test_dist_graph_store.py

import os
os.environ['OMP_NUM_THREADS'] = '1'
import dgl
import sys
import numpy as np
import time
import socket
from scipy import sparse as spsp
from numpy.testing import assert_array_equal
from multiprocessing import Process, Manager, Condition, Value
import multiprocessing as mp
from dgl.heterograph_index import create_unitgraph_from_coo
from dgl.data.utils import load_graphs, save_graphs
from dgl.distributed import DistGraphServer, DistGraph
from dgl.distributed import partition_graph, load_partition, load_partition_book, node_split, edge_split
from numpy.testing import assert_almost_equal
import backend as F
import math
import unittest
import pickle
from utils import reset_envs, generate_ip_config

if os.name != 'nt':
    import fcntl
    import struct

def create_random_graph(n):
    arr = (spsp.random(n, n, density=0.001, format='coo', random_state=100) != 0).astype(np.int64)
    return dgl.from_scipy(arr)

def run_server(graph_name, server_id, server_count, num_clients, shared_mem, keep_alive=False):
    g = DistGraphServer(server_id, "kv_ip_config.txt", server_count, num_clients,
                        '/tmp/dist_graph/{}.json'.format(graph_name),
                        disable_shared_mem=not shared_mem,
                        graph_format=['csc', 'coo'], keep_alive=keep_alive)
    print('start server', server_id)
    g.start()

def emb_init(shape, dtype):
    return F.zeros(shape, dtype, F.cpu())

def rand_init(shape, dtype):
    return F.tensor(np.random.normal(size=shape), F.float32)

def check_dist_graph_empty(g, num_clients, num_nodes, num_edges):
    # Test API
    assert g.number_of_nodes() == num_nodes
    assert g.number_of_edges() == num_edges

    # Test init node data
    new_shape = (g.number_of_nodes(), 2)
    g.ndata['test1'] = dgl.distributed.DistTensor(new_shape, F.int32)
    nids = F.arange(0, int(g.number_of_nodes() / 2))
    feats = g.ndata['test1'][nids]
    assert np.all(F.asnumpy(feats) == 0)

    # create a tensor and destroy a tensor and create it again.
    test3 = dgl.distributed.DistTensor(new_shape, F.float32, 'test3', init_func=rand_init)
    del test3
    test3 = dgl.distributed.DistTensor((g.number_of_nodes(), 3), F.float32, 'test3')
    del test3

    # Test write data
    new_feats = F.ones((len(nids), 2), F.int32, F.cpu())
    g.ndata['test1'][nids] = new_feats
    feats = g.ndata['test1'][nids]
    assert np.all(F.asnumpy(feats) == 1)

    # Test metadata operations.
    assert g.node_attr_schemes()['test1'].dtype == F.int32

    print('end')

def run_client_empty(graph_name, part_id, server_count, num_clients, num_nodes, num_edges):
    os.environ['DGL_NUM_SERVER'] = str(server_count)
    dgl.distributed.initialize("kv_ip_config.txt")
    gpb, graph_name, _, _ = load_partition_book('/tmp/dist_graph/{}.json'.format(graph_name),
                                                part_id, None)
    g = DistGraph(graph_name, gpb=gpb)
    check_dist_graph_empty(g, num_clients, num_nodes, num_edges)

def check_server_client_empty(shared_mem, num_servers, num_clients):
    prepare_dist(num_servers)
    g = create_random_graph(10000)

    # Partition the graph
    num_parts = 1
    graph_name = 'dist_graph_test_1'
    partition_graph(g, graph_name, num_parts, '/tmp/dist_graph')

    # let's just test on one partition for now.
    # We cannot run multiple servers and clients on the same machine.
    serv_ps = []
    ctx = mp.get_context('spawn')
    for serv_id in range(num_servers):
        p = ctx.Process(target=run_server, args=(graph_name, serv_id, num_servers,
                                                 num_clients, shared_mem))
        serv_ps.append(p)
        p.start()

    cli_ps = []
    for cli_id in range(num_clients):
        print('start client', cli_id)
        p = ctx.Process(target=run_client_empty, args=(graph_name, 0, num_servers, num_clients,
                                                       g.number_of_nodes(), g.number_of_edges()))
        p.start()
        cli_ps.append(p)

    for p in cli_ps:
        p.join()

    for p in serv_ps:
        p.join()

    print('clients have terminated')

def run_client(graph_name, part_id, server_count, num_clients, num_nodes, num_edges, group_id):
    os.environ['DGL_NUM_SERVER'] = str(server_count)
    os.environ['DGL_GROUP_ID'] = str(group_id)
    dgl.distributed.initialize("kv_ip_config.txt")
    gpb, graph_name, _, _ = load_partition_book('/tmp/dist_graph/{}.json'.format(graph_name),
                                                part_id, None)
    g = DistGraph(graph_name, gpb=gpb)
    check_dist_graph(g, num_clients, num_nodes, num_edges)

def run_emb_client(graph_name, part_id, server_count, num_clients, num_nodes, num_edges, group_id):
    os.environ['DGL_NUM_SERVER'] = str(server_count)
    os.environ['DGL_GROUP_ID'] = str(group_id)
    dgl.distributed.initialize("kv_ip_config.txt")
    gpb, graph_name, _, _ = load_partition_book('/tmp/dist_graph/{}.json'.format(graph_name),
                                                part_id, None)
    g = DistGraph(graph_name, gpb=gpb)
    check_dist_emb(g, num_clients, num_nodes, num_edges)

def run_client_hierarchy(graph_name, part_id, server_count, node_mask, edge_mask, return_dict):
    os.environ['DGL_NUM_SERVER'] = str(server_count)
    dgl.distributed.initialize("kv_ip_config.txt")
    gpb, graph_name, _, _ = load_partition_book('/tmp/dist_graph/{}.json'.format(graph_name),
                                                part_id, None)
    g = DistGraph(graph_name, gpb=gpb)
    node_mask = F.tensor(node_mask)
    edge_mask = F.tensor(edge_mask)
    nodes = node_split(node_mask, g.get_partition_book(), node_trainer_ids=g.ndata['trainer_id'])
    edges = edge_split(edge_mask, g.get_partition_book(), edge_trainer_ids=g.edata['trainer_id'])
    rank = g.rank()
    return_dict[rank] = (nodes, edges)

def check_dist_emb(g, num_clients, num_nodes, num_edges):
    from dgl.distributed.optim import SparseAdagrad
    from dgl.distributed import DistEmbedding
    # Test sparse emb
    try:
        emb = DistEmbedding(g.number_of_nodes(), 1, 'emb1', emb_init)
        nids = F.arange(0, int(g.number_of_nodes()))
        lr = 0.001
        optimizer = SparseAdagrad([emb], lr=lr)
        with F.record_grad():
            feats = emb(nids)
            assert np.all(F.asnumpy(feats) == np.zeros((len(nids), 1)))
            loss = F.sum(feats + 1, 0)
        loss.backward()
        optimizer.step()
        feats = emb(nids)
        if num_clients == 1:
            assert_almost_equal(F.asnumpy(feats), np.ones((len(nids), 1)) * -lr)
        rest = np.setdiff1d(np.arange(g.number_of_nodes()), F.asnumpy(nids))
        feats1 = emb(rest)
        assert np.all(F.asnumpy(feats1) == np.zeros((len(rest), 1)))

        policy = dgl.distributed.PartitionPolicy('node', g.get_partition_book())
        grad_sum = dgl.distributed.DistTensor((g.number_of_nodes(), 1), F.float32,
                                              'emb1_sum', policy)
        if num_clients == 1:
            assert np.all(F.asnumpy(grad_sum[nids]) == np.ones((len(nids), 1)) * num_clients)
        assert np.all(F.asnumpy(grad_sum[rest]) == np.zeros((len(rest), 1)))

        emb = DistEmbedding(g.number_of_nodes(), 1, 'emb2', emb_init)
        with F.no_grad():
            feats1 = emb(nids)
        assert np.all(F.asnumpy(feats1) == 0)

        optimizer = SparseAdagrad([emb], lr=lr)
        with F.record_grad():
            feats1 = emb(nids)
            feats2 = emb(nids)
            feats = F.cat([feats1, feats2], 0)
            assert np.all(F.asnumpy(feats) == np.zeros((len(nids) * 2, 1)))
            loss = F.sum(feats + 1, 0)
        loss.backward()
        optimizer.step()
        with F.no_grad():
            feats = emb(nids)
        if num_clients == 1:
            assert_almost_equal(F.asnumpy(feats), np.ones((len(nids), 1)) * 1 * -lr)
        rest = np.setdiff1d(np.arange(g.number_of_nodes()), F.asnumpy(nids))
        feats1 = emb(rest)
        assert np.all(F.asnumpy(feats1) == np.zeros((len(rest), 1)))
    except NotImplementedError as e:
        pass
    except Exception as e:
        print(e)
        sys.exit(-1)

def check_dist_graph(g, num_clients, num_nodes, num_edges):
    # Test API
    assert g.number_of_nodes() == num_nodes
    assert g.number_of_edges() == num_edges

    # Test reading node data
    nids = F.arange(0, int(g.number_of_nodes() / 2))
    feats1 = g.ndata['features'][nids]
    feats = F.squeeze(feats1, 1)
    assert np.all(F.asnumpy(feats == nids))

    # Test reading edge data
    eids = F.arange(0, int(g.number_of_edges() / 2))
    feats1 = g.edata['features'][eids]
    feats = F.squeeze(feats1, 1)
    assert np.all(F.asnumpy(feats == eids))

    # Test init node data
    new_shape = (g.number_of_nodes(), 2)
    test1 = dgl.distributed.DistTensor(new_shape, F.int32)
    g.ndata['test1'] = test1
    feats = g.ndata['test1'][nids]
    assert np.all(F.asnumpy(feats) == 0)
    assert test1.count_nonzero() == 0

    # reference to a one that exists
    test2 = dgl.distributed.DistTensor(new_shape, F.float32, 'test2', init_func=rand_init)
    test3 = dgl.distributed.DistTensor(new_shape, F.float32, 'test2')
    assert np.all(F.asnumpy(test2[nids]) == F.asnumpy(test3[nids]))

    # create a tensor and destroy a tensor and create it again.
    test3 = dgl.distributed.DistTensor(new_shape, F.float32, 'test3', init_func=rand_init)
    del test3
    test3 = dgl.distributed.DistTensor((g.number_of_nodes(), 3), F.float32, 'test3')
    del test3

    # add tests for anonymous distributed tensor.
    test3 = dgl.distributed.DistTensor(new_shape, F.float32, init_func=rand_init)
    data = test3[0:10]
    test4 = dgl.distributed.DistTensor(new_shape, F.float32, init_func=rand_init)
    del test3
    test5 = dgl.distributed.DistTensor(new_shape, F.float32, init_func=rand_init)
    assert np.sum(F.asnumpy(test5[0:10] != data)) > 0

    # test a persistent tesnor
    test4 = dgl.distributed.DistTensor(new_shape, F.float32, 'test4', init_func=rand_init,
                                       persistent=True)
    del test4
    try:
        test4 = dgl.distributed.DistTensor((g.number_of_nodes(), 3), F.float32, 'test4')
        raise Exception('')
    except:
        pass

    # Test write data
    new_feats = F.ones((len(nids), 2), F.int32, F.cpu())
    g.ndata['test1'][nids] = new_feats
    feats = g.ndata['test1'][nids]
    assert np.all(F.asnumpy(feats) == 1)

    # Test metadata operations.
    assert len(g.ndata['features']) == g.number_of_nodes()
    assert g.ndata['features'].shape == (g.number_of_nodes(), 1)
    assert g.ndata['features'].dtype == F.int64
    assert g.node_attr_schemes()['features'].dtype == F.int64
    assert g.node_attr_schemes()['test1'].dtype == F.int32
    assert g.node_attr_schemes()['features'].shape == (1,)

    selected_nodes = np.random.randint(0, 100, size=g.number_of_nodes()) > 30
    # Test node split
    nodes = node_split(selected_nodes, g.get_partition_book())
    nodes = F.asnumpy(nodes)
    # We only have one partition, so the local nodes are basically all nodes in the graph.
    local_nids = np.arange(g.number_of_nodes())
    for n in nodes:
        assert n in local_nids

    print('end')

def check_dist_emb_server_client(shared_mem, num_servers, num_clients, num_groups=1):
    prepare_dist(num_servers)
    g = create_random_graph(10000)

    # Partition the graph
    num_parts = 1
    graph_name = f'check_dist_emb_{shared_mem}_{num_servers}_{num_clients}_{num_groups}'
    g.ndata['features'] = F.unsqueeze(F.arange(0, g.number_of_nodes()), 1)
    g.edata['features'] = F.unsqueeze(F.arange(0, g.number_of_edges()), 1)
    partition_graph(g, graph_name, num_parts, '/tmp/dist_graph')

    # let's just test on one partition for now.
    # We cannot run multiple servers and clients on the same machine.
    serv_ps = []
    ctx = mp.get_context('spawn')
    keep_alive = num_groups > 1
    for serv_id in range(num_servers):
        p = ctx.Process(target=run_server, args=(graph_name, serv_id, num_servers,
                                                 num_clients, shared_mem, keep_alive))
        serv_ps.append(p)
        p.start()

    cli_ps = []
    for cli_id in range(num_clients):
        for group_id in range(num_groups):
            print('start client[{}] for group[{}]'.format(cli_id, group_id))
            p = ctx.Process(target=run_emb_client, args=(graph_name, 0, num_servers, num_clients,
                                                        g.number_of_nodes(),
                                                        g.number_of_edges(),
                                                        group_id))
            p.start()
            time.sleep(1) # avoid race condition when instantiating DistGraph
            cli_ps.append(p)

    for p in cli_ps:
        p.join()
        assert p.exitcode == 0

    if keep_alive:
        for p in serv_ps:
            assert p.is_alive()
        # force shutdown server
        dgl.distributed.shutdown_servers("kv_ip_config.txt", num_servers)
    for p in serv_ps:
        p.join()

    print('clients have terminated')

def check_server_client(shared_mem, num_servers, num_clients, num_groups=1):
    prepare_dist(num_servers)
    g = create_random_graph(10000)

    # Partition the graph
    num_parts = 1
    graph_name = f'check_server_client_{shared_mem}_{num_servers}_{num_clients}_{num_groups}'
    g.ndata['features'] = F.unsqueeze(F.arange(0, g.number_of_nodes()), 1)
    g.edata['features'] = F.unsqueeze(F.arange(0, g.number_of_edges()), 1)
    partition_graph(g, graph_name, num_parts, '/tmp/dist_graph')

    # let's just test on one partition for now.
    # We cannot run multiple servers and clients on the same machine.
    serv_ps = []
    ctx = mp.get_context('spawn')
    keep_alive = num_groups > 1
    for serv_id in range(num_servers):
        p = ctx.Process(target=run_server, args=(graph_name, serv_id, num_servers,
                                                 num_clients, shared_mem, keep_alive))
        serv_ps.append(p)
        p.start()

    # launch different client groups simultaneously
    cli_ps = []
    for cli_id in range(num_clients):
        for group_id in range(num_groups):
            print('start client[{}] for group[{}]'.format(cli_id, group_id))
            p = ctx.Process(target=run_client, args=(graph_name, 0, num_servers, num_clients, g.number_of_nodes(),
                                                    g.number_of_edges(), group_id))
            p.start()
            time.sleep(1) # avoid race condition when instantiating DistGraph
            cli_ps.append(p)
    for p in cli_ps:
        p.join()

    if keep_alive:
        for p in serv_ps:
            assert p.is_alive()
        # force shutdown server
        dgl.distributed.shutdown_servers("kv_ip_config.txt", num_servers)
    for p in serv_ps:
        p.join()

    print('clients have terminated')

def check_server_client_hierarchy(shared_mem, num_servers, num_clients):
    prepare_dist(num_servers)
    g = create_random_graph(10000)

    # Partition the graph
    num_parts = 1
    graph_name = 'dist_graph_test_2'
    g.ndata['features'] = F.unsqueeze(F.arange(0, g.number_of_nodes()), 1)
    g.edata['features'] = F.unsqueeze(F.arange(0, g.number_of_edges()), 1)
    partition_graph(g, graph_name, num_parts, '/tmp/dist_graph', num_trainers_per_machine=num_clients)

    # let's just test on one partition for now.
    # We cannot run multiple servers and clients on the same machine.
    serv_ps = []
    ctx = mp.get_context('spawn')
    for serv_id in range(num_servers):
        p = ctx.Process(target=run_server, args=(graph_name, serv_id, num_servers,
                                                 num_clients, shared_mem))
        serv_ps.append(p)
        p.start()

    cli_ps = []
    manager = mp.Manager()
    return_dict = manager.dict()
    node_mask = np.zeros((g.number_of_nodes(),), np.int32)
    edge_mask = np.zeros((g.number_of_edges(),), np.int32)
    nodes = np.random.choice(g.number_of_nodes(), g.number_of_nodes() // 10, replace=False)
    edges = np.random.choice(g.number_of_edges(), g.number_of_edges() // 10, replace=False)
    node_mask[nodes] = 1
    edge_mask[edges] = 1
    nodes = np.sort(nodes)
    edges = np.sort(edges)
    for cli_id in range(num_clients):
        print('start client', cli_id)
        p = ctx.Process(target=run_client_hierarchy, args=(graph_name, 0, num_servers,
                                                           node_mask, edge_mask, return_dict))
        p.start()
        cli_ps.append(p)

    for p in cli_ps:
        p.join()
    for p in serv_ps:
        p.join()

    nodes1 = []
    edges1 = []
    for n, e in return_dict.values():
        nodes1.append(n)
        edges1.append(e)
    nodes1, _ = F.sort_1d(F.cat(nodes1, 0))
    edges1, _ = F.sort_1d(F.cat(edges1, 0))
    assert np.all(F.asnumpy(nodes1) == nodes)
    assert np.all(F.asnumpy(edges1) == edges)

    print('clients have terminated')


def run_client_hetero(graph_name, part_id, server_count, num_clients, num_nodes, num_edges):
    os.environ['DGL_NUM_SERVER'] = str(server_count)
    dgl.distributed.initialize("kv_ip_config.txt")
    gpb, graph_name, _, _ = load_partition_book('/tmp/dist_graph/{}.json'.format(graph_name),
                                                part_id, None)
    g = DistGraph(graph_name, gpb=gpb)
    check_dist_graph_hetero(g, num_clients, num_nodes, num_edges)

def create_random_hetero():
    num_nodes = {'n1': 10000, 'n2': 10010, 'n3': 10020}
    etypes = [('n1', 'r1', 'n2'),
              ('n1', 'r2', 'n3'),
              ('n2', 'r3', 'n3')]
    edges = {}
    for etype in etypes:
        src_ntype, _, dst_ntype = etype
        arr = spsp.random(num_nodes[src_ntype], num_nodes[dst_ntype], density=0.001, format='coo',
                          random_state=100)
        edges[etype] = (arr.row, arr.col)
    g = dgl.heterograph(edges, num_nodes)
    g.nodes['n1'].data['feat'] = F.unsqueeze(F.arange(0, g.number_of_nodes('n1')), 1)
    g.edges['r1'].data['feat'] = F.unsqueeze(F.arange(0, g.number_of_edges('r1')), 1)
    return g

def check_dist_graph_hetero(g, num_clients, num_nodes, num_edges):
    # Test API
    for ntype in num_nodes:
        assert ntype in g.ntypes
        assert num_nodes[ntype] == g.number_of_nodes(ntype)
    for etype in num_edges:
        assert etype in g.etypes
        assert num_edges[etype] == g.number_of_edges(etype)
    etypes = [('n1', 'r1', 'n2'),
              ('n1', 'r2', 'n3'),
              ('n2', 'r3', 'n3')]
    for i, etype in enumerate(g.canonical_etypes):
        assert etype[0] == etypes[i][0]
        assert etype[1] == etypes[i][1]
        assert etype[2] == etypes[i][2]
    assert g.number_of_nodes() == sum([num_nodes[ntype] for ntype in num_nodes])
    assert g.number_of_edges() == sum([num_edges[etype] for etype in num_edges])

    # Test reading node data
    nids = F.arange(0, int(g.number_of_nodes('n1') / 2))
    feats1 = g.nodes['n1'].data['feat'][nids]
    feats = F.squeeze(feats1, 1)
    assert np.all(F.asnumpy(feats == nids))

    # Test reading edge data
    eids = F.arange(0, int(g.number_of_edges('r1') / 2))
    feats1 = g.edges['r1'].data['feat'][eids]
    feats = F.squeeze(feats1, 1)
    assert np.all(F.asnumpy(feats == eids))

    # Test init node data
    new_shape = (g.number_of_nodes('n1'), 2)
    g.nodes['n1'].data['test1'] = dgl.distributed.DistTensor(new_shape, F.int32)
    feats = g.nodes['n1'].data['test1'][nids]
    assert np.all(F.asnumpy(feats) == 0)

    # create a tensor and destroy a tensor and create it again.
    test3 = dgl.distributed.DistTensor(new_shape, F.float32, 'test3', init_func=rand_init)
    del test3
    test3 = dgl.distributed.DistTensor((g.number_of_nodes('n1'), 3), F.float32, 'test3')
    del test3

    # add tests for anonymous distributed tensor.
    test3 = dgl.distributed.DistTensor(new_shape, F.float32, init_func=rand_init)
    data = test3[0:10]
    test4 = dgl.distributed.DistTensor(new_shape, F.float32, init_func=rand_init)
    del test3
    test5 = dgl.distributed.DistTensor(new_shape, F.float32, init_func=rand_init)
    assert np.sum(F.asnumpy(test5[0:10] != data)) > 0

    # test a persistent tesnor
    test4 = dgl.distributed.DistTensor(new_shape, F.float32, 'test4', init_func=rand_init,
                                       persistent=True)
    del test4
    try:
        test4 = dgl.distributed.DistTensor((g.number_of_nodes('n1'), 3), F.float32, 'test4')
        raise Exception('')
    except:
        pass

    # Test write data
    new_feats = F.ones((len(nids), 2), F.int32, F.cpu())
    g.nodes['n1'].data['test1'][nids] = new_feats
    feats = g.nodes['n1'].data['test1'][nids]
    assert np.all(F.asnumpy(feats) == 1)

    # Test metadata operations.
    assert len(g.nodes['n1'].data['feat']) == g.number_of_nodes('n1')
    assert g.nodes['n1'].data['feat'].shape == (g.number_of_nodes('n1'), 1)
    assert g.nodes['n1'].data['feat'].dtype == F.int64

    selected_nodes = np.random.randint(0, 100, size=g.number_of_nodes('n1')) > 30
    # Test node split
    nodes = node_split(selected_nodes, g.get_partition_book(), ntype='n1')
    nodes = F.asnumpy(nodes)
    # We only have one partition, so the local nodes are basically all nodes in the graph.
    local_nids = np.arange(g.number_of_nodes('n1'))
    for n in nodes:
        assert n in local_nids

    print('end')

def check_server_client_hetero(shared_mem, num_servers, num_clients):
    prepare_dist(num_servers)
    g = create_random_hetero()

    # Partition the graph
    num_parts = 1
    graph_name = 'dist_graph_test_3'
    partition_graph(g, graph_name, num_parts, '/tmp/dist_graph')

    # let's just test on one partition for now.
    # We cannot run multiple servers and clients on the same machine.
    serv_ps = []
    ctx = mp.get_context('spawn')
    for serv_id in range(num_servers):
        p = ctx.Process(target=run_server, args=(graph_name, serv_id, num_servers,
                                                 num_clients, shared_mem))
        serv_ps.append(p)
        p.start()

    cli_ps = []
    num_nodes = {ntype: g.number_of_nodes(ntype) for ntype in g.ntypes}
    num_edges = {etype: g.number_of_edges(etype) for etype in g.etypes}
    for cli_id in range(num_clients):
        print('start client', cli_id)
        p = ctx.Process(target=run_client_hetero, args=(graph_name, 0, num_servers, num_clients, num_nodes,
                                                        num_edges))
        p.start()
        cli_ps.append(p)

    for p in cli_ps:
        p.join()

    for p in serv_ps:
        p.join()

    print('clients have terminated')

@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support some of operations in DistGraph")
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Turn off Mxnet support")
def test_server_client():
    reset_envs()
    os.environ['DGL_DIST_MODE'] = 'distributed'
    check_server_client_hierarchy(False, 1, 4)
    check_server_client_empty(True, 1, 1)
    check_server_client_hetero(True, 1, 1)
    check_server_client_hetero(False, 1, 1)
    check_server_client(True, 1, 1)
    check_server_client(False, 1, 1)
    check_server_client(True, 2, 2)
    check_server_client(True, 1, 1, 2)
    check_server_client(False, 1, 1, 2)
    check_server_client(True, 2, 2, 2)

@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support distributed DistEmbedding")
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Mxnet doesn't support distributed DistEmbedding")
def test_dist_emb_server_client():
    reset_envs()
    os.environ['DGL_DIST_MODE'] = 'distributed'
    check_dist_emb_server_client(True, 1, 1)
    check_dist_emb_server_client(False, 1, 1)
    check_dist_emb_server_client(True, 2, 2)
    check_dist_emb_server_client(True, 1, 1, 2)
    check_dist_emb_server_client(False, 1, 1, 2)
    check_dist_emb_server_client(True, 2, 2, 2)

@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support some of operations in DistGraph")
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Turn off Mxnet support")
def test_standalone():
    reset_envs()
    os.environ['DGL_DIST_MODE'] = 'standalone'

    g = create_random_graph(10000)
    # Partition the graph
    num_parts = 1
    graph_name = 'dist_graph_test_3'
    g.ndata['features'] = F.unsqueeze(F.arange(0, g.number_of_nodes()), 1)
    g.edata['features'] = F.unsqueeze(F.arange(0, g.number_of_edges()), 1)
    partition_graph(g, graph_name, num_parts, '/tmp/dist_graph')

    dgl.distributed.initialize("kv_ip_config.txt")
    dist_g = DistGraph(graph_name, part_config='/tmp/dist_graph/{}.json'.format(graph_name))
    check_dist_graph(dist_g, 1, g.number_of_nodes(), g.number_of_edges())
    dgl.distributed.exit_client() # this is needed since there's two test here in one process

@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support distributed DistEmbedding")
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Mxnet doesn't support distributed DistEmbedding")
def test_standalone_node_emb():
    reset_envs()
    os.environ['DGL_DIST_MODE'] = 'standalone'

    g = create_random_graph(10000)
    # Partition the graph
    num_parts = 1
    graph_name = 'dist_graph_test_3'
    g.ndata['features'] = F.unsqueeze(F.arange(0, g.number_of_nodes()), 1)
    g.edata['features'] = F.unsqueeze(F.arange(0, g.number_of_edges()), 1)
    partition_graph(g, graph_name, num_parts, '/tmp/dist_graph')

    dgl.distributed.initialize("kv_ip_config.txt")
    dist_g = DistGraph(graph_name, part_config='/tmp/dist_graph/{}.json'.format(graph_name))
    check_dist_emb(dist_g, 1, g.number_of_nodes(), g.number_of_edges())
    dgl.distributed.exit_client() # this is needed since there's two test here in one process

@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
def test_split():
    g = create_random_graph(10000)
    num_parts = 4
    num_hops = 2
    partition_graph(g, 'dist_graph_test', num_parts, '/tmp/dist_graph', num_hops=num_hops, part_method='metis')

    node_mask = np.random.randint(0, 100, size=g.number_of_nodes()) > 30
    edge_mask = np.random.randint(0, 100, size=g.number_of_edges()) > 30
    selected_nodes = np.nonzero(node_mask)[0]
    selected_edges = np.nonzero(edge_mask)[0]

    # The code now collects the roles of all client processes and use the information
    # to determine how to split the workloads. Here is to simulate the multi-client
    # use case.
    def set_roles(num_clients):
        dgl.distributed.role.CUR_ROLE = 'default'
        dgl.distributed.role.GLOBAL_RANK = {i:i for i in range(num_clients)}
        dgl.distributed.role.PER_ROLE_RANK['default'] = {i:i for i in range(num_clients)}

    for i in range(num_parts):
        set_roles(num_parts)
        part_g, node_feats, edge_feats, gpb, _, _, _ = load_partition('/tmp/dist_graph/dist_graph_test.json', i)
        local_nids = F.nonzero_1d(part_g.ndata['inner_node'])
        local_nids = F.gather_row(part_g.ndata[dgl.NID], local_nids)
        nodes1 = np.intersect1d(selected_nodes, F.asnumpy(local_nids))
        nodes2 = node_split(node_mask, gpb, rank=i, force_even=False)
        assert np.all(np.sort(nodes1) == np.sort(F.asnumpy(nodes2)))
        local_nids = F.asnumpy(local_nids)
        for n in nodes1:
            assert n in local_nids

        set_roles(num_parts * 2)
        nodes3 = node_split(node_mask, gpb, rank=i * 2, force_even=False)
        nodes4 = node_split(node_mask, gpb, rank=i * 2 + 1, force_even=False)
        nodes5 = F.cat([nodes3, nodes4], 0)
        assert np.all(np.sort(nodes1) == np.sort(F.asnumpy(nodes5)))

        set_roles(num_parts)
        local_eids = F.nonzero_1d(part_g.edata['inner_edge'])
        local_eids = F.gather_row(part_g.edata[dgl.EID], local_eids)
        edges1 = np.intersect1d(selected_edges, F.asnumpy(local_eids))
        edges2 = edge_split(edge_mask, gpb, rank=i, force_even=False)
        assert np.all(np.sort(edges1) == np.sort(F.asnumpy(edges2)))
        local_eids = F.asnumpy(local_eids)
        for e in edges1:
            assert e in local_eids

        set_roles(num_parts * 2)
        edges3 = edge_split(edge_mask, gpb, rank=i * 2, force_even=False)
        edges4 = edge_split(edge_mask, gpb, rank=i * 2 + 1, force_even=False)
        edges5 = F.cat([edges3, edges4], 0)
        assert np.all(np.sort(edges1) == np.sort(F.asnumpy(edges5)))

@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
def test_split_even():
    g = create_random_graph(10000)
    num_parts = 4
    num_hops = 2
    partition_graph(g, 'dist_graph_test', num_parts, '/tmp/dist_graph', num_hops=num_hops, part_method='metis')

    node_mask = np.random.randint(0, 100, size=g.number_of_nodes()) > 30
    edge_mask = np.random.randint(0, 100, size=g.number_of_edges()) > 30
    selected_nodes = np.nonzero(node_mask)[0]
    selected_edges = np.nonzero(edge_mask)[0]
    all_nodes1 = []
    all_nodes2 = []
    all_edges1 = []
    all_edges2 = []

    # The code now collects the roles of all client processes and use the information
    # to determine how to split the workloads. Here is to simulate the multi-client
    # use case.
    def set_roles(num_clients):
        dgl.distributed.role.CUR_ROLE = 'default'
        dgl.distributed.role.GLOBAL_RANK = {i:i for i in range(num_clients)}
        dgl.distributed.role.PER_ROLE_RANK['default'] = {i:i for i in range(num_clients)}

    for i in range(num_parts):
        set_roles(num_parts)
        part_g, node_feats, edge_feats, gpb, _, _, _ = load_partition('/tmp/dist_graph/dist_graph_test.json', i)
        local_nids = F.nonzero_1d(part_g.ndata['inner_node'])
        local_nids = F.gather_row(part_g.ndata[dgl.NID], local_nids)
        nodes = node_split(node_mask, gpb, rank=i, force_even=True)
        all_nodes1.append(nodes)
        subset = np.intersect1d(F.asnumpy(nodes), F.asnumpy(local_nids))
        print('part {} get {} nodes and {} are in the partition'.format(i, len(nodes), len(subset)))

        set_roles(num_parts * 2)
        nodes1 = node_split(node_mask, gpb, rank=i * 2, force_even=True)
        nodes2 = node_split(node_mask, gpb, rank=i * 2 + 1, force_even=True)
        nodes3, _ = F.sort_1d(F.cat([nodes1, nodes2], 0))
        all_nodes2.append(nodes3)
        subset = np.intersect1d(F.asnumpy(nodes), F.asnumpy(nodes3))
        print('intersection has', len(subset))

        set_roles(num_parts)
        local_eids = F.nonzero_1d(part_g.edata['inner_edge'])
        local_eids = F.gather_row(part_g.edata[dgl.EID], local_eids)
        edges = edge_split(edge_mask, gpb, rank=i, force_even=True)
        all_edges1.append(edges)
        subset = np.intersect1d(F.asnumpy(edges), F.asnumpy(local_eids))
        print('part {} get {} edges and {} are in the partition'.format(i, len(edges), len(subset)))

        set_roles(num_parts * 2)
        edges1 = edge_split(edge_mask, gpb, rank=i * 2, force_even=True)
        edges2 = edge_split(edge_mask, gpb, rank=i * 2 + 1, force_even=True)
        edges3, _ = F.sort_1d(F.cat([edges1, edges2], 0))
        all_edges2.append(edges3)
        subset = np.intersect1d(F.asnumpy(edges), F.asnumpy(edges3))
        print('intersection has', len(subset))
    all_nodes1 = F.cat(all_nodes1, 0)
    all_edges1 = F.cat(all_edges1, 0)
    all_nodes2 = F.cat(all_nodes2, 0)
    all_edges2 = F.cat(all_edges2, 0)
    all_nodes = np.nonzero(node_mask)[0]
    all_edges = np.nonzero(edge_mask)[0]
    assert np.all(all_nodes == F.asnumpy(all_nodes1))
    assert np.all(all_edges == F.asnumpy(all_edges1))
    assert np.all(all_nodes == F.asnumpy(all_nodes2))
    assert np.all(all_edges == F.asnumpy(all_edges2))

def prepare_dist(num_servers=1):
    generate_ip_config("kv_ip_config.txt", 1, num_servers=num_servers)

if __name__ == '__main__':
    os.makedirs('/tmp/dist_graph', exist_ok=True)
    test_dist_emb_server_client()
    test_server_client()
    test_split()
    test_split_even()
    test_standalone()
    test_standalone_node_emb()