test_heterograph.py 97.5 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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@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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        assert F.array_equal(F.tensor(sg.edges['plays'].data[dgl.EID]),
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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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        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}
    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)

    # 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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@parametrize_dtype
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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)

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@parametrize_dtype
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def test_isolated_ntype(idtype):
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    g = dgl.heterograph({
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        num_nodes_dict={'A': 3, 'B': 4, 'C': 4},
        idtype=idtype, device=F.ctx())
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    assert g.number_of_nodes('A') == 3
    assert g.number_of_nodes('B') == 4
    assert g.number_of_nodes('C') == 4

    g = dgl.heterograph({
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        num_nodes_dict={'A': 3, 'B': 4, 'C': 4},
        idtype=idtype, device=F.ctx())
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    assert g.number_of_nodes('A') == 3
    assert g.number_of_nodes('B') == 4
    assert g.number_of_nodes('C') == 4

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    G = dgl.graph(([0, 1, 2], [4, 5, 6]), num_nodes=11, idtype=idtype, device=F.ctx())
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    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)
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    g = dgl.to_heterogeneous(G, ['A', 'B', 'C'], ['AB'])
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    assert g.number_of_nodes('A') == 3
    assert g.number_of_nodes('B') == 4
    assert g.number_of_nodes('C') == 4

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def test_ismultigraph(idtype):
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    g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5])},
                         {'A': 6, 'B': 6}, idtype=idtype, device=F.ctx())
1616
    assert g1.is_multigraph == False
1617
1618
    g2 = dgl.heterograph({('A', 'AC', 'C'): ([0, 0, 0, 1], [1, 1, 2, 5])},
                         {'A': 6, 'C': 6}, idtype=idtype, device=F.ctx())
1619
    assert g2.is_multigraph == True
1620
    g3 = dgl.graph(((0, 1), (1, 2)), num_nodes=6, idtype=idtype, device=F.ctx())
1621
    assert g3.is_multigraph == False
1622
    g4 = dgl.graph(([0, 0, 1], [1, 1, 2]), num_nodes=6, idtype=idtype, device=F.ctx())
1623
    assert g4.is_multigraph == True
1624
1625
1626
1627
    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())
1628
    assert g.is_multigraph == False
1629
1630
1631
1632
    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())
1633
    assert g.is_multigraph == True
1634
1635
1636
1637
    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())
1638
    assert g.is_multigraph == True
1639
1640
1641
1642
    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())
1643
1644
    assert g.is_multigraph == True

1645
@parametrize_dtype
1646
def test_bipartite(idtype):
1647
1648
    g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1], [1, 2, 5])},
                         idtype=idtype, device=F.ctx())
1649
1650
1651
1652
1653
1654
1655
    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
1656
1657
1658
1659
    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
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
    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
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
    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
1697

1698
@parametrize_dtype
1699
def test_dtype_cast(idtype):
1700
    g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
1701
    assert g.idtype == idtype
1702
1703
    g.ndata["feat"] = F.tensor([3, 4, 5])
    g.edata["h"] = F.tensor([3, 4, 5, 6])
1704
    if idtype == "int32":
1705
        g_cast = g.long()
1706
        assert g_cast.idtype == F.int64
1707
1708
    else:
        g_cast = g.int()
1709
1710
        assert g_cast.idtype == F.int32
    test_utils.check_graph_equal(g, g_cast, check_idtype=False)
1711

1712
1713
@parametrize_dtype
def test_format(idtype):
1714
    # single relation
1715
    g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
1716
1717
1718
    assert g.formats()['created'] == ['coo']
    g1 = g.formats(['coo', 'csr', 'csc'])
    assert len(g1.formats()['created']) + len(g1.formats()['not created']) == 3
1719
    g1.create_formats_()
1720
1721
    assert len(g1.formats()['created']) == 3
    assert g.formats()['created'] == ['coo']
1722
1723
1724

    # multiple relation
    g = dgl.heterograph({
1725
1726
1727
1728
        ('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())
1729
1730
    user_feat = F.randn((g['follows'].number_of_src_nodes(), 5))
    g['follows'].srcdata['h'] = user_feat
1731
    g1 = g.formats('csc')
1732
1733
1734
    # test frame
    assert F.array_equal(g1['follows'].srcdata['h'], user_feat)
    # test each relation graph
1735
1736
    assert g1.formats()['created'] == ['csc']
    assert len(g1.formats()['not created']) == 0
1737

1738
1739
@parametrize_dtype
def test_edges_order(idtype):
1740
1741
1742
1743
    # (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])
1744
    ), idtype=idtype, device=F.ctx())
1745

1746
    print(g.formats())
1747
    src, dst = g.all_edges(order='srcdst')
1748
1749
    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))
1750

1751
@parametrize_dtype
1752
def test_reverse(idtype):
1753
1754
    g = dgl.heterograph({
        ('user', 'follows', 'user'): ([0, 1, 2, 4, 3 ,1, 3], [1, 2, 3, 2, 0, 0, 1]),
1755
    }, idtype=idtype, device=F.ctx())
1756
    gidx = g._graph
1757
    r_gidx = gidx.reverse()
1758
1759
1760
1761
1762

    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)
1763
1764
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1765
1766

    # force to start with 'csr'
1767
1768
    gidx = gidx.formats('csr')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1769
    r_gidx = gidx.reverse()
1770
1771
    assert 'csr' in gidx.formats()['created']
    assert 'csc' in r_gidx.formats()['created']
1772
1773
1774
1775
    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)
1776
1777
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1778
1779

    # force to start with 'csc'
1780
1781
    gidx = gidx.formats('csc')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1782
    r_gidx = gidx.reverse()
1783
1784
    assert 'csc' in gidx.formats()['created']
    assert 'csr' in r_gidx.formats()['created']
1785
1786
1787
1788
    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)
1789
1790
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1791
1792
1793
1794
1795

    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]),
1796
        }, idtype=idtype, device=F.ctx())
1797
    gidx = g._graph
1798
1799
1800
1801
1802
1803
1804
1805
    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]

1806
1807
1808
1809
1810
1811
1812
1813
1814
    # 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)
1815
1816
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1817
1818
    g_s, g_d, _ = gidx.edges(1)
    rg_s, rg_d, _ = r_gidx.edges(1)
1819
1820
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1821
1822
    g_s, g_d, _ = gidx.edges(2)
    rg_s, rg_d, _ = r_gidx.edges(2)
1823
1824
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1825
1826

    # force to start with 'csr'
1827
1828
    gidx = gidx.formats('csr')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1829
    r_gidx = gidx.reverse()
1830
    # three node types and three edge types
1831
1832
    assert 'csr' in gidx.formats()['created']
    assert 'csc' in r_gidx.formats()['created']
1833
1834
1835
1836
1837
1838
1839
1840
    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)
1841
1842
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1843
1844
    g_s, g_d, _ = gidx.edges(1)
    rg_s, rg_d, _ = r_gidx.edges(1)
1845
1846
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1847
1848
    g_s, g_d, _ = gidx.edges(2)
    rg_s, rg_d, _ = r_gidx.edges(2)
1849
1850
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1851
1852

    # force to start with 'csc'
1853
1854
    gidx = gidx.formats('csc')
    gidx = gidx.formats(['coo', 'csr', 'csc'])
1855
    r_gidx = gidx.reverse()
1856
    # three node types and three edge types
1857
1858
    assert 'csc' in gidx.formats()['created']
    assert 'csr' in r_gidx.formats()['created']
1859
1860
1861
1862
1863
1864
1865
1866
    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)
1867
1868
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1869
1870
    g_s, g_d, _ = gidx.edges(1)
    rg_s, rg_d, _ = r_gidx.edges(1)
1871
1872
    assert F.array_equal(g_s, rg_d)
    assert F.array_equal(g_d, rg_s)
1873
1874
    g_s, g_d, _ = gidx.edges(2)
    rg_s, rg_d, _ = r_gidx.edges(2)
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
    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
1903
    g = dgl.graph(([], []), num_nodes=0, idtype=idtype, device=F.ctx())
1904
1905
1906
1907
1908
    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
1909
    g = create_test_heterograph3(idtype)
1910
1911
1912
1913
1914
1915
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
1944
1945
1946
1947
1948
1949
1950
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
    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
1994
    g = dgl.graph(([], []), num_nodes=0, idtype=idtype, device=F.ctx())
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
    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
2009
2010
    g = dgl.heterograph({('user', 'plays', 'game'): ([0, 1], [1, 2])},
                        idtype=idtype, device=F.ctx())
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
    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
2037
2038
    g = dgl.heterograph({('user', 'plays', 'game'): ([0, 1], [1, 2])},
                        idtype=idtype, device=F.ctx())
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
    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
2051
2052
2053
2054
2055
    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())
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
    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
2074
    g = create_test_heterograph3(idtype)
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
    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
2118
    g = dgl.graph(([], []), num_nodes=3, idtype=idtype, device=F.ctx())
2119
2120
2121
2122
2123
2124
    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
2125
2126
    g = dgl.heterograph({('user', 'plays', 'game'): ([0, 1], [1, 2])},
                        idtype=idtype, device=F.ctx())
2127
2128
2129
2130
2131
2132
2133
    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
2134
    g = create_test_heterograph3(idtype)
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
    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
2189
2190
2191
    g = dgl.heterograph({
        ('user', 'plays', 'game'): ([0, 1], [1, 2])
    }, idtype=idtype, device=F.ctx())
2192
2193
2194
2195
2196
2197
    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))
2198
2199
    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
    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
2211
2212
2213
2214
    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())
2215
2216
2217
2218
2219
2220
2221
2222
    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
2223
    g = create_test_heterograph3(idtype)
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
    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
2285
2286
    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
2287
2288
2289
2290
2291
2292
2293
2294
    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))
2295
2296
    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
2297
2298
2299
2300
2301
2302
2303
2304
    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))
2305
2306
    g = dgl.heterograph(
        {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx())
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
    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
2317
    g = create_test_heterograph3(idtype)
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
    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))
2335

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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()



2430
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)
    test_frame(F.int32)
    test_frame_device(F.int32)
2465
    pass