test_heterograph.py 98.9 KB
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import dgl
import dgl.function as fn
from collections import Counter
import numpy as np
import scipy.sparse as ssp
import itertools
import backend as F
import networkx as nx
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import unittest, pytest
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from dgl import DGLError
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import test_utils
from test_utils import parametrize_dtype, get_cases
from scipy.sparse import rand
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def create_test_heterograph(idtype):
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    # test heterograph from the docstring, plus a user -- wishes -- game relation
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    # 3 users, 2 games, 2 developers
    # metagraph:
    #    ('user', 'follows', 'user'),
    #    ('user', 'plays', 'game'),
    #    ('user', 'wishes', 'game'),
    #    ('developer', 'develops', 'game')])
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    g = dgl.heterograph({
        ('user', 'follows', 'user'): ([0, 1], [1, 2]),
        ('user', 'plays', 'game'): ([0, 1, 2, 1], [0, 0, 1, 1]),
        ('user', 'wishes', 'game'): ([0, 2], [1, 0]),
        ('developer', 'develops', 'game'): ([0, 1], [0, 1])
    }, idtype=idtype, device=F.ctx())
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    assert g.idtype == idtype
    assert g.device == F.ctx()
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    return g

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def create_test_heterograph1(idtype):
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    edges = []
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    edges.extend([(0, 1), (1, 2)])  # follows
    edges.extend([(0, 3), (1, 3), (2, 4), (1, 4)])  # plays
    edges.extend([(0, 4), (2, 3)])  # wishes
    edges.extend([(5, 3), (6, 4)])  # develops
    edges = tuple(zip(*edges))
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    ntypes = F.tensor([0, 0, 0, 1, 1, 2, 2])
    etypes = F.tensor([0, 0, 1, 1, 1, 1, 2, 2, 3, 3])
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    g0 = dgl.graph(edges, idtype=idtype, device=F.ctx())
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    g0.ndata[dgl.NTYPE] = ntypes
    g0.edata[dgl.ETYPE] = etypes
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    return dgl.to_heterogeneous(g0, ['user', 'game', 'developer'],
                                ['follows', 'plays', 'wishes', 'develops'])
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def create_test_heterograph2(idtype):
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    g = dgl.heterograph({
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        ('user', 'follows', 'user'): ([0, 1], [1, 2]),
        ('user', 'plays', 'game'): ([0, 1, 2, 1], [0, 0, 1, 1]),
        ('user', 'wishes', 'game'): ([0, 2], [1, 0]),
        ('developer', 'develops', 'game'): ([0, 1], [0, 1]),
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        }, idtype=idtype, device=F.ctx())
    assert g.idtype == idtype
    assert g.device == F.ctx()
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    return g

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def create_test_heterograph3(idtype):
    g = dgl.heterograph({
        ('user', 'plays', 'game'): (F.tensor([0, 1, 1, 2], dtype=idtype),
                                    F.tensor([0, 0, 1, 1], dtype=idtype)),
        ('developer', 'develops', 'game'): (F.tensor([0, 1], dtype=idtype),
                                            F.tensor([0, 1], dtype=idtype))},
        idtype=idtype, device=F.ctx())
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    g.nodes['user'].data['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
    g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())
    g.nodes['developer'].data['h'] = F.copy_to(F.tensor([3, 3], dtype=idtype), ctx=F.ctx())
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    g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 1, 1, 1], dtype=idtype), ctx=F.ctx())
    return g

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def create_test_heterograph4(idtype):
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    g = dgl.heterograph({
        ('user', 'follows', 'user'): (F.tensor([0, 1, 1, 2, 2, 2], dtype=idtype),
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                                      F.tensor([0, 0, 1, 1, 2, 2], dtype=idtype)),
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        ('user', 'plays', 'game'): (F.tensor([0, 1], dtype=idtype),
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                                    F.tensor([0, 1], dtype=idtype))},
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        idtype=idtype, device=F.ctx())
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    g.nodes['user'].data['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
    g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())
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    g.edges['follows'].data['h'] = F.copy_to(F.tensor([1, 2, 3, 4, 5, 6], dtype=idtype), ctx=F.ctx())
    g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx())
    return g

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def create_test_heterograph5(idtype):
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    g = dgl.heterograph({
        ('user', 'follows', 'user'): (F.tensor([1, 2], dtype=idtype),
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                                      F.tensor([0, 1], dtype=idtype)),
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        ('user', 'plays', 'game'): (F.tensor([0, 1], dtype=idtype),
                                    F.tensor([0, 1], dtype=idtype))},
        idtype=idtype, device=F.ctx())
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    g.nodes['user'].data['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
    g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())
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    g.edges['follows'].data['h'] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx())
    g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx())
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    return g

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def get_redfn(name):
    return getattr(F, name)

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@parametrize_dtype
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def test_create(idtype):
    device = F.ctx()
    g0 = create_test_heterograph(idtype)
    g1 = create_test_heterograph1(idtype)
    g2 = create_test_heterograph2(idtype)
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    assert set(g0.ntypes) == set(g1.ntypes) == set(g2.ntypes)
    assert set(g0.canonical_etypes) == set(g1.canonical_etypes) == set(g2.canonical_etypes)
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    # Create a bipartite graph from a SciPy matrix
    src_ids = np.array([2, 3, 4])
    dst_ids = np.array([1, 2, 3])
    eweight = np.array([0.2, 0.3, 0.5])
    sp_mat = ssp.coo_matrix((eweight, (src_ids, dst_ids)))
    g = dgl.bipartite_from_scipy(sp_mat, utype='user', etype='plays',
                                 vtype='game', idtype=idtype, device=device)
    assert g.idtype == idtype
    assert g.device == device
    assert g.num_src_nodes() == 5
    assert g.num_dst_nodes() == 4
    assert g.num_edges() == 3
    src, dst = g.edges()
    assert F.allclose(src, F.tensor([2, 3, 4], dtype=idtype))
    assert F.allclose(dst, F.tensor([1, 2, 3], dtype=idtype))
    g = dgl.bipartite_from_scipy(sp_mat, utype='_U', etype='_E', vtype='_V',
                                 eweight_name='w', idtype=idtype, device=device)
    assert F.allclose(g.edata['w'], F.tensor(eweight))

    # Create a bipartite graph from a NetworkX graph
    nx_g = nx.DiGraph()
    nx_g.add_nodes_from([1, 3], bipartite=0, feat1=np.zeros((2)), feat2=np.ones((2)))
    nx_g.add_nodes_from([2, 4, 5], bipartite=1, feat3=np.zeros((3)))
    nx_g.add_edge(1, 4, weight=np.ones((1)), eid=np.array([1]))
    nx_g.add_edge(3, 5, weight=np.ones((1)), eid=np.array([0]))
    g = dgl.bipartite_from_networkx(nx_g, utype='user', etype='plays',
                                    vtype='game', idtype=idtype, device=device)
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    assert g.idtype == idtype
    assert g.device == device
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    assert g.num_src_nodes() == 2
    assert g.num_dst_nodes() == 3
    assert g.num_edges() == 2
    src, dst = g.edges()
    assert F.allclose(src, F.tensor([0, 1], dtype=idtype))
    assert F.allclose(dst, F.tensor([1, 2], dtype=idtype))
    g = dgl.bipartite_from_networkx(nx_g, utype='_U', etype='_E', vtype='V',
                                    u_attrs=['feat1', 'feat2'],
                                    e_attrs = ['weight'], v_attrs = ['feat3'])
    assert F.allclose(g.srcdata['feat1'], F.tensor(np.zeros((2, 2))))
    assert F.allclose(g.srcdata['feat2'], F.tensor(np.ones((2, 2))))
    assert F.allclose(g.dstdata['feat3'], F.tensor(np.zeros((3, 3))))
    assert F.allclose(g.edata['weight'], F.tensor(np.ones((2, 1))))
    g = dgl.bipartite_from_networkx(nx_g, utype='_U', etype='_E', vtype='V',
                                    edge_id_attr_name='eid', idtype=idtype, device=device)
    src, dst = g.edges()
    assert F.allclose(src, F.tensor([1, 0], dtype=idtype))
    assert F.allclose(dst, F.tensor([2, 1], dtype=idtype))
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    # create from scipy
    spmat = ssp.coo_matrix(([1,1,1], ([0, 0, 1], [2, 3, 2])), shape=(4, 4))
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    g = dgl.from_scipy(spmat, idtype=idtype, device=device)
    assert g.num_nodes() == 4
    assert g.num_edges() == 3
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    assert g.idtype == idtype
    assert g.device == device
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    # test inferring number of nodes for heterograph
    g = dgl.heterograph({
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        ('l0', 'e0', 'l1'): ([0, 0], [1, 2]),
        ('l0', 'e1', 'l2'): ([2], [2]),
        ('l2', 'e2', 'l2'): ([1, 3], [1, 3])
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        }, idtype=idtype, device=device)
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    assert g.num_nodes('l0') == 3
    assert g.num_nodes('l1') == 3
    assert g.num_nodes('l2') == 4
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    assert g.idtype == idtype
    assert g.device == device
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    # test if validate flag works
    # homo graph
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    with pytest.raises(DGLError):
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        g = dgl.graph(
            ([0, 0, 0, 1, 1, 2], [0, 1, 2, 0, 1, 2]),
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            num_nodes=2,
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            idtype=idtype, device=device
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        )
    # bipartite graph
    def _test_validate_bipartite(card):
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        with pytest.raises(DGLError):
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            g = dgl.heterograph({
                ('_U', '_E', '_V'): ([0, 0, 1, 1, 2], [1, 1, 2, 2, 3])
            }, {'_U': card[0], '_V': card[1]}, idtype=idtype, device=device)
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    _test_validate_bipartite((3, 3))
    _test_validate_bipartite((2, 4))

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    # test from_scipy
    num_nodes = 10
    density = 0.25
    for fmt in ['csr', 'coo', 'csc']:
        adj = rand(num_nodes, num_nodes, density=density, format=fmt)
        g = dgl.from_scipy(adj, eweight_name='w', idtype=idtype)
        assert g.idtype == idtype
        assert g.device == F.cpu()
        assert F.array_equal(g.edata['w'], F.copy_to(F.tensor(adj.data), F.cpu()))

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@parametrize_dtype
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def test_query(idtype):
    g = create_test_heterograph(idtype)
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    ntypes = ['user', 'game', 'developer']
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    canonical_etypes = [
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        ('user', 'follows', 'user'),
        ('user', 'plays', 'game'),
        ('user', 'wishes', 'game'),
        ('developer', 'develops', 'game')]
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    etypes = ['follows', 'plays', 'wishes', 'develops']
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    # node & edge types
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    assert set(ntypes) == set(g.ntypes)
    assert set(etypes) == set(g.etypes)
    assert set(canonical_etypes) == set(g.canonical_etypes)
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    # metagraph
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    mg = g.metagraph()
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    assert set(g.ntypes) == set(mg.nodes)
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    etype_triplets = [(u, v, e) for u, v, e in mg.edges(keys=True)]
    assert set([
        ('user', 'user', 'follows'),
        ('user', 'game', 'plays'),
        ('user', 'game', 'wishes'),
        ('developer', 'game', 'develops')]) == set(etype_triplets)
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    for i in range(len(etypes)):
        assert g.to_canonical_etype(etypes[i]) == canonical_etypes[i]
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    def _test(g):
        # number of nodes
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        assert [g.num_nodes(ntype) for ntype in ntypes] == [3, 2, 2]
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        # number of edges
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        assert [g.num_edges(etype) for etype in etypes] == [2, 4, 2, 2]
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        # has_node & has_nodes
        for ntype in ntypes:
            n = g.number_of_nodes(ntype)
            for i in range(n):
                assert g.has_node(i, ntype)
            assert not g.has_node(n, ntype)
            assert np.array_equal(
                F.asnumpy(g.has_nodes([0, n], ntype)).astype('int32'), [1, 0])
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        assert not g.is_multigraph
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        for etype in etypes:
            srcs, dsts = edges[etype]
            for src, dst in zip(srcs, dsts):
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                assert g.has_edges_between(src, dst, etype)
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            assert F.asnumpy(g.has_edges_between(srcs, dsts, etype)).all()

            srcs, dsts = negative_edges[etype]
            for src, dst in zip(srcs, dsts):
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                assert not g.has_edges_between(src, dst, etype)
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            assert not F.asnumpy(g.has_edges_between(srcs, dsts, etype)).any()

            srcs, dsts = edges[etype]
            n_edges = len(srcs)

            # predecessors & in_edges & in_degree
            pred = [s for s, d in zip(srcs, dsts) if d == 0]
            assert set(F.asnumpy(g.predecessors(0, etype)).tolist()) == set(pred)
            u, v = g.in_edges([0], etype=etype)
            assert F.asnumpy(v).tolist() == [0] * len(pred)
            assert set(F.asnumpy(u).tolist()) == set(pred)
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            assert g.in_degrees(0, etype) == len(pred)
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            # successors & out_edges & out_degree
            succ = [d for s, d in zip(srcs, dsts) if s == 0]
            assert set(F.asnumpy(g.successors(0, etype)).tolist()) == set(succ)
            u, v = g.out_edges([0], etype=etype)
            assert F.asnumpy(u).tolist() == [0] * len(succ)
            assert set(F.asnumpy(v).tolist()) == set(succ)
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            assert g.out_degrees(0, etype) == len(succ)
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            # edge_id & edge_ids
            for i, (src, dst) in enumerate(zip(srcs, dsts)):
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                assert g.edge_ids(src, dst, etype=etype) == i
                _, _, eid = g.edge_ids(src, dst, etype=etype, return_uv=True)
                assert eid == i
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            assert F.asnumpy(g.edge_ids(srcs, dsts, etype=etype)).tolist() == list(range(n_edges))
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            u, v, e = g.edge_ids(srcs, dsts, etype=etype, return_uv=True)
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            u, v, e = F.asnumpy(u), F.asnumpy(v), F.asnumpy(e)
            assert u[e].tolist() == srcs
            assert v[e].tolist() == dsts
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            # find_edges
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            for eid in [list(range(n_edges)), np.arange(n_edges), F.astype(F.arange(0, n_edges), g.idtype)]:
                u, v = g.find_edges(eid, etype)
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                assert F.asnumpy(u).tolist() == srcs
                assert F.asnumpy(v).tolist() == dsts
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            # all_edges.
            for order in ['eid']:
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                u, v, e = g.edges('all', order, etype)
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                assert F.asnumpy(u).tolist() == srcs
                assert F.asnumpy(v).tolist() == dsts
                assert F.asnumpy(e).tolist() == list(range(n_edges))

            # in_degrees & out_degrees
            in_degrees = F.asnumpy(g.in_degrees(etype=etype))
            out_degrees = F.asnumpy(g.out_degrees(etype=etype))
            src_count = Counter(srcs)
            dst_count = Counter(dsts)
            utype, _, vtype = g.to_canonical_etype(etype)
            for i in range(g.number_of_nodes(utype)):
                assert out_degrees[i] == src_count[i]
            for i in range(g.number_of_nodes(vtype)):
                assert in_degrees[i] == dst_count[i]

    edges = {
        'follows': ([0, 1], [1, 2]),
        'plays': ([0, 1, 2, 1], [0, 0, 1, 1]),
        'wishes': ([0, 2], [1, 0]),
        'develops': ([0, 1], [0, 1]),
    }
    # edges that does not exist in the graph
    negative_edges = {
        'follows': ([0, 1], [0, 1]),
        'plays': ([0, 2], [1, 0]),
        'wishes': ([0, 1], [0, 1]),
        'develops': ([0, 1], [1, 0]),
    }
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    g = create_test_heterograph(idtype)
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    _test(g)
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    g = create_test_heterograph1(idtype)
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    _test(g)
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    if F._default_context_str != 'gpu':
        # XXX: CUDA COO operators have not been live yet.
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        g = create_test_heterograph2(idtype)
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        _test(g)
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    etypes = canonical_etypes
    edges = {
        ('user', 'follows', 'user'): ([0, 1], [1, 2]),
        ('user', 'plays', 'game'): ([0, 1, 2, 1], [0, 0, 1, 1]),
        ('user', 'wishes', 'game'): ([0, 2], [1, 0]),
        ('developer', 'develops', 'game'): ([0, 1], [0, 1]),
    }
    # edges that does not exist in the graph
    negative_edges = {
        ('user', 'follows', 'user'): ([0, 1], [0, 1]),
        ('user', 'plays', 'game'): ([0, 2], [1, 0]),
        ('user', 'wishes', 'game'): ([0, 1], [0, 1]),
        ('developer', 'develops', 'game'): ([0, 1], [1, 0]),
        }
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    g = create_test_heterograph(idtype)
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    _test(g)
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    g = create_test_heterograph1(idtype)
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    _test(g)
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    if F._default_context_str != 'gpu':
        # XXX: CUDA COO operators have not been live yet.
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        g = create_test_heterograph2(idtype)
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        _test(g)
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    # test repr
    print(g)

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@parametrize_dtype
def test_empty_query(idtype):
    g = dgl.graph(([1, 2, 3], [0, 4, 5]), idtype=idtype, device=F.ctx())
    g.add_nodes(0)
    g.add_edges([], [])
    g.remove_edges([])
    g.remove_nodes([])
    assert F.shape(g.has_nodes([])) == (0,)
    assert F.shape(g.has_edges_between([], [])) == (0,)
    g.edge_ids([], [])
    g.edge_ids([], [], return_uv=True)
    g.find_edges([])

    assert F.shape(g.in_edges([], form='eid')) == (0,)
    u, v = g.in_edges([], form='uv')
    assert F.shape(u) == (0,)
    assert F.shape(v) == (0,)
    u, v, e = g.in_edges([], form='all')
    assert F.shape(u) == (0,)
    assert F.shape(v) == (0,)
    assert F.shape(e) == (0,)

    assert F.shape(g.out_edges([], form='eid')) == (0,)
    u, v = g.out_edges([], form='uv')
    assert F.shape(u) == (0,)
    assert F.shape(v) == (0,)
    u, v, e = g.out_edges([], form='all')
    assert F.shape(u) == (0,)
    assert F.shape(v) == (0,)
    assert F.shape(e) == (0,)

    assert F.shape(g.in_degrees([])) == (0,)
    assert F.shape(g.out_degrees([])) == (0,)

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@unittest.skipIf(F._default_context_str == 'gpu', reason="GPU does not have COO impl.")
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def _test_hypersparse():
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    N1 = 1 << 50        # should crash if allocated a CSR
    N2 = 1 << 48

    g = dgl.heterograph({
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        ('user', 'follows', 'user'): (F.tensor([0], F.int64), F.tensor([1], F.int64)),
        ('user', 'plays', 'game'): (F.tensor([0], F.int64), F.tensor([N2], F.int64))},
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        {'user': N1, 'game': N1},
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        device=F.ctx())
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    assert g.number_of_nodes('user') == N1
    assert g.number_of_nodes('game') == N1
    assert g.number_of_edges('follows') == 1
    assert g.number_of_edges('plays') == 1

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    assert g.has_edges_between(0, 1, 'follows')
    assert not g.has_edges_between(0, 0, 'follows')
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    mask = F.asnumpy(g.has_edges_between([0, 0], [0, 1], 'follows')).tolist()
    assert mask == [0, 1]

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    assert g.has_edges_between(0, N2, 'plays')
    assert not g.has_edges_between(0, 0, 'plays')
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    mask = F.asnumpy(g.has_edges_between([0, 0], [0, N2], 'plays')).tolist()
    assert mask == [0, 1]

    assert F.asnumpy(g.predecessors(0, 'follows')).tolist() == []
    assert F.asnumpy(g.successors(0, 'follows')).tolist() == [1]
    assert F.asnumpy(g.predecessors(1, 'follows')).tolist() == [0]
    assert F.asnumpy(g.successors(1, 'follows')).tolist() == []

    assert F.asnumpy(g.predecessors(0, 'plays')).tolist() == []
    assert F.asnumpy(g.successors(0, 'plays')).tolist() == [N2]
    assert F.asnumpy(g.predecessors(N2, 'plays')).tolist() == [0]
    assert F.asnumpy(g.successors(N2, 'plays')).tolist() == []

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    assert g.edge_ids(0, 1, etype='follows') == 0
    assert g.edge_ids(0, N2, etype='plays') == 0
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    u, v = g.find_edges([0], 'follows')
    assert F.asnumpy(u).tolist() == [0]
    assert F.asnumpy(v).tolist() == [1]
    u, v = g.find_edges([0], 'plays')
    assert F.asnumpy(u).tolist() == [0]
    assert F.asnumpy(v).tolist() == [N2]
    u, v, e = g.all_edges('all', 'eid', 'follows')
    assert F.asnumpy(u).tolist() == [0]
    assert F.asnumpy(v).tolist() == [1]
    assert F.asnumpy(e).tolist() == [0]
    u, v, e = g.all_edges('all', 'eid', 'plays')
    assert F.asnumpy(u).tolist() == [0]
    assert F.asnumpy(v).tolist() == [N2]
    assert F.asnumpy(e).tolist() == [0]

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    assert g.in_degrees(0, 'follows') == 0
    assert g.in_degrees(1, 'follows') == 1
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    assert F.asnumpy(g.in_degrees([0, 1], 'follows')).tolist() == [0, 1]
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    assert g.in_degrees(0, 'plays') == 0
    assert g.in_degrees(N2, 'plays') == 1
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    assert F.asnumpy(g.in_degrees([0, N2], 'plays')).tolist() == [0, 1]
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    assert g.out_degrees(0, 'follows') == 1
    assert g.out_degrees(1, 'follows') == 0
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    assert F.asnumpy(g.out_degrees([0, 1], 'follows')).tolist() == [1, 0]
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    assert g.out_degrees(0, 'plays') == 1
    assert g.out_degrees(N2, 'plays') == 0
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    assert F.asnumpy(g.out_degrees([0, N2], 'plays')).tolist() == [1, 0]

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def _test_edge_ids():
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    N1 = 1 << 50        # should crash if allocated a CSR
    N2 = 1 << 48

    g = dgl.heterograph({
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        ('user', 'follows', 'user'): (F.tensor([0], F.int64), F.tensor([1], F.int64)),
        ('user', 'plays', 'game'): (F.tensor([0], F.int64), F.tensor([N2], F.int64))},
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        {'user': N1, 'game': N1})
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    with pytest.raises(DGLError):
        eid = g.edge_ids(0, 0, etype='follows')
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    g2 = dgl.heterograph({
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        ('user', 'follows', 'user'): (F.tensor([0, 0], F.int64), F.tensor([1, 1], F.int64)),
        ('user', 'plays', 'game'): (F.tensor([0], F.int64), F.tensor([N2], F.int64))},
        {'user': N1, 'game': N1}, device=F.cpu())
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    eid = g2.edge_ids(0, 1, etype='follows')
    assert eid == 0
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@parametrize_dtype
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def test_adj(idtype):
    g = create_test_heterograph(idtype)
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    adj = F.sparse_to_numpy(g.adj(transpose=False, etype='follows'))
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    assert np.allclose(
            adj,
            np.array([[0., 0., 0.],
                      [1., 0., 0.],
                      [0., 1., 0.]]))
    adj = F.sparse_to_numpy(g.adj(transpose=True, etype='follows'))
    assert np.allclose(
            adj,
            np.array([[0., 1., 0.],
                      [0., 0., 1.],
                      [0., 0., 0.]]))
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    adj = F.sparse_to_numpy(g.adj(transpose=False, etype='plays'))
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    assert np.allclose(
            adj,
            np.array([[1., 1., 0.],
                      [0., 1., 1.]]))
    adj = F.sparse_to_numpy(g.adj(transpose=True, etype='plays'))
    assert np.allclose(
            adj,
            np.array([[1., 0.],
                      [1., 1.],
                      [0., 1.]]))

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    adj = g.adj(transpose=False, scipy_fmt='csr', etype='follows')
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    assert np.allclose(
            adj.todense(),
            np.array([[0., 0., 0.],
                      [1., 0., 0.],
                      [0., 1., 0.]]))
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    adj = g.adj(transpose=False, scipy_fmt='coo', etype='follows')
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    assert np.allclose(
            adj.todense(),
            np.array([[0., 0., 0.],
                      [1., 0., 0.],
                      [0., 1., 0.]]))
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    adj = g.adj(transpose=False, scipy_fmt='csr', etype='plays')
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    assert np.allclose(
            adj.todense(),
            np.array([[1., 1., 0.],
                      [0., 1., 1.]]))
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    adj = g.adj(transpose=False, scipy_fmt='coo', etype='plays')
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    assert np.allclose(
            adj.todense(),
            np.array([[1., 1., 0.],
                      [0., 1., 1.]]))
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    adj = F.sparse_to_numpy(g['follows'].adj(transpose=False))
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    assert np.allclose(
            adj,
            np.array([[0., 0., 0.],
                      [1., 0., 0.],
                      [0., 1., 0.]]))

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@parametrize_dtype
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def test_inc(idtype):
    g = create_test_heterograph(idtype)
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    adj = F.sparse_to_numpy(g['follows'].inc('in'))
    assert np.allclose(
            adj,
            np.array([[0., 0.],
                      [1., 0.],
                      [0., 1.]]))
    adj = F.sparse_to_numpy(g['follows'].inc('out'))
    assert np.allclose(
            adj,
            np.array([[1., 0.],
                      [0., 1.],
                      [0., 0.]]))
    adj = F.sparse_to_numpy(g['follows'].inc('both'))
    assert np.allclose(
            adj,
            np.array([[-1., 0.],
                      [1., -1.],
                      [0., 1.]]))
    adj = F.sparse_to_numpy(g.inc('in', etype='plays'))
    assert np.allclose(
            adj,
            np.array([[1., 1., 0., 0.],
                      [0., 0., 1., 1.]]))
    adj = F.sparse_to_numpy(g.inc('out', etype='plays'))
    assert np.allclose(
            adj,
            np.array([[1., 0., 0., 0.],
                      [0., 1., 0., 1.],
                      [0., 0., 1., 0.]]))
    adj = F.sparse_to_numpy(g.inc('both', etype='follows'))
    assert np.allclose(
            adj,
            np.array([[-1., 0.],
                      [1., -1.],
                      [0., 1.]]))
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@parametrize_dtype
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def test_view(idtype):
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    # test single node type
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    g = dgl.heterograph({
        ('user', 'follows', 'user'): ([0, 1], [1, 2])
    }, idtype=idtype, device=F.ctx())
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    f1 = F.randn((3, 6))
    g.ndata['h'] = f1
    f2 = g.nodes['user'].data['h']
    assert F.array_equal(f1, f2)
    fail = False
    try:
        g.ndata['h'] = {'user' : f1}
    except Exception:
        fail = True
    assert fail

    # test single edge type
    f3 = F.randn((2, 4))
    g.edata['h'] = f3
    f4 = g.edges['follows'].data['h']
    assert F.array_equal(f3, f4)
    fail = False
    try:
        g.edata['h'] = {'follows' : f3}
    except Exception:
        fail = True
    assert fail

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    # test data view
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    g = create_test_heterograph(idtype)
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    f1 = F.randn((3, 6))
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    g.nodes['user'].data['h'] = f1       # ok
    f2 = g.nodes['user'].data['h']
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    assert F.array_equal(f1, f2)
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    assert F.array_equal(g.nodes('user'), F.arange(0, 3, idtype))
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    g.nodes['user'].data.pop('h')

    # multi type ndata
    f1 = F.randn((3, 6))
    f2 = F.randn((2, 6))
    fail = False
    try:
        g.ndata['h'] = f1
    except Exception:
        fail = True
    assert fail
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    f3 = F.randn((2, 4))
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    g.edges['user', 'follows', 'user'].data['h'] = f3
    f4 = g.edges['user', 'follows', 'user'].data['h']
    f5 = g.edges['follows'].data['h']
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    assert F.array_equal(f3, f4)
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    assert F.array_equal(f3, f5)
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    assert F.array_equal(g.edges(etype='follows', form='eid'), F.arange(0, 2, idtype))
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    g.edges['follows'].data.pop('h')

    f3 = F.randn((2, 4))
    fail = False
    try:
        g.edata['h'] = f3
    except Exception:
        fail = True
    assert fail

    # test srcdata
    f1 = F.randn((3, 6))
    g.srcnodes['user'].data['h'] = f1       # ok
    f2 = g.srcnodes['user'].data['h']
    assert F.array_equal(f1, f2)
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    assert F.array_equal(g.srcnodes('user'), F.arange(0, 3, idtype))
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    g.srcnodes['user'].data.pop('h')

    # test dstdata
    f1 = F.randn((3, 6))
    g.dstnodes['user'].data['h'] = f1       # ok
    f2 = g.dstnodes['user'].data['h']
    assert F.array_equal(f1, f2)
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    assert F.array_equal(g.dstnodes('user'), F.arange(0, 3, idtype))
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    g.dstnodes['user'].data.pop('h')

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@parametrize_dtype
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def test_view1(idtype):
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    # test relation view
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    HG = create_test_heterograph(idtype)
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    ntypes = ['user', 'game', 'developer']
    canonical_etypes = [
        ('user', 'follows', 'user'),
        ('user', 'plays', 'game'),
        ('user', 'wishes', 'game'),
        ('developer', 'develops', 'game')]
    etypes = ['follows', 'plays', 'wishes', 'develops']

    def _test_query():
        for etype in etypes:
            utype, _, vtype = HG.to_canonical_etype(etype)
            g = HG[etype]
            srcs, dsts = edges[etype]
            for src, dst in zip(srcs, dsts):
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                assert g.has_edges_between(src, dst)
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            assert F.asnumpy(g.has_edges_between(srcs, dsts)).all()

            srcs, dsts = negative_edges[etype]
            for src, dst in zip(srcs, dsts):
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                assert not g.has_edges_between(src, dst)
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            assert not F.asnumpy(g.has_edges_between(srcs, dsts)).any()

            srcs, dsts = edges[etype]
            n_edges = len(srcs)

            # predecessors & in_edges & in_degree
            pred = [s for s, d in zip(srcs, dsts) if d == 0]
            assert set(F.asnumpy(g.predecessors(0)).tolist()) == set(pred)
            u, v = g.in_edges([0])
            assert F.asnumpy(v).tolist() == [0] * len(pred)
            assert set(F.asnumpy(u).tolist()) == set(pred)
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            assert g.in_degrees(0) == len(pred)
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            # successors & out_edges & out_degree
            succ = [d for s, d in zip(srcs, dsts) if s == 0]
            assert set(F.asnumpy(g.successors(0)).tolist()) == set(succ)
            u, v = g.out_edges([0])
            assert F.asnumpy(u).tolist() == [0] * len(succ)
            assert set(F.asnumpy(v).tolist()) == set(succ)
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            assert g.out_degrees(0) == len(succ)
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            # edge_id & edge_ids
            for i, (src, dst) in enumerate(zip(srcs, dsts)):
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                assert g.edge_ids(src, dst, etype=etype) == i
                _, _, eid = g.edge_ids(src, dst, etype=etype, return_uv=True)
                assert eid == i
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            assert F.asnumpy(g.edge_ids(srcs, dsts)).tolist() == list(range(n_edges))
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            u, v, e = g.edge_ids(srcs, dsts, return_uv=True)
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            u, v, e = F.asnumpy(u), F.asnumpy(v), F.asnumpy(e)
            assert u[e].tolist() == srcs
            assert v[e].tolist() == dsts
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            # find_edges
            u, v = g.find_edges(list(range(n_edges)))
            assert F.asnumpy(u).tolist() == srcs
            assert F.asnumpy(v).tolist() == dsts

            # all_edges.
            for order in ['eid']:
                u, v, e = g.all_edges(form='all', order=order)
                assert F.asnumpy(u).tolist() == srcs
                assert F.asnumpy(v).tolist() == dsts
                assert F.asnumpy(e).tolist() == list(range(n_edges))

            # in_degrees & out_degrees
            in_degrees = F.asnumpy(g.in_degrees())
            out_degrees = F.asnumpy(g.out_degrees())
            src_count = Counter(srcs)
            dst_count = Counter(dsts)
            for i in range(g.number_of_nodes(utype)):
                assert out_degrees[i] == src_count[i]
            for i in range(g.number_of_nodes(vtype)):
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                assert in_degrees[i] == dst_count[i]
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    edges = {
        'follows': ([0, 1], [1, 2]),
        'plays': ([0, 1, 2, 1], [0, 0, 1, 1]),
        'wishes': ([0, 2], [1, 0]),
        'develops': ([0, 1], [0, 1]),
    }
    # edges that does not exist in the graph
    negative_edges = {
        'follows': ([0, 1], [0, 1]),
        'plays': ([0, 2], [1, 0]),
        'wishes': ([0, 1], [0, 1]),
        'develops': ([0, 1], [1, 0]),
    }
    _test_query()
    etypes = canonical_etypes
    edges = {
        ('user', 'follows', 'user'): ([0, 1], [1, 2]),
        ('user', 'plays', 'game'): ([0, 1, 2, 1], [0, 0, 1, 1]),
        ('user', 'wishes', 'game'): ([0, 2], [1, 0]),
        ('developer', 'develops', 'game'): ([0, 1], [0, 1]),
    }
    # edges that does not exist in the graph
    negative_edges = {
        ('user', 'follows', 'user'): ([0, 1], [0, 1]),
        ('user', 'plays', 'game'): ([0, 2], [1, 0]),
        ('user', 'wishes', 'game'): ([0, 1], [0, 1]),
        ('developer', 'develops', 'game'): ([0, 1], [1, 0]),
        }
    _test_query()

    # test features
    HG.nodes['user'].data['h'] = F.ones((HG.number_of_nodes('user'), 5))
    HG.nodes['game'].data['m'] = F.ones((HG.number_of_nodes('game'), 3)) * 2

    # test only one node type
    g = HG['follows']
    assert g.number_of_nodes() == 3

    # test ndata and edata
    f1 = F.randn((3, 6))
    g.ndata['h'] = f1       # ok
    f2 = HG.nodes['user'].data['h']
    assert F.array_equal(f1, f2)
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    assert F.array_equal(g.nodes(), F.arange(0, 3, g.idtype))
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    f3 = F.randn((2, 4))
    g.edata['h'] = f3
    f4 = HG.edges['follows'].data['h']
    assert F.array_equal(f3, f4)
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    assert F.array_equal(g.edges(form='eid'), F.arange(0, 2, g.idtype))
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@parametrize_dtype
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def test_flatten(idtype):
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    def check_mapping(g, fg):
        if len(fg.ntypes) == 1:
            SRC = DST = fg.ntypes[0]
        else:
            SRC = fg.ntypes[0]
            DST = fg.ntypes[1]

        etypes = F.asnumpy(fg.edata[dgl.ETYPE]).tolist()
        eids = F.asnumpy(fg.edata[dgl.EID]).tolist()

        for i, (etype, eid) in enumerate(zip(etypes, eids)):
            src_g, dst_g = g.find_edges([eid], g.canonical_etypes[etype])
            src_fg, dst_fg = fg.find_edges([i])
            # TODO(gq): I feel this code is quite redundant; can we just add new members (like
            # "induced_srcid") to returned heterograph object and not store them as features?
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            assert F.asnumpy(src_g) == F.asnumpy(F.gather_row(fg.nodes[SRC].data[dgl.NID], src_fg)[0])
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            tid = F.asnumpy(F.gather_row(fg.nodes[SRC].data[dgl.NTYPE], src_fg)).item()
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            assert g.canonical_etypes[etype][0] == g.ntypes[tid]
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            assert F.asnumpy(dst_g) == F.asnumpy(F.gather_row(fg.nodes[DST].data[dgl.NID], dst_fg)[0])
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            tid = F.asnumpy(F.gather_row(fg.nodes[DST].data[dgl.NTYPE], dst_fg)).item()
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            assert g.canonical_etypes[etype][2] == g.ntypes[tid]

    # check for wildcard slices
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    g = create_test_heterograph(idtype)
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    g.nodes['user'].data['h'] = F.ones((3, 5))
    g.nodes['game'].data['i'] = F.ones((2, 5))
    g.edges['plays'].data['e'] = F.ones((4, 4))
    g.edges['wishes'].data['e'] = F.ones((2, 4))
    g.edges['wishes'].data['f'] = F.ones((2, 4))

    fg = g['user', :, 'game']   # user--plays->game and user--wishes->game
    assert len(fg.ntypes) == 2
    assert fg.ntypes == ['user', 'game']
    assert fg.etypes == ['plays+wishes']
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    assert fg.idtype == g.idtype
    assert fg.device == g.device
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    etype = fg.etypes[0]
    assert fg[etype] is not None        # Issue #2166
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    assert F.array_equal(fg.nodes['user'].data['h'], F.ones((3, 5)))
    assert F.array_equal(fg.nodes['game'].data['i'], F.ones((2, 5)))
    assert F.array_equal(fg.edata['e'], F.ones((6, 4)))
    assert 'f' not in fg.edata

    etypes = F.asnumpy(fg.edata[dgl.ETYPE]).tolist()
    eids = F.asnumpy(fg.edata[dgl.EID]).tolist()
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    assert set(zip(etypes, eids)) == set([(3, 0), (3, 1), (2, 1), (2, 0), (2, 3), (2, 2)])
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    check_mapping(g, fg)

    fg = g['user', :, 'user']
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    assert fg.idtype == g.idtype
    assert fg.device == g.device
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    # NOTE(gq): The node/edge types from the parent graph is returned if there is only one
    # node/edge type.  This differs from the behavior above.
    assert fg.ntypes == ['user']
    assert fg.etypes == ['follows']
    u1, v1 = g.edges(etype='follows', order='eid')
    u2, v2 = fg.edges(etype='follows', order='eid')
    assert F.array_equal(u1, u2)
    assert F.array_equal(v1, v2)

    fg = g['developer', :, 'game']
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    assert fg.idtype == g.idtype
    assert fg.device == g.device
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    assert fg.ntypes == ['developer', 'game']
    assert fg.etypes == ['develops']
    u1, v1 = g.edges(etype='develops', order='eid')
    u2, v2 = fg.edges(etype='develops', order='eid')
    assert F.array_equal(u1, u2)
    assert F.array_equal(v1, v2)

    fg = g[:, :, :]
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    assert fg.idtype == g.idtype
    assert fg.device == g.device
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    assert fg.ntypes == ['developer+user', 'game+user']
    assert fg.etypes == ['develops+follows+plays+wishes']
    check_mapping(g, fg)

    # Test another heterograph
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    g = dgl.heterograph({
        ('user', 'follows', 'user'): ([0, 1, 2], [1, 2, 3]),
        ('user', 'knows', 'user'): ([0, 2], [2, 3])
    }, idtype=idtype, device=F.ctx())
    g.nodes['user'].data['h'] = F.randn((4, 3))
    g.edges['follows'].data['w'] = F.randn((3, 2))
    g.nodes['user'].data['hh'] = F.randn((4, 5))
    g.edges['knows'].data['ww'] = F.randn((2, 10))
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    fg = g['user', :, 'user']
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    assert fg.idtype == g.idtype
    assert fg.device == g.device
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    assert fg.ntypes == ['user']
    assert fg.etypes == ['follows+knows']
    check_mapping(g, fg)

    fg = g['user', :, :]
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    assert fg.idtype == g.idtype
    assert fg.device == g.device
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    assert fg.ntypes == ['user']
    assert fg.etypes == ['follows+knows']
    check_mapping(g, fg)

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@unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
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@parametrize_dtype
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def test_to_device(idtype):
    # TODO: rewrite this test case to accept different graphs so we
    #  can test reverse graph and batched graph
    g = create_test_heterograph(idtype)
    g.nodes['user'].data['h'] = F.ones((3, 5))
    g.nodes['game'].data['i'] = F.ones((2, 5))
    g.edges['plays'].data['e'] = F.ones((4, 4))
    assert g.device == F.ctx()
    g = g.to(F.cpu())
    assert g.device == F.cpu()
    assert F.context(g.nodes['user'].data['h']) == F.cpu()
    assert F.context(g.nodes['game'].data['i']) == F.cpu()
    assert F.context(g.edges['plays'].data['e']) == F.cpu()
    for ntype in g.ntypes:
        assert F.context(g.batch_num_nodes(ntype)) == F.cpu()
    for etype in g.canonical_etypes:
        assert F.context(g.batch_num_edges(etype)) == F.cpu()

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    if F.is_cuda_available():
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        g1 = g.to(F.cuda())
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        assert g1.device == F.cuda()
        assert F.context(g1.nodes['user'].data['h']) == F.cuda()
        assert F.context(g1.nodes['game'].data['i']) == F.cuda()
        assert F.context(g1.edges['plays'].data['e']) == F.cuda()
        for ntype in g1.ntypes:
            assert F.context(g1.batch_num_nodes(ntype)) == F.cuda()
        for etype in g1.canonical_etypes:
            assert F.context(g1.batch_num_edges(etype)) == F.cuda()
        assert F.context(g.nodes['user'].data['h']) == F.cpu()
        assert F.context(g.nodes['game'].data['i']) == F.cpu()
        assert F.context(g.edges['plays'].data['e']) == F.cpu()
        for ntype in g.ntypes:
            assert F.context(g.batch_num_nodes(ntype)) == F.cpu()
        for etype in g.canonical_etypes:
            assert F.context(g.batch_num_edges(etype)) == F.cpu()
        with pytest.raises(DGLError):
            g1.nodes['user'].data['h'] = F.copy_to(F.ones((3, 5)), F.cpu())
        with pytest.raises(DGLError):
            g1.edges['plays'].data['e'] = F.copy_to(F.ones((4, 4)), F.cpu())
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@unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
@parametrize_dtype
@pytest.mark.parametrize('g', get_cases(['block']))
def test_to_device2(g, idtype):
    g = g.astype(idtype)
    g = g.to(F.cpu())
    assert g.device == F.cpu()
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    if F.is_cuda_available():
        g1 = g.to(F.cuda())
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        assert g1.device == F.cuda()
        assert g1.ntypes == g.ntypes
        assert g1.etypes == g.etypes
        assert g1.canonical_etypes == g.canonical_etypes
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@parametrize_dtype
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def test_convert_bound(idtype):
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    def _test_bipartite_bound(data, card):
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        with pytest.raises(DGLError):
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            dgl.heterograph({
                ('_U', '_E', '_V'): data
            }, {'_U': card[0], '_V': card[1]}, idtype=idtype, device=F.ctx())
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    def _test_graph_bound(data, card):
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        with pytest.raises(DGLError):
            dgl.graph(data, num_nodes=card, idtype=idtype, device=F.ctx())
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    _test_bipartite_bound(([1, 2], [1, 2]), (2, 3))
    _test_bipartite_bound(([0, 1], [1, 4]), (2, 3))
    _test_graph_bound(([1, 3], [1, 2]), 3)
    _test_graph_bound(([0, 1], [1, 3]), 3)
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@parametrize_dtype
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def test_convert(idtype):
    hg = create_test_heterograph(idtype)
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    hs = []
    for ntype in hg.ntypes:
        h = F.randn((hg.number_of_nodes(ntype), 5))
        hg.nodes[ntype].data['h'] = h
        hs.append(h)
    hg.nodes['user'].data['x'] = F.randn((3, 3))
    ws = []
    for etype in hg.canonical_etypes:
        w = F.randn((hg.number_of_edges(etype), 5))
        hg.edges[etype].data['w'] = w
        ws.append(w)
    hg.edges['plays'].data['x'] = F.randn((4, 3))

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    g = dgl.to_homogeneous(hg, ndata=['h'], edata=['w'])
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    assert g.idtype == idtype
    assert g.device == hg.device
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    assert F.array_equal(F.cat(hs, dim=0), g.ndata['h'])
    assert 'x' not in g.ndata
    assert F.array_equal(F.cat(ws, dim=0), g.edata['w'])
    assert 'x' not in g.edata

    src, dst = g.all_edges(order='eid')
    src = F.asnumpy(src)
    dst = F.asnumpy(dst)
    etype_id, eid = F.asnumpy(g.edata[dgl.ETYPE]), F.asnumpy(g.edata[dgl.EID])
    ntype_id, nid = F.asnumpy(g.ndata[dgl.NTYPE]), F.asnumpy(g.ndata[dgl.NID])
    for i in range(g.number_of_edges()):
        srctype = hg.ntypes[ntype_id[src[i]]]
        dsttype = hg.ntypes[ntype_id[dst[i]]]
        etype = hg.etypes[etype_id[i]]
        src_i, dst_i = hg.find_edges([eid[i]], (srctype, etype, dsttype))
        assert np.asscalar(F.asnumpy(src_i)) == nid[src[i]]
        assert np.asscalar(F.asnumpy(dst_i)) == nid[dst[i]]

    mg = nx.MultiDiGraph([
        ('user', 'user', 'follows'),
        ('user', 'game', 'plays'),
        ('user', 'game', 'wishes'),
        ('developer', 'game', 'develops')])

    for _mg in [None, mg]:
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        hg2 = dgl.to_heterogeneous(
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                g, hg.ntypes, hg.etypes,
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                ntype_field=dgl.NTYPE, etype_field=dgl.ETYPE, metagraph=_mg)
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        assert hg2.idtype == hg.idtype
        assert hg2.device == hg.device
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        assert set(hg.ntypes) == set(hg2.ntypes)
        assert set(hg.canonical_etypes) == set(hg2.canonical_etypes)
        for ntype in hg.ntypes:
            assert hg.number_of_nodes(ntype) == hg2.number_of_nodes(ntype)
            assert F.array_equal(hg.nodes[ntype].data['h'], hg2.nodes[ntype].data['h'])
        for canonical_etype in hg.canonical_etypes:
            src, dst = hg.all_edges(etype=canonical_etype, order='eid')
            src2, dst2 = hg2.all_edges(etype=canonical_etype, order='eid')
            assert F.array_equal(src, src2)
            assert F.array_equal(dst, dst2)
            assert F.array_equal(hg.edges[canonical_etype].data['w'], hg2.edges[canonical_etype].data['w'])

    # hetero_from_homo test case 2
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    g = dgl.graph(([0, 1, 2, 0], [2, 2, 3, 3]), idtype=idtype, device=F.ctx())
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    g.ndata[dgl.NTYPE] = F.tensor([0, 0, 1, 2])
    g.edata[dgl.ETYPE] = F.tensor([0, 0, 1, 2])
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    hg = dgl.to_heterogeneous(g, ['l0', 'l1', 'l2'], ['e0', 'e1', 'e2'])
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    assert hg.idtype == idtype
    assert hg.device == g.device
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    assert set(hg.canonical_etypes) == set(
        [('l0', 'e0', 'l1'), ('l1', 'e1', 'l2'), ('l0', 'e2', 'l2')])
    assert hg.number_of_nodes('l0') == 2
    assert hg.number_of_nodes('l1') == 1
    assert hg.number_of_nodes('l2') == 1
    assert hg.number_of_edges('e0') == 2
    assert hg.number_of_edges('e1') == 1
    assert hg.number_of_edges('e2') == 1
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    assert F.array_equal(hg.ndata[dgl.NID]['l0'], F.tensor([0, 1], F.int64))
    assert F.array_equal(hg.ndata[dgl.NID]['l1'], F.tensor([2], F.int64))
    assert F.array_equal(hg.ndata[dgl.NID]['l2'], F.tensor([3], F.int64))
    assert F.array_equal(hg.edata[dgl.EID][('l0', 'e0', 'l1')], F.tensor([0, 1], F.int64))
    assert F.array_equal(hg.edata[dgl.EID][('l0', 'e2', 'l2')], F.tensor([3], F.int64))
    assert F.array_equal(hg.edata[dgl.EID][('l1', 'e1', 'l2')], F.tensor([2], F.int64))
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    # hetero_from_homo test case 3
    mg = nx.MultiDiGraph([
        ('user', 'movie', 'watches'),
        ('user', 'TV', 'watches')])
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    g = dgl.graph(((0, 0), (1, 2)), idtype=idtype, device=F.ctx())
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    g.ndata[dgl.NTYPE] = F.tensor([0, 1, 2])
    g.edata[dgl.ETYPE] = F.tensor([0, 0])
    for _mg in [None, mg]:
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        hg = dgl.to_heterogeneous(g, ['user', 'TV', 'movie'], ['watches'], metagraph=_mg)
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        assert hg.idtype == g.idtype
        assert hg.device == g.device
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        assert set(hg.canonical_etypes) == set(
            [('user', 'watches', 'movie'), ('user', 'watches', 'TV')])
        assert hg.number_of_nodes('user') == 1
        assert hg.number_of_nodes('TV') == 1
        assert hg.number_of_nodes('movie') == 1
        assert hg.number_of_edges(('user', 'watches', 'TV')) == 1
        assert hg.number_of_edges(('user', 'watches', 'movie')) == 1
        assert len(hg.etypes) == 2

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    # hetero_to_homo test case 2
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    hg = dgl.heterograph({
        ('_U', '_E', '_V'): ([0, 1], [0, 1])
    }, {'_U': 2, '_V': 3}, idtype=idtype, device=F.ctx())
    g = dgl.to_homogeneous(hg)
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    assert hg.idtype == g.idtype
    assert hg.device == g.device
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    assert g.number_of_nodes() == 5

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@parametrize_dtype
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def test_metagraph_reachable(idtype):
    g = create_test_heterograph(idtype)
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    x = F.randn((3, 5))
    g.nodes['user'].data['h'] = x

    new_g = dgl.metapath_reachable_graph(g, ['follows', 'plays'])
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    assert new_g.idtype == idtype
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    assert new_g.ntypes == ['game', 'user']
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    assert new_g.number_of_edges() == 3
    assert F.asnumpy(new_g.has_edges_between([0, 0, 1], [0, 1, 1])).all()

    new_g = dgl.metapath_reachable_graph(g, ['follows'])
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    assert new_g.idtype == idtype
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    assert new_g.ntypes == ['user']
    assert new_g.number_of_edges() == 2
    assert F.asnumpy(new_g.has_edges_between([0, 1], [1, 2])).all()

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@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="MXNet doesn't support bool tensor")
@parametrize_dtype
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def test_subgraph_mask(idtype):
    g = create_test_heterograph(idtype)
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    g_graph = g['follows']
    g_bipartite = g['plays']

    x = F.randn((3, 5))
    y = F.randn((2, 4))
    g.nodes['user'].data['h'] = x
    g.edges['follows'].data['h'] = y

    def _check_subgraph(g, sg):
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        assert sg.idtype == g.idtype
        assert sg.device == g.device
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        assert sg.ntypes == g.ntypes
        assert sg.etypes == g.etypes
        assert sg.canonical_etypes == g.canonical_etypes
        assert F.array_equal(F.tensor(sg.nodes['user'].data[dgl.NID]),
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        assert F.array_equal(F.tensor(sg.nodes['game'].data[dgl.NID]),
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                             F.tensor([0], idtype))
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        assert F.array_equal(F.tensor(sg.edges['follows'].data[dgl.EID]),
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                             F.tensor([1], idtype))
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        assert F.array_equal(F.tensor(sg.edges['plays'].data[dgl.EID]),
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                             F.tensor([1], idtype))
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        assert F.array_equal(F.tensor(sg.edges['wishes'].data[dgl.EID]),
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                             F.tensor([1], idtype))
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        assert sg.number_of_nodes('developer') == 0
        assert sg.number_of_edges('develops') == 0
        assert F.array_equal(sg.nodes['user'].data['h'], g.nodes['user'].data['h'][1:3])
        assert F.array_equal(sg.edges['follows'].data['h'], g.edges['follows'].data['h'][1:2])

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    sg1 = g.subgraph({'user': F.tensor([False, True, True], dtype=F.bool),
                      'game': F.tensor([True, False, False, False], dtype=F.bool)})
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    _check_subgraph(g, sg1)
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    if F._default_context_str != 'gpu':
        # TODO(minjie): enable this later
        sg2 = g.edge_subgraph({'follows': F.tensor([False, True], dtype=F.bool),
                               'plays': F.tensor([False, True, False, False], dtype=F.bool),
                               'wishes': F.tensor([False, True], dtype=F.bool)})
        _check_subgraph(g, sg2)
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@parametrize_dtype
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def test_subgraph(idtype):
    g = create_test_heterograph(idtype)
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    g_graph = g['follows']
    g_bipartite = g['plays']

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    x = F.randn((3, 5))
    y = F.randn((2, 4))
    g.nodes['user'].data['h'] = x
    g.edges['follows'].data['h'] = y

    def _check_subgraph(g, sg):
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        assert sg.idtype == g.idtype
        assert sg.device == g.device
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        assert sg.ntypes == g.ntypes
        assert sg.etypes == g.etypes
        assert sg.canonical_etypes == g.canonical_etypes
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        assert F.array_equal(F.tensor(sg.nodes['user'].data[dgl.NID]),
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                             F.tensor([1, 2], g.idtype))
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        assert F.array_equal(F.tensor(sg.nodes['game'].data[dgl.NID]),
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                             F.tensor([0], g.idtype))
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        assert F.array_equal(F.tensor(sg.edges['follows'].data[dgl.EID]),
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                             F.tensor([1], g.idtype))
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        assert F.array_equal(F.tensor(sg.edges['plays'].data[dgl.EID]),
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                             F.tensor([1], g.idtype))
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        assert F.array_equal(F.tensor(sg.edges['wishes'].data[dgl.EID]),
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                             F.tensor([1], g.idtype))
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        assert sg.number_of_nodes('developer') == 0
        assert sg.number_of_edges('develops') == 0
        assert F.array_equal(sg.nodes['user'].data['h'], g.nodes['user'].data['h'][1:3])
        assert F.array_equal(sg.edges['follows'].data['h'], g.edges['follows'].data['h'][1:2])

    sg1 = g.subgraph({'user': [1, 2], 'game': [0]})
    _check_subgraph(g, sg1)
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    if F._default_context_str != 'gpu':
        # TODO(minjie): enable this later
        sg2 = g.edge_subgraph({'follows': [1], 'plays': [1], 'wishes': [1]})
        _check_subgraph(g, sg2)
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    # backend tensor input
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    sg1 = g.subgraph({'user': F.tensor([1, 2], dtype=idtype),
                      'game': F.tensor([0], dtype=idtype)})
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    _check_subgraph(g, sg1)
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    if F._default_context_str != 'gpu':
        # TODO(minjie): enable this later
        sg2 = g.edge_subgraph({'follows': F.tensor([1], dtype=idtype),
                               'plays': F.tensor([1], dtype=idtype),
                               'wishes': F.tensor([1], dtype=idtype)})
        _check_subgraph(g, sg2)
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    # numpy input
    sg1 = g.subgraph({'user': np.array([1, 2]),
                      'game': np.array([0])})
    _check_subgraph(g, sg1)
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    if F._default_context_str != 'gpu':
        # TODO(minjie): enable this later
        sg2 = g.edge_subgraph({'follows': np.array([1]),
                               'plays': np.array([1]),
                               'wishes': np.array([1])})
        _check_subgraph(g, sg2)
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    def _check_subgraph_single_ntype(g, sg, preserve_nodes=False):
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        assert sg.idtype == g.idtype
        assert sg.device == g.device
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        assert sg.ntypes == g.ntypes
        assert sg.etypes == g.etypes
        assert sg.canonical_etypes == g.canonical_etypes
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        if not preserve_nodes:
            assert F.array_equal(F.tensor(sg.nodes['user'].data[dgl.NID]),
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                                 F.tensor([1, 2], g.idtype))
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        else:
            for ntype in sg.ntypes:
                assert g.number_of_nodes(ntype) == sg.number_of_nodes(ntype)

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        assert F.array_equal(F.tensor(sg.edges['follows'].data[dgl.EID]),
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                             F.tensor([1], g.idtype))
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        if not preserve_nodes:
            assert F.array_equal(sg.nodes['user'].data['h'], g.nodes['user'].data['h'][1:3])
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        assert F.array_equal(sg.edges['follows'].data['h'], g.edges['follows'].data['h'][1:2])

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    def _check_subgraph_single_etype(g, sg, preserve_nodes=False):
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        assert sg.ntypes == g.ntypes
        assert sg.etypes == g.etypes
        assert sg.canonical_etypes == g.canonical_etypes
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        if not preserve_nodes:
            assert F.array_equal(F.tensor(sg.nodes['user'].data[dgl.NID]),
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                                 F.tensor([0, 1], g.idtype))
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            assert F.array_equal(F.tensor(sg.nodes['game'].data[dgl.NID]),
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                                 F.tensor([0], g.idtype))
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        else:
            for ntype in sg.ntypes:
                assert g.number_of_nodes(ntype) == sg.number_of_nodes(ntype)

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        assert F.array_equal(F.tensor(sg.edges['plays'].data[dgl.EID]),
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                             F.tensor([0, 1], g.idtype))
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    sg1_graph = g_graph.subgraph([1, 2])
    _check_subgraph_single_ntype(g_graph, sg1_graph)
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    if F._default_context_str != 'gpu':
        # TODO(minjie): enable this later
        sg1_graph = g_graph.edge_subgraph([1])
        _check_subgraph_single_ntype(g_graph, sg1_graph)
        sg1_graph = g_graph.edge_subgraph([1], preserve_nodes=True)
        _check_subgraph_single_ntype(g_graph, sg1_graph, True)
        sg2_bipartite = g_bipartite.edge_subgraph([0, 1])
        _check_subgraph_single_etype(g_bipartite, sg2_bipartite)
        sg2_bipartite = g_bipartite.edge_subgraph([0, 1], preserve_nodes=True)
        _check_subgraph_single_etype(g_bipartite, sg2_bipartite, True)
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    def _check_typed_subgraph1(g, sg):
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        assert g.idtype == sg.idtype
        assert g.device == sg.device
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        assert set(sg.ntypes) == {'user', 'game'}
        assert set(sg.etypes) == {'follows', 'plays', 'wishes'}
        for ntype in sg.ntypes:
            assert sg.number_of_nodes(ntype) == g.number_of_nodes(ntype)
        for etype in sg.etypes:
            src_sg, dst_sg = sg.all_edges(etype=etype, order='eid')
            src_g, dst_g = g.all_edges(etype=etype, order='eid')
            assert F.array_equal(src_sg, src_g)
            assert F.array_equal(dst_sg, dst_g)
        assert F.array_equal(sg.nodes['user'].data['h'], g.nodes['user'].data['h'])
        assert F.array_equal(sg.edges['follows'].data['h'], g.edges['follows'].data['h'])
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        g.nodes['user'].data['h'] = F.scatter_row(g.nodes['user'].data['h'], F.tensor([2]), F.randn((1, 5)))
        g.edges['follows'].data['h'] = F.scatter_row(g.edges['follows'].data['h'], F.tensor([1]), F.randn((1, 4)))
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        assert F.array_equal(sg.nodes['user'].data['h'], g.nodes['user'].data['h'])
        assert F.array_equal(sg.edges['follows'].data['h'], g.edges['follows'].data['h'])

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    def _check_typed_subgraph2(g, sg):
        assert set(sg.ntypes) == {'developer', 'game'}
        assert set(sg.etypes) == {'develops'}
        for ntype in sg.ntypes:
            assert sg.number_of_nodes(ntype) == g.number_of_nodes(ntype)
        for etype in sg.etypes:
            src_sg, dst_sg = sg.all_edges(etype=etype, order='eid')
            src_g, dst_g = g.all_edges(etype=etype, order='eid')
            assert F.array_equal(src_sg, src_g)
            assert F.array_equal(dst_sg, dst_g)

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    sg3 = g.node_type_subgraph(['user', 'game'])
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    _check_typed_subgraph1(g, sg3)
    sg4 = g.edge_type_subgraph(['develops'])
    _check_typed_subgraph2(g, sg4)
    sg5 = g.edge_type_subgraph(['follows', 'plays', 'wishes'])
    _check_typed_subgraph1(g, sg5)
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@parametrize_dtype
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def test_apply(idtype):
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    def node_udf(nodes):
        return {'h': nodes.data['h'] * 2}
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    def node_udf2(nodes):
        return {'h': F.sum(nodes.data['h'], dim=1, keepdims=True)}
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    def edge_udf(edges):
        return {'h': edges.data['h'] * 2 + edges.src['h']}

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    g = create_test_heterograph(idtype)
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    g.nodes['user'].data['h'] = F.ones((3, 5))
    g.apply_nodes(node_udf, ntype='user')
    assert F.array_equal(g.nodes['user'].data['h'], F.ones((3, 5)) * 2)

    g['plays'].edata['h'] = F.ones((4, 5))
    g.apply_edges(edge_udf, etype=('user', 'plays', 'game'))
    assert F.array_equal(g['plays'].edata['h'], F.ones((4, 5)) * 4)

    # test apply on graph with only one type
    g['follows'].apply_nodes(node_udf)
    assert F.array_equal(g.nodes['user'].data['h'], F.ones((3, 5)) * 4)
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    g['plays'].apply_edges(edge_udf)
    assert F.array_equal(g['plays'].edata['h'], F.ones((4, 5)) * 12)

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    # Test the case that feature size changes
    g.nodes['user'].data['h'] = F.ones((3, 5))
    g.apply_nodes(node_udf2, ntype='user')
    assert F.array_equal(g.nodes['user'].data['h'], F.ones((3, 1)) * 5)

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    # test fail case
    # fail due to multiple types
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    with pytest.raises(DGLError):
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        g.apply_nodes(node_udf)

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    with pytest.raises(DGLError):
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        g.apply_edges(edge_udf)

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@parametrize_dtype
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def test_level2(idtype):
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    #edges = {
    #    'follows': ([0, 1], [1, 2]),
    #    'plays': ([0, 1, 2, 1], [0, 0, 1, 1]),
    #    'wishes': ([0, 2], [1, 0]),
    #    'develops': ([0, 1], [0, 1]),
    #}
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    g = create_test_heterograph(idtype)
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    def rfunc(nodes):
        return {'y': F.sum(nodes.mailbox['m'], 1)}
    def rfunc2(nodes):
        return {'y': F.max(nodes.mailbox['m'], 1)}
    def mfunc(edges):
        return {'m': edges.src['h']}
    def afunc(nodes):
        return {'y' : nodes.data['y'] + 1}

    #############################################################
    #  send_and_recv
    #############################################################

    g.nodes['user'].data['h'] = F.ones((3, 2))
    g.send_and_recv([2, 3], mfunc, rfunc, etype='plays')
    y = g.nodes['game'].data['y']
    assert F.array_equal(y, F.tensor([[0., 0.], [2., 2.]]))

    # only one type
    g['plays'].send_and_recv([2, 3], mfunc, rfunc)
    y = g.nodes['game'].data['y']
    assert F.array_equal(y, F.tensor([[0., 0.], [2., 2.]]))
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    # test fail case
    # fail due to multiple types
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    with pytest.raises(DGLError):
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        g.send_and_recv([2, 3], mfunc, rfunc)

    g.nodes['game'].data.clear()

    #############################################################
    #  pull
    #############################################################

    g.nodes['user'].data['h'] = F.ones((3, 2))
    g.pull(1, mfunc, rfunc, etype='plays')
    y = g.nodes['game'].data['y']
    assert F.array_equal(y, F.tensor([[0., 0.], [2., 2.]]))

    # only one type
    g['plays'].pull(1, mfunc, rfunc)
    y = g.nodes['game'].data['y']
    assert F.array_equal(y, F.tensor([[0., 0.], [2., 2.]]))

    # test fail case
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    with pytest.raises(DGLError):
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        g.pull(1, mfunc, rfunc)

    g.nodes['game'].data.clear()

    #############################################################
    #  update_all
    #############################################################

    g.nodes['user'].data['h'] = F.ones((3, 2))
    g.update_all(mfunc, rfunc, etype='plays')
    y = g.nodes['game'].data['y']
    assert F.array_equal(y, F.tensor([[2., 2.], [2., 2.]]))

    # only one type
    g['plays'].update_all(mfunc, rfunc)
    y = g.nodes['game'].data['y']
    assert F.array_equal(y, F.tensor([[2., 2.], [2., 2.]]))

    # test fail case
    # fail due to multiple types
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    with pytest.raises(DGLError):
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        g.update_all(mfunc, rfunc)

    # test multi
    g.multi_update_all(
        {'plays' : (mfunc, rfunc),
         ('user', 'wishes', 'game'): (mfunc, rfunc2)},
        'sum')
    assert F.array_equal(g.nodes['game'].data['y'], F.tensor([[3., 3.], [3., 3.]]))

    # test multi
    g.multi_update_all(
        {'plays' : (mfunc, rfunc, afunc),
         ('user', 'wishes', 'game'): (mfunc, rfunc2)},
        'sum', afunc)
    assert F.array_equal(g.nodes['game'].data['y'], F.tensor([[5., 5.], [5., 5.]]))

    # test cross reducer
    g.nodes['user'].data['h'] = F.randn((3, 2))
    for cred in ['sum', 'max', 'min', 'mean', 'stack']:
        g.multi_update_all(
            {'plays' : (mfunc, rfunc, afunc),
             'wishes': (mfunc, rfunc2)},
            cred, afunc)
        y = g.nodes['game'].data['y']
        g['plays'].update_all(mfunc, rfunc, afunc)
        y1 = g.nodes['game'].data['y']
        g['wishes'].update_all(mfunc, rfunc2)
        y2 = g.nodes['game'].data['y']
        if cred == 'stack':
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            # stack has an internal order by edge type id
            yy = F.stack([y1, y2], 1)
            yy = yy + 1  # final afunc
            assert F.array_equal(y, yy)
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        else:
            yy = get_redfn(cred)(F.stack([y1, y2], 0), 0)
            yy = yy + 1  # final afunc
            assert F.array_equal(y, yy)

    # test fail case
    # fail because cannot infer ntype
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    with pytest.raises(DGLError):
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        g.update_all(
            {'plays' : (mfunc, rfunc),
             'follows': (mfunc, rfunc2)},
            'sum')

    g.nodes['game'].data.clear()
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@parametrize_dtype
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def test_updates(idtype):
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    def msg_func(edges):
        return {'m': edges.src['h']}
    def reduce_func(nodes):
        return {'y': F.sum(nodes.mailbox['m'], 1)}
    def apply_func(nodes):
        return {'y': nodes.data['y'] * 2}
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    g = create_test_heterograph(idtype)
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    x = F.randn((3, 5))
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    g.nodes['user'].data['h'] = x
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    for msg, red, apply in itertools.product(
            [fn.copy_u('h', 'm'), msg_func], [fn.sum('m', 'y'), reduce_func],
            [None, apply_func]):
        multiplier = 1 if apply is None else 2

        g['user', 'plays', 'game'].update_all(msg, red, apply)
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        y = g.nodes['game'].data['y']
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        assert F.array_equal(y[0], (x[0] + x[1]) * multiplier)
        assert F.array_equal(y[1], (x[1] + x[2]) * multiplier)
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        del g.nodes['game'].data['y']
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        g['user', 'plays', 'game'].send_and_recv(([0, 1, 2], [0, 1, 1]), msg, red, apply)
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        y = g.nodes['game'].data['y']
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        assert F.array_equal(y[0], x[0] * multiplier)
        assert F.array_equal(y[1], (x[1] + x[2]) * multiplier)
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        del g.nodes['game'].data['y']
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        # pulls from destination (game) node 0
        g['user', 'plays', 'game'].pull(0, msg, red, apply)
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        y = g.nodes['game'].data['y']
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        assert F.array_equal(y[0], (x[0] + x[1]) * multiplier)
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        del g.nodes['game'].data['y']
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        # pushes from source (user) node 0
        g['user', 'plays', 'game'].push(0, msg, red, apply)
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        y = g.nodes['game'].data['y']
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        assert F.array_equal(y[0], x[0] * multiplier)
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        del g.nodes['game'].data['y']

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@parametrize_dtype
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def test_backward(idtype):
    g = create_test_heterograph(idtype)
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    x = F.randn((3, 5))
    F.attach_grad(x)
    g.nodes['user'].data['h'] = x
    with F.record_grad():
        g.multi_update_all(
            {'plays' : (fn.copy_u('h', 'm'), fn.sum('m', 'y')),
             'wishes': (fn.copy_u('h', 'm'), fn.sum('m', 'y'))},
            'sum')
        y = g.nodes['game'].data['y']
        F.backward(y, F.ones(y.shape))
    print(F.grad(x))
    assert F.array_equal(F.grad(x), F.tensor([[2., 2., 2., 2., 2.],
                                              [2., 2., 2., 2., 2.],
                                              [2., 2., 2., 2., 2.]]))
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@parametrize_dtype
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def test_empty_heterograph(idtype):
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    def assert_empty(g):
        assert g.number_of_nodes('user') == 0
        assert g.number_of_edges('plays') == 0
        assert g.number_of_nodes('game') == 0

    # empty src-dst pair
    assert_empty(dgl.heterograph({('user', 'plays', 'game'): ([], [])}))

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    g = dgl.heterograph({('user', 'follows', 'user'): ([], [])}, idtype=idtype, device=F.ctx())
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    assert g.idtype == idtype
    assert g.device == F.ctx()
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    assert g.number_of_nodes('user') == 0
    assert g.number_of_edges('follows') == 0

    # empty relation graph with others
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    g = dgl.heterograph({('user', 'plays', 'game'): ([], []), ('developer', 'develops', 'game'):
        ([0, 1], [0, 1])}, idtype=idtype, device=F.ctx())
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    assert g.idtype == idtype
    assert g.device == F.ctx()
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    assert g.number_of_nodes('user') == 0
    assert g.number_of_edges('plays') == 0
    assert g.number_of_nodes('game') == 2
    assert g.number_of_edges('develops') == 2
    assert g.number_of_nodes('developer') == 2

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@parametrize_dtype
def test_types_in_function(idtype):
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    def mfunc1(edges):
        assert edges.canonical_etype == ('user', 'follow', 'user')
        return {}

    def rfunc1(nodes):
        assert nodes.ntype == 'user'
        return {}

    def filter_nodes1(nodes):
        assert nodes.ntype == 'user'
        return F.zeros((3,))

    def filter_edges1(edges):
        assert edges.canonical_etype == ('user', 'follow', 'user')
        return F.zeros((2,))

    def mfunc2(edges):
        assert edges.canonical_etype == ('user', 'plays', 'game')
        return {}

    def rfunc2(nodes):
        assert nodes.ntype == 'game'
        return {}

    def filter_nodes2(nodes):
        assert nodes.ntype == 'game'
        return F.zeros((3,))

    def filter_edges2(edges):
        assert edges.canonical_etype == ('user', 'plays', 'game')
        return F.zeros((2,))

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    g = dgl.heterograph({('user', 'follow', 'user'): ((0, 1), (1, 2))},
                        idtype=idtype, device=F.ctx())
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    g.apply_nodes(rfunc1)
    g.apply_edges(mfunc1)
    g.update_all(mfunc1, rfunc1)
    g.send_and_recv([0, 1], mfunc1, rfunc1)
    g.push([0], mfunc1, rfunc1)
    g.pull([1], mfunc1, rfunc1)
    g.filter_nodes(filter_nodes1)
    g.filter_edges(filter_edges1)

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    g.apply_nodes(rfunc2, ntype='game')
    g.apply_edges(mfunc2)
    g.update_all(mfunc2, rfunc2)
    g.send_and_recv([0, 1], mfunc2, rfunc2)
    g.push([0], mfunc2, rfunc2)
    g.pull([1], mfunc2, rfunc2)
    g.filter_nodes(filter_nodes2, ntype='game')
    g.filter_edges(filter_edges2)

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def test_stack_reduce(idtype):
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    #edges = {
    #    'follows': ([0, 1], [1, 2]),
    #    'plays': ([0, 1, 2, 1], [0, 0, 1, 1]),
    #    'wishes': ([0, 2], [1, 0]),
    #    'develops': ([0, 1], [0, 1]),
    #}
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    g.nodes['user'].data['h'] = F.randn((3, 200))
    def rfunc(nodes):
        return {'y': F.sum(nodes.mailbox['m'], 1)}
    def rfunc2(nodes):
        return {'y': F.max(nodes.mailbox['m'], 1)}
    def mfunc(edges):
        return {'m': edges.src['h']}
    g.multi_update_all(
            {'plays' : (mfunc, rfunc),
             'wishes': (mfunc, rfunc2)},
            'stack')
    assert g.nodes['game'].data['y'].shape == (g.number_of_nodes('game'), 2, 200)
    # only one type-wise update_all, stack still adds one dimension
    g.multi_update_all(
            {'plays' : (mfunc, rfunc)},
            'stack')
    assert g.nodes['game'].data['y'].shape == (g.number_of_nodes('game'), 1, 200)

1626
@parametrize_dtype
1627
def test_isolated_ntype(idtype):
1628
    g = dgl.heterograph({
1629
        ('A', 'AB', 'B'): ([0, 1, 2], [1, 2, 3])},
1630
1631
        num_nodes_dict={'A': 3, 'B': 4, 'C': 4},
        idtype=idtype, device=F.ctx())
1632
1633
1634
1635
1636
    assert g.number_of_nodes('A') == 3
    assert g.number_of_nodes('B') == 4
    assert g.number_of_nodes('C') == 4

    g = dgl.heterograph({
1637
        ('A', 'AC', 'C'): ([0, 1, 2], [1, 2, 3])},
1638
1639
        num_nodes_dict={'A': 3, 'B': 4, 'C': 4},
        idtype=idtype, device=F.ctx())
1640
1641
1642
1643
    assert g.number_of_nodes('A') == 3
    assert g.number_of_nodes('B') == 4
    assert g.number_of_nodes('C') == 4

1644
    G = dgl.graph(([0, 1, 2], [4, 5, 6]), num_nodes=11, idtype=idtype, device=F.ctx())
1645
1646
    G.ndata[dgl.NTYPE] = F.tensor([0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=F.int64)
    G.edata[dgl.ETYPE] = F.tensor([0, 0, 0], dtype=F.int64)
1647
    g = dgl.to_heterogeneous(G, ['A', 'B', 'C'], ['AB'])
1648
1649
1650
1651
    assert g.number_of_nodes('A') == 3
    assert g.number_of_nodes('B') == 4
    assert g.number_of_nodes('C') == 4

1652
1653

@parametrize_dtype
1654
def test_ismultigraph(idtype):
1655
1656
    g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5])},
                         {'A': 6, 'B': 6}, idtype=idtype, device=F.ctx())
1657
    assert g1.is_multigraph == False
1658
1659
    g2 = dgl.heterograph({('A', 'AC', 'C'): ([0, 0, 0, 1], [1, 1, 2, 5])},
                         {'A': 6, 'C': 6}, idtype=idtype, device=F.ctx())
1660
    assert g2.is_multigraph == True
1661
    g3 = dgl.graph(((0, 1), (1, 2)), num_nodes=6, idtype=idtype, device=F.ctx())
1662
    assert g3.is_multigraph == False
1663
    g4 = dgl.graph(([0, 0, 1], [1, 1, 2]), num_nodes=6, idtype=idtype, device=F.ctx())
1664
    assert g4.is_multigraph == True
1665
1666
1667
1668
    g = dgl.heterograph({
        ('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5]),
        ('A', 'AA', 'A'): ([0, 1], [1, 2])},
        {'A': 6, 'B': 6}, idtype=idtype, device=F.ctx())
1669
    assert g.is_multigraph == False
1670
1671
1672
1673
    g = dgl.heterograph({
        ('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5]),
        ('A', 'AC', 'C'): ([0, 0, 0, 1], [1, 1, 2, 5])},
        {'A': 6, 'B': 6, 'C': 6}, idtype=idtype, device=F.ctx())
1674
    assert g.is_multigraph == True
1675
1676
1677
1678
    g = dgl.heterograph({
        ('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5]),
        ('A', 'AA', 'A'): ([0, 0, 1], [1, 1, 2])},
        {'A': 6, 'B': 6}, idtype=idtype, device=F.ctx())
1679
    assert g.is_multigraph == True
1680
1681
1682
1683
    g = dgl.heterograph({
        ('A', 'AC', 'C'): ([0, 0, 0, 1], [1, 1, 2, 5]),
        ('A', 'AA', 'A'): ([0, 1], [1, 2])},
        {'A': 6, 'C': 6}, idtype=idtype, device=F.ctx())
1684
1685
    assert g.is_multigraph == True

1686
@parametrize_dtype
1687
def test_bipartite(idtype):
1688
1689
    g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1], [1, 2, 5])},
                         idtype=idtype, device=F.ctx())
1690
1691
1692
1693
1694
1695
1696
    assert g1.is_unibipartite
    assert len(g1.ntypes) == 2
    assert g1.etypes == ['AB']
    assert g1.srctypes == ['A']
    assert g1.dsttypes == ['B']
    assert g1.number_of_nodes('A') == 2
    assert g1.number_of_nodes('B') == 6
1697
1698
1699
1700
    assert g1.number_of_src_nodes('A') == 2
    assert g1.number_of_src_nodes() == 2
    assert g1.number_of_dst_nodes('B') == 6
    assert g1.number_of_dst_nodes() == 6
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
    assert g1.number_of_edges() == 3
    g1.srcdata['h'] = F.randn((2, 5))
    assert F.array_equal(g1.srcnodes['A'].data['h'], g1.srcdata['h'])
    assert F.array_equal(g1.nodes['A'].data['h'], g1.srcdata['h'])
    assert F.array_equal(g1.nodes['SRC/A'].data['h'], g1.srcdata['h'])
    g1.dstdata['h'] = F.randn((6, 3))
    assert F.array_equal(g1.dstnodes['B'].data['h'], g1.dstdata['h'])
    assert F.array_equal(g1.nodes['B'].data['h'], g1.dstdata['h'])
    assert F.array_equal(g1.nodes['DST/B'].data['h'], g1.dstdata['h'])

    # more complicated bipartite
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
    g2 = dgl.heterograph({
        ('A', 'AB', 'B'): ([0, 0, 1], [1, 2, 5]),
        ('A', 'AC', 'C'): ([1, 0], [0, 0])
    }, idtype=idtype, device=F.ctx())

    assert g2.is_unibipartite
    assert g2.srctypes == ['A']
    assert set(g2.dsttypes) == {'B', 'C'}
    assert g2.number_of_nodes('A') == 2
    assert g2.number_of_nodes('B') == 6
    assert g2.number_of_nodes('C') == 1
    assert g2.number_of_src_nodes('A') == 2
    assert g2.number_of_src_nodes() == 2
    assert g2.number_of_dst_nodes('B') == 6
    assert g2.number_of_dst_nodes('C') == 1
    g2.srcdata['h'] = F.randn((2, 5))
    assert F.array_equal(g2.srcnodes['A'].data['h'], g2.srcdata['h'])
    assert F.array_equal(g2.nodes['A'].data['h'], g2.srcdata['h'])
    assert F.array_equal(g2.nodes['SRC/A'].data['h'], g2.srcdata['h'])

    g3 = dgl.heterograph({
        ('A', 'AB', 'B'): ([0, 0, 1], [1, 2, 5]),
        ('A', 'AC', 'C'): ([1, 0], [0, 0]),
        ('A', 'AA', 'A'): ([0, 1], [0, 1])
    }, idtype=idtype, device=F.ctx())
    assert not g3.is_unibipartite
1738

1739
@parametrize_dtype
1740
def test_dtype_cast(idtype):
1741
    g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
1742
    assert g.idtype == idtype
1743
1744
    g.ndata["feat"] = F.tensor([3, 4, 5])
    g.edata["h"] = F.tensor([3, 4, 5, 6])
1745
    if idtype == "int32":
1746
        g_cast = g.long()
1747
        assert g_cast.idtype == F.int64
1748
1749
    else:
        g_cast = g.int()
1750
1751
        assert g_cast.idtype == F.int32
    test_utils.check_graph_equal(g, g_cast, check_idtype=False)
1752

1753
1754
@parametrize_dtype
def test_format(idtype):
1755
    # single relation
1756
    g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
1757
1758
1759
    assert g.formats()['created'] == ['coo']
    g1 = g.formats(['coo', 'csr', 'csc'])
    assert len(g1.formats()['created']) + len(g1.formats()['not created']) == 3
1760
    g1.create_formats_()
1761
1762
    assert len(g1.formats()['created']) == 3
    assert g.formats()['created'] == ['coo']
1763
1764
1765

    # multiple relation
    g = dgl.heterograph({
1766
1767
1768
1769
        ('user', 'follows', 'user'): ([0, 1], [1, 2]),
        ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1]),
        ('developer', 'develops', 'game'): ([0, 1], [0, 1])
        }, idtype=idtype, device=F.ctx())
1770
1771
    user_feat = F.randn((g['follows'].number_of_src_nodes(), 5))
    g['follows'].srcdata['h'] = user_feat
1772
    g1 = g.formats('csc')
1773
1774
1775
    # test frame
    assert F.array_equal(g1['follows'].srcdata['h'], user_feat)
    # test each relation graph
1776
1777
    assert g1.formats()['created'] == ['csc']
    assert len(g1.formats()['not created']) == 0
1778

1779
1780
@parametrize_dtype
def test_edges_order(idtype):
1781
1782
1783
1784
    # (0, 2), (1, 2), (0, 1), (0, 1), (2, 1)
    g = dgl.graph((
        np.array([0, 1, 0, 0, 2]),
        np.array([2, 2, 1, 1, 1])
1785
    ), idtype=idtype, device=F.ctx())
1786

1787
    print(g.formats())
1788
    src, dst = g.all_edges(order='srcdst')
1789
1790
    assert F.array_equal(src, F.tensor([0, 0, 0, 1, 2], dtype=idtype))
    assert F.array_equal(dst, F.tensor([1, 1, 2, 2, 1], dtype=idtype))
1791

1792
@parametrize_dtype
1793
def test_reverse(idtype):
1794
1795
    g = dgl.heterograph({
        ('user', 'follows', 'user'): ([0, 1, 2, 4, 3 ,1, 3], [1, 2, 3, 2, 0, 0, 1]),
1796
    }, idtype=idtype, device=F.ctx())
1797
    gidx = g._graph
1798
    r_gidx = gidx.reverse()
1799
1800
1801
1802
1803

    assert gidx.number_of_nodes(0) == r_gidx.number_of_nodes(0)
    assert gidx.number_of_edges(0) == r_gidx.number_of_edges(0)
    g_s, g_d, _ = gidx.edges(0)
    rg_s, rg_d, _ = r_gidx.edges(0)
1804
1805
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1806
1807

    # force to start with 'csr'
1808
1809
    gidx = gidx.formats('csr')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1810
    r_gidx = gidx.reverse()
1811
1812
    assert 'csr' in gidx.formats()['created']
    assert 'csc' in r_gidx.formats()['created']
1813
1814
1815
1816
    assert gidx.number_of_nodes(0) == r_gidx.number_of_nodes(0)
    assert gidx.number_of_edges(0) == r_gidx.number_of_edges(0)
    g_s, g_d, _ = gidx.edges(0)
    rg_s, rg_d, _ = r_gidx.edges(0)
1817
1818
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1819
1820

    # force to start with 'csc'
1821
1822
    gidx = gidx.formats('csc')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1823
    r_gidx = gidx.reverse()
1824
1825
    assert 'csc' in gidx.formats()['created']
    assert 'csr' in r_gidx.formats()['created']
1826
1827
1828
1829
    assert gidx.number_of_nodes(0) == r_gidx.number_of_nodes(0)
    assert gidx.number_of_edges(0) == r_gidx.number_of_edges(0)
    g_s, g_d, _ = gidx.edges(0)
    rg_s, rg_d, _ = r_gidx.edges(0)
1830
1831
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1832
1833
1834
1835
1836

    g = dgl.heterograph({
        ('user', 'follows', 'user'): ([0, 1, 2, 4, 3 ,1, 3], [1, 2, 3, 2, 0, 0, 1]),
        ('user', 'plays', 'game'): ([0, 0, 2, 3, 3, 4, 1], [1, 0, 1, 0, 1, 0, 0]),
        ('developer', 'develops', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1]),
1837
        }, idtype=idtype, device=F.ctx())
1838
    gidx = g._graph
1839
1840
1841
1842
1843
1844
1845
1846
    r_gidx = gidx.reverse()

    # metagraph
    mg = gidx.metagraph
    r_mg = r_gidx.metagraph
    for etype in range(3):
        assert mg.find_edge(etype) == r_mg.find_edge(etype)[::-1]

1847
1848
1849
1850
1851
1852
1853
1854
1855
    # three node types and three edge types
    assert gidx.number_of_nodes(0) == r_gidx.number_of_nodes(0)
    assert gidx.number_of_nodes(1) == r_gidx.number_of_nodes(1)
    assert gidx.number_of_nodes(2) == r_gidx.number_of_nodes(2)
    assert gidx.number_of_edges(0) == r_gidx.number_of_edges(0)
    assert gidx.number_of_edges(1) == r_gidx.number_of_edges(1)
    assert gidx.number_of_edges(2) == r_gidx.number_of_edges(2)
    g_s, g_d, _ = gidx.edges(0)
    rg_s, rg_d, _ = r_gidx.edges(0)
1856
1857
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1858
1859
    g_s, g_d, _ = gidx.edges(1)
    rg_s, rg_d, _ = r_gidx.edges(1)
1860
1861
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1862
1863
    g_s, g_d, _ = gidx.edges(2)
    rg_s, rg_d, _ = r_gidx.edges(2)
1864
1865
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1866
1867

    # force to start with 'csr'
1868
1869
    gidx = gidx.formats('csr')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1870
    r_gidx = gidx.reverse()
1871
    # three node types and three edge types
1872
1873
    assert 'csr' in gidx.formats()['created']
    assert 'csc' in r_gidx.formats()['created']
1874
1875
1876
1877
1878
1879
1880
1881
    assert gidx.number_of_nodes(0) == r_gidx.number_of_nodes(0)
    assert gidx.number_of_nodes(1) == r_gidx.number_of_nodes(1)
    assert gidx.number_of_nodes(2) == r_gidx.number_of_nodes(2)
    assert gidx.number_of_edges(0) == r_gidx.number_of_edges(0)
    assert gidx.number_of_edges(1) == r_gidx.number_of_edges(1)
    assert gidx.number_of_edges(2) == r_gidx.number_of_edges(2)
    g_s, g_d, _ = gidx.edges(0)
    rg_s, rg_d, _ = r_gidx.edges(0)
1882
1883
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1884
1885
    g_s, g_d, _ = gidx.edges(1)
    rg_s, rg_d, _ = r_gidx.edges(1)
1886
1887
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1888
1889
    g_s, g_d, _ = gidx.edges(2)
    rg_s, rg_d, _ = r_gidx.edges(2)
1890
1891
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1892
1893

    # force to start with 'csc'
1894
1895
    gidx = gidx.formats('csc')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1896
    r_gidx = gidx.reverse()
1897
    # three node types and three edge types
1898
1899
    assert 'csc' in gidx.formats()['created']
    assert 'csr' in r_gidx.formats()['created']
1900
1901
1902
1903
1904
1905
1906
1907
    assert gidx.number_of_nodes(0) == r_gidx.number_of_nodes(0)
    assert gidx.number_of_nodes(1) == r_gidx.number_of_nodes(1)
    assert gidx.number_of_nodes(2) == r_gidx.number_of_nodes(2)
    assert gidx.number_of_edges(0) == r_gidx.number_of_edges(0)
    assert gidx.number_of_edges(1) == r_gidx.number_of_edges(1)
    assert gidx.number_of_edges(2) == r_gidx.number_of_edges(2)
    g_s, g_d, _ = gidx.edges(0)
    rg_s, rg_d, _ = r_gidx.edges(0)
1908
1909
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1910
1911
    g_s, g_d, _ = gidx.edges(1)
    rg_s, rg_d, _ = r_gidx.edges(1)
1912
1913
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1914
1915
    g_s, g_d, _ = gidx.edges(2)
    rg_s, rg_d, _ = r_gidx.edges(2)
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)

@parametrize_dtype
def test_clone(idtype):
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    g.ndata['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
    g.edata['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx())

    new_g = g.clone()
    assert g.number_of_nodes() == new_g.number_of_nodes()
    assert g.number_of_edges() == new_g.number_of_edges()
    assert g.device == new_g.device
    assert g.idtype == new_g.idtype
    assert F.array_equal(g.ndata['h'], new_g.ndata['h'])
    assert F.array_equal(g.edata['h'], new_g.edata['h'])
    # data change
    new_g.ndata['h'] = F.copy_to(F.tensor([2, 2, 2], dtype=idtype), ctx=F.ctx())
    assert (F.array_equal(g.ndata['h'], new_g.ndata['h']) == False)
    g.edata['h'] = F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())
    assert (F.array_equal(g.edata['h'], new_g.edata['h']) == False)
    # graph structure change
    g.add_nodes(1)
    assert g.number_of_nodes() != new_g.number_of_nodes()
    new_g.add_edges(1, 1)
    assert g.number_of_edges() != new_g.number_of_edges()

    # zero data graph
1944
    g = dgl.graph(([], []), num_nodes=0, idtype=idtype, device=F.ctx())
1945
1946
1947
1948
1949
    new_g = g.clone()
    assert g.number_of_nodes() == new_g.number_of_nodes()
    assert g.number_of_edges() == new_g.number_of_edges()

    # heterograph
1950
    g = create_test_heterograph3(idtype)
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
    g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx())
    new_g = g.clone()
    assert g.number_of_nodes('user') == new_g.number_of_nodes('user')
    assert g.number_of_nodes('game') == new_g.number_of_nodes('game')
    assert g.number_of_nodes('developer') == new_g.number_of_nodes('developer')
    assert g.number_of_edges('plays') == new_g.number_of_edges('plays')
    assert g.number_of_edges('develops') == new_g.number_of_edges('develops')
    assert F.array_equal(g.nodes['user'].data['h'], new_g.nodes['user'].data['h'])
    assert F.array_equal(g.nodes['game'].data['h'], new_g.nodes['game'].data['h'])
    assert F.array_equal(g.edges['plays'].data['h'], new_g.edges['plays'].data['h'])
    assert g.device == new_g.device
    assert g.idtype == new_g.idtype
    u, v = g.edges(form='uv', order='eid', etype='plays')
    nu, nv = new_g.edges(form='uv', order='eid', etype='plays')
    assert F.array_equal(u, nu)
    assert F.array_equal(v, nv)
    # graph structure change
    u = F.tensor([0, 4], dtype=idtype)
    v = F.tensor([2, 6], dtype=idtype)
    g.add_edges(u, v, etype='plays')
    u, v = g.edges(form='uv', order='eid', etype='plays')
    assert u.shape[0] != nu.shape[0]
    assert v.shape[0] != nv.shape[0]
    assert g.nodes['user'].data['h'].shape[0] != new_g.nodes['user'].data['h'].shape[0]
    assert g.nodes['game'].data['h'].shape[0] != new_g.nodes['game'].data['h'].shape[0]
    assert g.edges['plays'].data['h'].shape[0] != new_g.edges['plays'].data['h'].shape[0]


@parametrize_dtype
def test_add_edges(idtype):
    # homogeneous graph
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    u = 0
    v = 1
    g.add_edges(u, v)
    assert g.device == F.ctx()
    assert g.number_of_nodes() == 3
    assert g.number_of_edges() == 3
    u = [0]
    v = [1]
    g.add_edges(u, v)
    assert g.device == F.ctx()
    assert g.number_of_nodes() == 3
    assert g.number_of_edges() == 4
    u = F.tensor(u, dtype=idtype)
    v = F.tensor(v, dtype=idtype)
    g.add_edges(u, v)
    assert g.device == F.ctx()
    assert g.number_of_nodes() == 3
    assert g.number_of_edges() == 5
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype))
    assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype))

    # node id larger than current max node id
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    u = F.tensor([0, 1], dtype=idtype)
    v = F.tensor([2, 3], dtype=idtype)
    g.add_edges(u, v)
    assert g.number_of_nodes() == 4
    assert g.number_of_edges() == 4
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0, 1, 0, 1], dtype=idtype))
    assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))

    # has data
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    g.ndata['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
    g.edata['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx())
    u = F.tensor([0, 1], dtype=idtype)
    v = F.tensor([2, 3], dtype=idtype)
    e_feat = {'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
              'hh' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())}
    g.add_edges(u, v, e_feat)
    assert g.number_of_nodes() == 4
    assert g.number_of_edges() == 4
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0, 1, 0, 1], dtype=idtype))
    assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))
    assert F.array_equal(g.ndata['h'], F.tensor([1, 1, 1, 0], dtype=idtype))
    assert F.array_equal(g.edata['h'], F.tensor([1, 1, 2, 2], dtype=idtype))
    assert F.array_equal(g.edata['hh'], F.tensor([0, 0, 2, 2], dtype=idtype))

    # zero data graph
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    g = dgl.graph(([], []), num_nodes=0, idtype=idtype, device=F.ctx())
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    u = F.tensor([0, 1], dtype=idtype)
    v = F.tensor([2, 2], dtype=idtype)
    e_feat = {'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
              'hh' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())}
    g.add_edges(u, v, e_feat)
    assert g.number_of_nodes() == 3
    assert g.number_of_edges() == 2
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0, 1], dtype=idtype))
    assert F.array_equal(v, F.tensor([2, 2], dtype=idtype))
    assert F.array_equal(g.edata['h'], F.tensor([2, 2], dtype=idtype))
    assert F.array_equal(g.edata['hh'], F.tensor([2, 2], dtype=idtype))

    # bipartite graph
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    g = dgl.heterograph({('user', 'plays', 'game'): ([0, 1], [1, 2])},
                        idtype=idtype, device=F.ctx())
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    u = 0
    v = 1
    g.add_edges(u, v)
    assert g.device == F.ctx()
    assert g.number_of_nodes('user') == 2
    assert g.number_of_nodes('game') == 3
    assert g.number_of_edges() == 3
    u = [0]
    v = [1]
    g.add_edges(u, v)
    assert g.device == F.ctx()
    assert g.number_of_nodes('user') == 2
    assert g.number_of_nodes('game') == 3
    assert g.number_of_edges() == 4
    u = F.tensor(u, dtype=idtype)
    v = F.tensor(v, dtype=idtype)
    g.add_edges(u, v)
    assert g.device == F.ctx()
    assert g.number_of_nodes('user') == 2
    assert g.number_of_nodes('game') == 3
    assert g.number_of_edges() == 5
    u, v = g.edges(form='uv')
    assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype))
    assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype))

    # node id larger than current max node id
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    g = dgl.heterograph({('user', 'plays', 'game'): ([0, 1], [1, 2])},
                        idtype=idtype, device=F.ctx())
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    u = F.tensor([0, 2], dtype=idtype)
    v = F.tensor([2, 3], dtype=idtype)
    g.add_edges(u, v)
    assert g.device == F.ctx()
    assert g.number_of_nodes('user') == 3
    assert g.number_of_nodes('game') == 4
    assert g.number_of_edges() == 4
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype))
    assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))

    # has data
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    g = dgl.heterograph({
        ('user', 'plays', 'game'): ([0, 1], [1, 2])
    }, idtype=idtype, device=F.ctx())
    g.nodes['user'].data['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx())
    g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2, 2], dtype=idtype), ctx=F.ctx())
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    g.edata['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx())
    u = F.tensor([0, 2], dtype=idtype)
    v = F.tensor([2, 3], dtype=idtype)
    e_feat = {'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
              'hh' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())}
    g.add_edges(u, v, e_feat)
    assert g.number_of_nodes('user') == 3
    assert g.number_of_nodes('game') == 4
    assert g.number_of_edges() == 4
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype))
    assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))
    assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 0], dtype=idtype))
    assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2, 0], dtype=idtype))
    assert F.array_equal(g.edata['h'], F.tensor([1, 1, 2, 2], dtype=idtype))
    assert F.array_equal(g.edata['hh'], F.tensor([0, 0, 2, 2], dtype=idtype))

    # heterogeneous graph
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    g = create_test_heterograph3(idtype)
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    u = F.tensor([0, 2], dtype=idtype)
    v = F.tensor([2, 3], dtype=idtype)
    g.add_edges(u, v, etype='plays')
    assert g.number_of_nodes('user') == 3
    assert g.number_of_nodes('game') == 4
    assert g.number_of_nodes('developer') == 2
    assert g.number_of_edges('plays') == 6
    assert g.number_of_edges('develops') == 2
    u, v = g.edges(form='uv', order='eid', etype='plays')
    assert F.array_equal(u, F.tensor([0, 1, 1, 2, 0, 2], dtype=idtype))
    assert F.array_equal(v, F.tensor([0, 0, 1, 1, 2, 3], dtype=idtype))
    assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1], dtype=idtype))
    assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 0, 0], dtype=idtype))
    assert F.array_equal(g.edges['plays'].data['h'], F.tensor([1, 1, 1, 1, 0, 0], dtype=idtype))

    # add with feature
    e_feat = {'h': F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())}
    u = F.tensor([0, 2], dtype=idtype)
    v = F.tensor([2, 3], dtype=idtype)
    g.nodes['game'].data['h'] =  F.copy_to(F.tensor([2, 2, 1, 1], dtype=idtype), ctx=F.ctx())
    g.add_edges(u, v, data=e_feat, etype='develops')
    assert g.number_of_nodes('user') == 3
    assert g.number_of_nodes('game') == 4
    assert g.number_of_nodes('developer') == 3
    assert g.number_of_edges('plays') == 6
    assert g.number_of_edges('develops') == 4
    u, v = g.edges(form='uv', order='eid', etype='develops')
    assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype))
    assert F.array_equal(v, F.tensor([0, 1, 2, 3], dtype=idtype))
    assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3, 0], dtype=idtype))
    assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 1, 1], dtype=idtype))
    assert F.array_equal(g.edges['develops'].data['h'], F.tensor([0, 0, 2, 2], dtype=idtype))

@parametrize_dtype
def test_add_nodes(idtype):
    # homogeneous Graphs
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    g.ndata['h'] = F.copy_to(F.tensor([1,1,1], dtype=idtype), ctx=F.ctx())
    g.add_nodes(1)
    assert g.number_of_nodes() == 4
    assert F.array_equal(g.ndata['h'], F.tensor([1, 1, 1, 0], dtype=idtype))

    # zero node graph
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    g = dgl.graph(([], []), num_nodes=3, idtype=idtype, device=F.ctx())
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    g.ndata['h'] = F.copy_to(F.tensor([1,1,1], dtype=idtype), ctx=F.ctx())
    g.add_nodes(1, data={'h' : F.copy_to(F.tensor([2],  dtype=idtype), ctx=F.ctx())})
    assert g.number_of_nodes() == 4
    assert F.array_equal(g.ndata['h'], F.tensor([1, 1, 1, 2], dtype=idtype))

    # bipartite graph
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    g = dgl.heterograph({('user', 'plays', 'game'): ([0, 1], [1, 2])},
                        idtype=idtype, device=F.ctx())
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    g.add_nodes(2, data={'h' : F.copy_to(F.tensor([2, 2],  dtype=idtype), ctx=F.ctx())}, ntype='user')
    assert g.number_of_nodes('user') == 4
    assert F.array_equal(g.nodes['user'].data['h'], F.tensor([0, 0, 2, 2], dtype=idtype))
    g.add_nodes(2, ntype='game')
    assert g.number_of_nodes('game') == 5

    # heterogeneous graph
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    g = create_test_heterograph3(idtype)
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    g.add_nodes(1, ntype='user')
    g.add_nodes(2, data={'h' : F.copy_to(F.tensor([2, 2],  dtype=idtype), ctx=F.ctx())}, ntype='game')
    g.add_nodes(0, ntype='developer')
    assert g.number_of_nodes('user') == 4
    assert g.number_of_nodes('game') == 4
    assert g.number_of_nodes('developer') == 2
    assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1, 0], dtype=idtype))
    assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2, 2], dtype=idtype))

@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="MXNet has error with (0,) shape tensor.")
@parametrize_dtype
def test_remove_edges(idtype):
    # homogeneous Graphs
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    e = 0
    g.remove_edges(e)
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([1], dtype=idtype))
    assert F.array_equal(v, F.tensor([2], dtype=idtype))
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    e = [0]
    g.remove_edges(e)
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([1], dtype=idtype))
    assert F.array_equal(v, F.tensor([2], dtype=idtype))
    e = F.tensor([0], dtype=idtype)
    g.remove_edges(e)
    assert g.number_of_edges() == 0

    # has node data
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    g.ndata['h'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
    g.remove_edges(1)
    assert g.number_of_edges() == 1
    assert F.array_equal(g.ndata['h'], F.tensor([1, 2, 3], dtype=idtype))

    # has edge data
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    g.edata['h'] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx())
    g.remove_edges(0)
    assert g.number_of_edges() == 1
    assert F.array_equal(g.edata['h'], F.tensor([2], dtype=idtype))

    # invalid eid
    assert_fail = False
    try:
        g.remove_edges(1)
    except:
        assert_fail = True
    assert assert_fail

    # bipartite graph
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    g = dgl.heterograph({
        ('user', 'plays', 'game'): ([0, 1], [1, 2])
    }, idtype=idtype, device=F.ctx())
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    e = 0
    g.remove_edges(e)
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([1], dtype=idtype))
    assert F.array_equal(v, F.tensor([2], dtype=idtype))
2239
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    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
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    e = [0]
    g.remove_edges(e)
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([1], dtype=idtype))
    assert F.array_equal(v, F.tensor([2], dtype=idtype))
    e = F.tensor([0], dtype=idtype)
    g.remove_edges(e)
    assert g.number_of_edges() == 0

    # has data
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    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
    g.nodes['user'].data['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx())
    g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2, 2], dtype=idtype), ctx=F.ctx())
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    g.edata['h'] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx())
    g.remove_edges(1)
    assert g.number_of_edges() == 1
    assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1], dtype=idtype))
    assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2], dtype=idtype))
    assert F.array_equal(g.edata['h'], F.tensor([1], dtype=idtype))

    # heterogeneous graph
2264
    g = create_test_heterograph3(idtype)
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    g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx())
    g.remove_edges(1, etype='plays')
    assert g.number_of_edges('plays') == 3
    u, v = g.edges(form='uv', order='eid', etype='plays')
    assert F.array_equal(u, F.tensor([0, 1, 2], dtype=idtype))
    assert F.array_equal(v, F.tensor([0, 1, 1], dtype=idtype))
    assert F.array_equal(g.edges['plays'].data['h'], F.tensor([1, 3, 4], dtype=idtype))
    # remove all edges of 'develops'
    g.remove_edges([0, 1], etype='develops')
    assert g.number_of_edges('develops') == 0
    assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1], dtype=idtype))
    assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2], dtype=idtype))
    assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3], dtype=idtype))

@parametrize_dtype
def test_remove_nodes(idtype):
    # homogeneous Graphs
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    n = 0
    g.remove_nodes(n)
    assert g.number_of_nodes() == 2
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0], dtype=idtype))
    assert F.array_equal(v, F.tensor([1], dtype=idtype))
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    n = [1]
    g.remove_nodes(n)
    assert g.number_of_nodes() == 2
    assert g.number_of_edges() == 0
    g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
    n = F.tensor([2], dtype=idtype)
    g.remove_nodes(n)
    assert g.number_of_nodes() == 2
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0], dtype=idtype))
    assert F.array_equal(v, F.tensor([1], dtype=idtype))

    # invalid nid
    assert_fail = False
    try:
        g.remove_nodes(3)
    except:
        assert_fail = True
    assert assert_fail

    # has node and edge data
    g = dgl.graph(([0, 0, 2], [0, 1, 2]), idtype=idtype, device=F.ctx())
    g.ndata['hv'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
    g.edata['he'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
    g.remove_nodes(F.tensor([0], dtype=idtype))
    assert g.number_of_nodes() == 2
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([1], dtype=idtype))
    assert F.array_equal(v, F.tensor([1], dtype=idtype))
    assert F.array_equal(g.ndata['hv'], F.tensor([2, 3], dtype=idtype))
    assert F.array_equal(g.edata['he'], F.tensor([3], dtype=idtype))

    # node id larger than current max node id
2326
2327
    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
2328
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2330
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2332
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    n = 0
    g.remove_nodes(n, ntype='user')
    assert g.number_of_nodes('user') == 1
    assert g.number_of_nodes('game') == 3
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0], dtype=idtype))
    assert F.array_equal(v, F.tensor([2], dtype=idtype))
2336
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    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
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    n = [1]
    g.remove_nodes(n, ntype='user')
    assert g.number_of_nodes('user') == 1
    assert g.number_of_nodes('game') == 3
    assert g.number_of_edges() == 1
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0], dtype=idtype))
    assert F.array_equal(v, F.tensor([1], dtype=idtype))
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    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
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    n = F.tensor([0], dtype=idtype)
    g.remove_nodes(n, ntype='game')
    assert g.number_of_nodes('user') == 2
    assert g.number_of_nodes('game') == 2
    assert g.number_of_edges() == 2
    u, v = g.edges(form='uv', order='eid')
    assert F.array_equal(u, F.tensor([0, 1], dtype=idtype))
    assert F.array_equal(v, F.tensor([0 ,1], dtype=idtype))

    # heterogeneous graph
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    g = create_test_heterograph3(idtype)
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    g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx())
    g.remove_nodes(0, ntype='game')
    assert g.number_of_nodes('user') == 3
    assert g.number_of_nodes('game') == 1
    assert g.number_of_nodes('developer') == 2
    assert g.number_of_edges('plays') == 2
    assert g.number_of_edges('develops') == 1
    assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1], dtype=idtype))
    assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2], dtype=idtype))
    assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3], dtype=idtype))
    u, v = g.edges(form='uv', order='eid', etype='plays')
    assert F.array_equal(u, F.tensor([1, 2], dtype=idtype))
    assert F.array_equal(v, F.tensor([0, 0], dtype=idtype))
    assert F.array_equal(g.edges['plays'].data['h'], F.tensor([3, 4], dtype=idtype))
    u, v = g.edges(form='uv', order='eid', etype='develops')
    assert F.array_equal(u, F.tensor([1], dtype=idtype))
    assert F.array_equal(v, F.tensor([0], dtype=idtype))
2376

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@parametrize_dtype
def test_frame(idtype):
    g = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())
    g.ndata['h'] = F.copy_to(F.tensor([0, 1, 2, 3], dtype=idtype), ctx=F.ctx())
    g.edata['h'] = F.copy_to(F.tensor([0, 1, 2], dtype=idtype), ctx=F.ctx())

    # remove nodes
    sg = dgl.remove_nodes(g, [3])
    # check for lazy update
    assert F.array_equal(sg._node_frames[0]._columns['h'].storage, g.ndata['h'])
    assert F.array_equal(sg._edge_frames[0]._columns['h'].storage, g.edata['h'])
    assert sg.ndata['h'].shape[0] == 3
    assert sg.edata['h'].shape[0] == 2
    # update after read
    assert F.array_equal(sg._node_frames[0]._columns['h'].storage, F.tensor([0, 1, 2], dtype=idtype))
    assert F.array_equal(sg._edge_frames[0]._columns['h'].storage, F.tensor([0, 1], dtype=idtype))

    ng = dgl.add_nodes(sg, 1)
    assert ng.ndata['h'].shape[0] == 4
    assert F.array_equal(ng._node_frames[0]._columns['h'].storage, F.tensor([0, 1, 2, 0], dtype=idtype))
    ng = dgl.add_edges(ng, [3], [1])
    assert ng.edata['h'].shape[0] == 3
    assert F.array_equal(ng._edge_frames[0]._columns['h'].storage, F.tensor([0, 1, 0], dtype=idtype))

    # multi level lazy update
    sg = dgl.remove_nodes(g, [3])
    assert F.array_equal(sg._node_frames[0]._columns['h'].storage, g.ndata['h'])
    assert F.array_equal(sg._edge_frames[0]._columns['h'].storage, g.edata['h'])
    ssg = dgl.remove_nodes(sg, [1])
    assert F.array_equal(ssg._node_frames[0]._columns['h'].storage, g.ndata['h'])
    assert F.array_equal(ssg._edge_frames[0]._columns['h'].storage, g.edata['h'])
    # ssg is changed
    assert ssg.ndata['h'].shape[0] == 2
    assert ssg.edata['h'].shape[0] == 0
    assert F.array_equal(ssg._node_frames[0]._columns['h'].storage, F.tensor([0, 2], dtype=idtype))
    # sg still in lazy model
    assert F.array_equal(sg._node_frames[0]._columns['h'].storage, g.ndata['h'])
    assert F.array_equal(sg._edge_frames[0]._columns['h'].storage, g.edata['h'])

@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TensorFlow always create a new tensor")
@unittest.skipIf(F._default_context_str == 'cpu', reason="cpu do not have context change problem")
@parametrize_dtype
def test_frame_device(idtype):
    g = dgl.graph(([0,1,2], [2,3,1]))
    g.ndata['h'] = F.copy_to(F.tensor([1,1,1,2], dtype=idtype), ctx=F.cpu())
    g.ndata['hh'] = F.copy_to(F.ones((4,3), dtype=idtype), ctx=F.cpu())
    g.edata['h'] = F.copy_to(F.tensor([1,2,3], dtype=idtype), ctx=F.cpu())

    g = g.to(F.ctx())
    # lazy device copy
    assert F.context(g._node_frames[0]._columns['h'].storage) == F.cpu()
    assert F.context(g._node_frames[0]._columns['hh'].storage) == F.cpu()
    print(g.ndata['h'])
    assert F.context(g._node_frames[0]._columns['h'].storage) == F.ctx()
    assert F.context(g._node_frames[0]._columns['hh'].storage) == F.cpu()
    assert F.context(g._edge_frames[0]._columns['h'].storage) == F.cpu()

    # lazy device copy in subgraph
    sg = dgl.node_subgraph(g, [0,1,2])
    assert F.context(sg._node_frames[0]._columns['h'].storage) == F.ctx()
    assert F.context(sg._node_frames[0]._columns['hh'].storage) == F.cpu()
    assert F.context(sg._edge_frames[0]._columns['h'].storage) == F.cpu()
    print(sg.ndata['hh'])
    assert F.context(sg._node_frames[0]._columns['hh'].storage) == F.ctx()
    assert F.context(sg._edge_frames[0]._columns['h'].storage) == F.cpu()

    # back to cpu
    sg = sg.to(F.cpu())
    assert F.context(sg._node_frames[0]._columns['h'].storage) == F.ctx()
    assert F.context(sg._node_frames[0]._columns['hh'].storage) == F.ctx()
    assert F.context(sg._edge_frames[0]._columns['h'].storage) == F.cpu()
    print(sg.ndata['h'])
    print(sg.ndata['hh'])
    print(sg.edata['h'])
    assert F.context(sg._node_frames[0]._columns['h'].storage) == F.cpu()
    assert F.context(sg._node_frames[0]._columns['hh'].storage) == F.cpu()
    assert F.context(sg._edge_frames[0]._columns['h'].storage) == F.cpu()

    # set some field
    sg = sg.to(F.ctx())
    assert F.context(sg._node_frames[0]._columns['h'].storage) == F.cpu()
    sg.ndata['h'][0] = 5
    assert F.context(sg._node_frames[0]._columns['h'].storage) == F.ctx()
    assert F.context(sg._node_frames[0]._columns['hh'].storage) == F.cpu()
    assert F.context(sg._edge_frames[0]._columns['h'].storage) == F.cpu()

    # add nodes
    ng = dgl.add_nodes(sg, 3)
    assert F.context(ng._node_frames[0]._columns['h'].storage) == F.ctx()
    assert F.context(ng._node_frames[0]._columns['hh'].storage) == F.ctx()
    assert F.context(ng._edge_frames[0]._columns['h'].storage) == F.cpu()



2471
if __name__ == '__main__':
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    # test_create()
    # test_query()
    # test_hypersparse()
    # test_adj("int32")
    # test_inc()
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    # test_view("int32")
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    # test_view1("int32")
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    # test_flatten(F.int32)
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    # test_convert_bound()
    # test_convert()
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    # test_to_device("int32")
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    # test_transform("int32")
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    # test_subgraph("int32")
    # test_subgraph_mask("int32")
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    # test_apply()
    # test_level1()
    # test_level2()
    # test_updates()
    # test_backward()
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    # test_empty_heterograph('int32')
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    # test_types_in_function()
    # test_stack_reduce()
    # test_isolated_ntype()
    # test_bipartite()
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    # test_dtype_cast()
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    # test_reverse("int32")
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    # test_format()
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    #test_add_edges(F.int32)
    #test_add_nodes(F.int32)
    #test_remove_edges(F.int32)
    #test_remove_nodes(F.int32)
    #test_clone(F.int32)
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    #test_frame(F.int32)
    #test_frame_device(F.int32)
    #test_empty_query(F.int32)
2507
    pass