 0, 16
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'''
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# set all edges
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g.edata['l'] = F.zeros((17, D))
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assert _pfc(g.edata['l']) == [0.] * 17
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# pop edges
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old_len = len(g.edata)
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g.edata.pop('l')
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assert len(g.edata) == old_len - 1
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g.edata['l'] = F.zeros((17, D))
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# set partial edges (many-many)
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u = F.tensor([0, 0, 2, 5, 9], g.idtype)
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v = F.tensor([1, 3, 9, 9, 0], g.idtype)
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g.edges[u, v].data['l'] = F.ones((5, D))
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truth = [0.] * 17
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truth[0] = truth[4] = truth[3] = truth[9] = truth[16] = 1.
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assert _pfc(g.edata['l']) == truth
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u = F.tensor([3, 4, 6], g.idtype)
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v = F.tensor([9, 9, 9], g.idtype)
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g.edges[u, v].data['l'] = F.ones((3, D))
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truth[5] = truth[7] = truth[11] = 1.
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assert _pfc(g.edata['l']) == truth
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u = F.tensor([0, 0, 0], g.idtype)
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v = F.tensor([4, 5, 6], g.idtype)
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g.edges[u, v].data['l'] = F.ones((3, D))
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truth[6] = truth[8] = truth[10] = 1.
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assert _pfc(g.edata['l']) == truth
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u = F.tensor([0, 6, 0], g.idtype)
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v = F.tensor([6, 9, 7], g.idtype)
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assert _pfc(g.edges[u, v].data['l']) == [1.0, 1.0, 0.0]
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@parametrize_dtype
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def test_batch_setter_autograd(idtype):
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g = generate_graph(idtype, grad=True)
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h1 = g.ndata['h']
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# partial set
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v = F.tensor([1, 2, 8], g.idtype)
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hh = F.attach_grad(F.zeros((len(v), D)))
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with F.record_grad():
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g.nodes[v].data['h'] = hh
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h2 = g.ndata['h']
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F.backward(h2, F.ones((10, D)) * 2)
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assert F.array_equal(F.grad(h1)[:,0], F.tensor([2., 0., 0., 2., 2., 2., 2., 2., 0., 2.]))
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assert F.array_equal(F.grad(hh)[:,0], F.tensor([2., 2., 2.]))
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def _test_nx_conversion():
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# check conversion between networkx and DGLGraph
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def _check_nx_feature(nxg, nf, ef):
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# check node and edge feature of nxg
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# this is used to check to_networkx
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num_nodes = len(nxg)
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num_edges = nxg.size()
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if num_nodes > 0:
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node_feat = ddict(list)
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for nid, attr in nxg.nodes(data=True):
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assert len(attr) == len(nf)
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for k in nxg.nodes[nid]:
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node_feat[k].append(F.unsqueeze(attr[k], 0))
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for k in node_feat:
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feat = F.cat(node_feat[k], 0)
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assert F.allclose(feat, nf[k])
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else:
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assert len(nf) == 0
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if num_edges > 0:
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edge_feat = ddict(lambda: [0] * num_edges)
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for u, v, attr in nxg.edges(data=True):
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assert len(attr) == len(ef) + 1 # extra id
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eid = attr['id']
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for k in ef:
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edge_feat[k][eid] = F.unsqueeze(attr[k], 0)
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for k in edge_feat:
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feat = F.cat(edge_feat[k], 0)
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assert F.allclose(feat, ef[k])
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else:
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assert len(ef) == 0
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n1 = F.randn((5, 3))
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n2 = F.randn((5, 10))
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n3 = F.randn((5, 4))
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e1 = F.randn((4, 5))
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e2 = F.randn((4, 7))
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g = dgl.graph(([0, 1, 3, 4], [2, 4, 0, 3]))
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g.ndata.update({'n1': n1, 'n2': n2, 'n3': n3})
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g.edata.update({'e1': e1, 'e2': e2})
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# convert to networkx
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nxg = g.to_networkx(node_attrs=['n1', 'n3'], edge_attrs=['e1', 'e2'])
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assert len(nxg) == 5
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assert nxg.size() == 4
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_check_nx_feature(nxg, {'n1': n1, 'n3': n3}, {'e1': e1, 'e2': e2})
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# convert to DGLGraph, nx graph has id in edge feature
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# use id feature to test non-tensor copy
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g = dgl.from_networkx(nxg, node_attrs=['n1'], edge_attrs=['e1', 'id'])
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# check graph size
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assert g.number_of_nodes() == 5
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assert g.number_of_edges() == 4
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# check number of features
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# test with existing dglgraph (so existing features should be cleared)
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assert len(g.ndata) == 1
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assert len(g.edata) == 2
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# check feature values
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assert F.allclose(g.ndata['n1'], n1)
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# with id in nx edge feature, e1 should follow original order
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assert F.allclose(g.edata['e1'], e1)
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assert F.array_equal(F.astype(g.edata['id'], F.int64), F.copy_to(F.arange(0, 4), F.cpu()))
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# test conversion after modifying DGLGraph
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g.edata.pop('id') # pop id so we don't need to provide id when adding edges
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new_n = F.randn((2, 3))
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new_e = F.randn((3, 5))
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g.add_nodes(2, data={'n1': new_n})
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# add three edges, one is a multi-edge
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g.add_edges([3, 6, 0], [4, 5, 2], data={'e1': new_e})
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n1 = F.cat((n1, new_n), 0)
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e1 = F.cat((e1, new_e), 0)
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# convert to networkx again
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nxg = g.to_networkx(node_attrs=['n1'], edge_attrs=['e1'])
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assert len(nxg) == 7
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assert nxg.size() == 7
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_check_nx_feature(nxg, {'n1': n1}, {'e1': e1})
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# now test convert from networkx without id in edge feature
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# first pop id in edge feature
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for _, _, attr in nxg.edges(data=True):
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attr.pop('id')
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# test with a new graph
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g = dgl.from_networkx(nxg, node_attrs=['n1'], edge_attrs=['e1'])
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# check graph size
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assert g.number_of_nodes() == 7
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assert g.number_of_edges() == 7
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# check number of features
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assert len(g.ndata) == 1
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assert len(g.edata) == 1
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# check feature values
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assert F.allclose(g.ndata['n1'], n1)
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# edge feature order follows nxg.edges()
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edge_feat = []
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for _, _, attr in nxg.edges(data=True):
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edge_feat.append(F.unsqueeze(attr['e1'], 0))
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edge_feat = F.cat(edge_feat, 0)
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assert F.allclose(g.edata['e1'], edge_feat)
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# Test converting from a networkx graph whose nodes are
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# not labeled with consecutive-integers.
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nxg = nx.cycle_graph(5)
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nxg.remove_nodes_from([0, 4])
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for u in nxg.nodes():
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nxg.nodes[u]['h'] = F.tensor([u])
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for u, v, d in nxg.edges(data=True):
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d['h'] = F.tensor([u, v])
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g = dgl.from_networkx(nxg, node_attrs=['h'], edge_attrs=['h'])
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assert g.number_of_nodes() == 3
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assert g.number_of_edges() == 4
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assert g.has_edge_between(0, 1)
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assert g.has_edge_between(1, 2)
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assert F.allclose(g.ndata['h'], F.tensor([[1.], [2.], [3.]]))
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assert F.allclose(g.edata['h'], F.tensor([[1., 2.], [1., 2.],
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[2., 3.], [2., 3.]]))
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@parametrize_dtype
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def test_apply_nodes(idtype):
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def _upd(nodes):
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return {'h' : nodes.data['h'] * 2}
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g = generate_graph(idtype)
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old = g.ndata['h']
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g.apply_nodes(_upd)
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assert F.allclose(old * 2, g.ndata['h'])
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u = F.tensor([0, 3, 4, 6], g.idtype)
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g.apply_nodes(lambda nodes : {'h' : nodes.data['h'] * 0.}, u)
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assert F.allclose(F.gather_row(g.ndata['h'], u), F.zeros((4, D)))
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@parametrize_dtype
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def test_apply_edges(idtype):
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def _upd(edges):
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return {'w' : edges.data['w'] * 2}
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g = generate_graph(idtype)
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old = g.edata['w']
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g.apply_edges(_upd)
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assert F.allclose(old * 2, g.edata['w'])
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u = F.tensor([0, 0, 0, 4, 5, 6], g.idtype)
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v = F.tensor([1, 2, 3, 9, 9, 9], g.idtype)
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g.apply_edges(lambda edges : {'w' : edges.data['w'] * 0.}, (u, v))
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eid = F.tensor(g.edge_ids(u, v))
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assert F.allclose(F.gather_row(g.edata['w'], eid), F.zeros((6, D)))
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@parametrize_dtype
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def test_update_routines(idtype):
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g = generate_graph(idtype)
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# send_and_recv
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reduce_msg_shapes.clear()
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u = [0, 0, 0, 4, 5, 6]
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v = [1, 2, 3, 9, 9, 9]
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g.send_and_recv((u, v), message_func, reduce_func, apply_node_func)
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assert(reduce_msg_shapes == {(1, 3, D), (3, 1, D)})
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reduce_msg_shapes.clear()
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try:
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g.send_and_recv([u, v])
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assert False
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except:
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pass
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# pull
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v = F.tensor([1, 2, 3, 9], g.idtype)
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reduce_msg_shapes.clear()
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g.pull(v, message_func, reduce_func, apply_node_func)
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assert(reduce_msg_shapes == {(1, 8, D), (3, 1, D)})
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reduce_msg_shapes.clear()
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# push
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v = F.tensor([0, 1, 2, 3], g.idtype)
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reduce_msg_shapes.clear()
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g.push(v, message_func, reduce_func, apply_node_func)
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assert(reduce_msg_shapes == {(1, 3, D), (8, 1, D)})
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reduce_msg_shapes.clear()
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# update_all
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reduce_msg_shapes.clear()
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g.update_all(message_func, reduce_func, apply_node_func)
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assert(reduce_msg_shapes == {(1, 8, D), (9, 1, D)})
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reduce_msg_shapes.clear()
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@parametrize_dtype
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def test_update_all_0deg(idtype):
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# test#1
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g = dgl.graph(([1, 2, 3, 4], [0, 0, 0, 0]), idtype=idtype, device=F.ctx())
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def _message(edges):
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return {'m' : edges.src['h']}
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def _reduce(nodes):
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return {'x' : nodes.data['h'] + F.sum(nodes.mailbox['m'], 1)}
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def _apply(nodes):
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return {'x' : nodes.data['x'] * 2}
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def _init2(shape, dtype, ctx, ids):
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return 2 + F.zeros(shape, dtype, ctx)
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g.set_n_initializer(_init2, 'x')
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old_repr = F.randn((5, 5))
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g.ndata['h'] = old_repr
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g.update_all(_message, _reduce, _apply)
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new_repr = g.ndata['x']
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# the first row of the new_repr should be the sum of all the node
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# features; while the 0-deg nodes should be initialized by the
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# initializer and applied with UDF.
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assert F.allclose(new_repr[1:], 2*(2+F.zeros((4,5))))
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assert F.allclose(new_repr[0], 2 * F.sum(old_repr, 0))
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# test#2: graph with no edge
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g = dgl.graph(([], []), num_nodes=5, idtype=idtype, device=F.ctx())
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g.ndata['h'] = old_repr
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g.update_all(_message, _reduce, lambda nodes : {'h' : nodes.data['h'] * 2})
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new_repr = g.ndata['h']
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# should fallback to apply
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assert F.allclose(new_repr, 2*old_repr)
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@parametrize_dtype
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def test_pull_0deg(idtype):
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g = dgl.graph(([0], [1]), idtype=idtype, device=F.ctx())
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def _message(edges):
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return {'m' : edges.src['h']}
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def _reduce(nodes):
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return {'x' : nodes.data['h'] + F.sum(nodes.mailbox['m'], 1)}
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def _apply(nodes):
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return {'x' : nodes.data['x'] * 2}
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def _init2(shape, dtype, ctx, ids):
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return 2 + F.zeros(shape, dtype, ctx)
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g.set_n_initializer(_init2, 'x')
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# test#1: pull both 0deg and non-0deg nodes
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old = F.randn((2, 5))
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g.ndata['h'] = old
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g.pull([0, 1], _message, _reduce, _apply)
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new = g.ndata['x']
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# 0deg check: initialized with the func and got applied
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assert F.allclose(new[0], F.full_1d(5, 4, dtype=F.float32))
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# non-0deg check
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assert F.allclose(new[1], F.sum(old, 0) * 2)
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# test#2: pull only 0deg node
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old = F.randn((2, 5))
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g.ndata['h'] = old
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g.pull(0, _message, _reduce, lambda nodes : {'h' : nodes.data['h'] * 2})
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new = g.ndata['h']
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# 0deg check: fallback to apply
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assert F.allclose(new[0], 2*old[0])
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# non-0deg check: not touched
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assert F.allclose(new[1], old[1])
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def test_dynamic_addition():
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N = 3
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D = 1
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g = DGLGraph()
|
|
g = g.to(F.ctx())
|
|
|
|
# Test node addition
|
|
g.add_nodes(N)
|
|
g.ndata.update({'h1': F.randn((N, D)),
|
|
'h2': F.randn((N, D))})
|
|
g.add_nodes(3)
|
|
assert g.ndata['h1'].shape[0] == g.ndata['h2'].shape[0] == N + 3
|
|
|
|
# Test edge addition
|
|
g.add_edge(0, 1)
|
|
g.add_edge(1, 0)
|
|
g.edata.update({'h1': F.randn((2, D)),
|
|
'h2': F.randn((2, D))})
|
|
assert g.edata['h1'].shape[0] == g.edata['h2'].shape[0] == 2
|
|
|
|
g.add_edges([0, 2], [2, 0])
|
|
g.edata['h1'] = F.randn((4, D))
|
|
assert g.edata['h1'].shape[0] == g.edata['h2'].shape[0] == 4
|
|
|
|
g.add_edge(1, 2)
|
|
g.edges[4].data['h1'] = F.randn((1, D))
|
|
assert g.edata['h1'].shape[0] == g.edata['h2'].shape[0] == 5
|
|
|
|
# test add edge with part of the features
|
|
g.add_edge(2, 1, {'h1': F.randn((1, D))})
|
|
assert len(g.edata['h1']) == len(g.edata['h2'])
|
|
|
|
|
|
@parametrize_dtype
|
|
def test_repr(idtype):
|
|
g = dgl.graph(([0, 0, 1], [1, 2, 2]), num_nodes=10, idtype=idtype, device=F.ctx())
|
|
repr_string = g.__repr__()
|
|
print(repr_string)
|
|
g.ndata['x'] = F.zeros((10, 5))
|
|
g.edata['y'] = F.zeros((3, 4))
|
|
repr_string = g.__repr__()
|
|
print(repr_string)
|
|
|
|
@parametrize_dtype
|
|
def test_local_var(idtype):
|
|
g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]), idtype=idtype, device=F.ctx())
|
|
g.ndata['h'] = F.zeros((g.number_of_nodes(), 3))
|
|
g.edata['w'] = F.zeros((g.number_of_edges(), 4))
|
|
# test override
|
|
def foo(g):
|
|
g = g.local_var()
|
|
g.ndata['h'] = F.ones((g.number_of_nodes(), 3))
|
|
g.edata['w'] = F.ones((g.number_of_edges(), 4))
|
|
foo(g)
|
|
assert F.allclose(g.ndata['h'], F.zeros((g.number_of_nodes(), 3)))
|
|
assert F.allclose(g.edata['w'], F.zeros((g.number_of_edges(), 4)))
|
|
# test out-place update
|
|
def foo(g):
|
|
g = g.local_var()
|
|
g.nodes[[2, 3]].data['h'] = F.ones((2, 3))
|
|
g.edges[[2, 3]].data['w'] = F.ones((2, 4))
|
|
foo(g)
|
|
assert F.allclose(g.ndata['h'], F.zeros((g.number_of_nodes(), 3)))
|
|
assert F.allclose(g.edata['w'], F.zeros((g.number_of_edges(), 4)))
|
|
# test out-place update 2
|
|
def foo(g):
|
|
g = g.local_var()
|
|
g.apply_nodes(lambda nodes: {'h' : nodes.data['h'] + 10}, [2, 3])
|
|
g.apply_edges(lambda edges: {'w' : edges.data['w'] + 10}, [2, 3])
|
|
foo(g)
|
|
assert F.allclose(g.ndata['h'], F.zeros((g.number_of_nodes(), 3)))
|
|
assert F.allclose(g.edata['w'], F.zeros((g.number_of_edges(), 4)))
|
|
# test auto-pop
|
|
def foo(g):
|
|
g = g.local_var()
|
|
g.ndata['hh'] = F.ones((g.number_of_nodes(), 3))
|
|
g.edata['ww'] = F.ones((g.number_of_edges(), 4))
|
|
foo(g)
|
|
assert 'hh' not in g.ndata
|
|
assert 'ww' not in g.edata
|
|
|
|
# test initializer1
|
|
g = dgl.graph(([0, 1], [1, 1]), idtype=idtype, device=F.ctx())
|
|
g.set_n_initializer(dgl.init.zero_initializer)
|
|
def foo(g):
|
|
g = g.local_var()
|
|
g.nodes[0].data['h'] = F.ones((1, 1))
|
|
assert F.allclose(g.ndata['h'], F.tensor([[1.], [0.]]))
|
|
foo(g)
|
|
# test initializer2
|
|
def foo_e_initializer(shape, dtype, ctx, id_range):
|
|
return F.ones(shape)
|
|
g.set_e_initializer(foo_e_initializer, field='h')
|
|
def foo(g):
|
|
g = g.local_var()
|
|
g.edges[0, 1].data['h'] = F.ones((1, 1))
|
|
assert F.allclose(g.edata['h'], F.ones((2, 1)))
|
|
g.edges[0, 1].data['w'] = F.ones((1, 1))
|
|
assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]]))
|
|
foo(g)
|
|
|
|
@parametrize_dtype
|
|
def test_local_scope(idtype):
|
|
g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]), idtype=idtype, device=F.ctx())
|
|
g.ndata['h'] = F.zeros((g.number_of_nodes(), 3))
|
|
g.edata['w'] = F.zeros((g.number_of_edges(), 4))
|
|
# test override
|
|
def foo(g):
|
|
with g.local_scope():
|
|
g.ndata['h'] = F.ones((g.number_of_nodes(), 3))
|
|
g.edata['w'] = F.ones((g.number_of_edges(), 4))
|
|
foo(g)
|
|
assert F.allclose(g.ndata['h'], F.zeros((g.number_of_nodes(), 3)))
|
|
assert F.allclose(g.edata['w'], F.zeros((g.number_of_edges(), 4)))
|
|
# test out-place update
|
|
def foo(g):
|
|
with g.local_scope():
|
|
g.nodes[[2, 3]].data['h'] = F.ones((2, 3))
|
|
g.edges[[2, 3]].data['w'] = F.ones((2, 4))
|
|
foo(g)
|
|
assert F.allclose(g.ndata['h'], F.zeros((g.number_of_nodes(), 3)))
|
|
assert F.allclose(g.edata['w'], F.zeros((g.number_of_edges(), 4)))
|
|
# test out-place update 2
|
|
def foo(g):
|
|
with g.local_scope():
|
|
g.apply_nodes(lambda nodes: {'h' : nodes.data['h'] + 10}, [2, 3])
|
|
g.apply_edges(lambda edges: {'w' : edges.data['w'] + 10}, [2, 3])
|
|
foo(g)
|
|
assert F.allclose(g.ndata['h'], F.zeros((g.number_of_nodes(), 3)))
|
|
assert F.allclose(g.edata['w'], F.zeros((g.number_of_edges(), 4)))
|
|
# test auto-pop
|
|
def foo(g):
|
|
with g.local_scope():
|
|
g.ndata['hh'] = F.ones((g.number_of_nodes(), 3))
|
|
g.edata['ww'] = F.ones((g.number_of_edges(), 4))
|
|
foo(g)
|
|
assert 'hh' not in g.ndata
|
|
assert 'ww' not in g.edata
|
|
|
|
# test nested scope
|
|
def foo(g):
|
|
with g.local_scope():
|
|
g.ndata['hh'] = F.ones((g.number_of_nodes(), 3))
|
|
g.edata['ww'] = F.ones((g.number_of_edges(), 4))
|
|
with g.local_scope():
|
|
g.ndata['hhh'] = F.ones((g.number_of_nodes(), 3))
|
|
g.edata['www'] = F.ones((g.number_of_edges(), 4))
|
|
assert 'hhh' not in g.ndata
|
|
assert 'www' not in g.edata
|
|
foo(g)
|
|
assert 'hh' not in g.ndata
|
|
assert 'ww' not in g.edata
|
|
|
|
# test initializer1
|
|
g = dgl.graph(([0, 1], [1, 1]), idtype=idtype, device=F.ctx())
|
|
g.set_n_initializer(dgl.init.zero_initializer)
|
|
def foo(g):
|
|
with g.local_scope():
|
|
g.nodes[0].data['h'] = F.ones((1, 1))
|
|
assert F.allclose(g.ndata['h'], F.tensor([[1.], [0.]]))
|
|
foo(g)
|
|
# test initializer2
|
|
def foo_e_initializer(shape, dtype, ctx, id_range):
|
|
return F.ones(shape)
|
|
g.set_e_initializer(foo_e_initializer, field='h')
|
|
def foo(g):
|
|
with g.local_scope():
|
|
g.edges[0, 1].data['h'] = F.ones((1, 1))
|
|
assert F.allclose(g.edata['h'], F.ones((2, 1)))
|
|
g.edges[0, 1].data['w'] = F.ones((1, 1))
|
|
assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]]))
|
|
foo(g)
|
|
|
|
@parametrize_dtype
|
|
def test_isolated_nodes(idtype):
|
|
g = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=F.ctx())
|
|
assert g.number_of_nodes() == 5
|
|
|
|
g = dgl.heterograph({
|
|
('user', 'plays', 'game'): ([0, 0, 1], [2, 3, 2])
|
|
}, {'user': 5, 'game': 7}, idtype=idtype, device=F.ctx())
|
|
assert g.idtype == idtype
|
|
assert g.number_of_nodes('user') == 5
|
|
assert g.number_of_nodes('game') == 7
|
|
|
|
# Test backward compatibility
|
|
g = dgl.heterograph({
|
|
('user', 'plays', 'game'): ([0, 0, 1], [2, 3, 2])
|
|
}, {'user': 5, 'game': 7}, idtype=idtype, device=F.ctx())
|
|
assert g.idtype == idtype
|
|
assert g.number_of_nodes('user') == 5
|
|
assert g.number_of_nodes('game') == 7
|
|
|
|
@parametrize_dtype
|
|
def test_send_multigraph(idtype):
|
|
g = dgl.graph(([0, 0, 0, 2], [1, 1, 1, 1]), idtype=idtype, device=F.ctx())
|
|
|
|
def _message_a(edges):
|
|
return {'a': edges.data['a']}
|
|
def _message_b(edges):
|
|
return {'a': edges.data['a'] * 3}
|
|
def _reduce(nodes):
|
|
return {'a': F.max(nodes.mailbox['a'], 1)}
|
|
|
|
def answer(*args):
|
|
return F.max(F.stack(args, 0), 0)
|
|
|
|
assert g.is_multigraph
|
|
|
|
# send by eid
|
|
old_repr = F.randn((4, 5))
|
|
# send_and_recv_on
|
|
g.ndata['a'] = F.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 F.allclose(new_repr[1], answer(old_repr[0], old_repr[2], old_repr[3]))
|
|
assert F.allclose(new_repr[[0, 2]], F.zeros((2, 5)))
|
|
|
|
@parametrize_dtype
|
|
def test_issue_1088(idtype):
|
|
# This test ensures that message passing on a heterograph with one edge type
|
|
# would not crash (GitHub issue #1088).
|
|
import dgl.function as fn
|
|
g = dgl.heterograph({('U', 'E', 'V'): ([0, 1, 2], [1, 2, 3])}, idtype=idtype, device=F.ctx())
|
|
g.nodes['U'].data['x'] = F.randn((3, 3))
|
|
g.update_all(fn.copy_u('x', 'm'), fn.sum('m', 'y'))
|
|
|
|
@parametrize_dtype
|
|
def test_degree_bucket_edge_ordering(idtype):
|
|
import dgl.function as fn
|
|
g = dgl.graph(
|
|
([1, 3, 5, 0, 4, 2, 3, 3, 4, 5], [1, 1, 0, 0, 1, 2, 2, 0, 3, 3]),
|
|
idtype=idtype, device=F.ctx())
|
|
g.edata['eid'] = F.copy_to(F.arange(0, 10), F.ctx())
|
|
def reducer(nodes):
|
|
eid = F.asnumpy(F.copy_to(nodes.mailbox['eid'], F.cpu()))
|
|
assert np.array_equal(eid, np.sort(eid, 1))
|
|
return {'n': F.sum(nodes.mailbox['eid'], 1)}
|
|
g.update_all(fn.copy_e('eid', 'eid'), reducer)
|