dmlc--dgl
Hongzhi (Steve), Chen
f0759a96ee
* auto-format-test * more * remove --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
1318 行
41 KiB
Python
1318 行
41 KiB
Python
import multiprocessing as mp
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import os
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import random
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import sys
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import time
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import traceback
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import unittest
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from pathlib import Path
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import backend as F
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import dgl
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import numpy as np
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import pytest
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from dgl.data import CitationGraphDataset, WN18Dataset
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from dgl.distributed import (
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DistGraph,
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DistGraphServer,
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load_partition,
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load_partition_book,
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partition_graph,
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sample_etype_neighbors,
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sample_neighbors,
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)
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from scipy import sparse as spsp
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from utils import generate_ip_config, reset_envs
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def start_server(
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rank,
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tmpdir,
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disable_shared_mem,
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graph_name,
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graph_format=["csc", "coo"],
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keep_alive=False,
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):
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g = DistGraphServer(
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rank,
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"rpc_ip_config.txt",
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1,
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1,
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tmpdir / (graph_name + ".json"),
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disable_shared_mem=disable_shared_mem,
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graph_format=graph_format,
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keep_alive=keep_alive,
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)
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g.start()
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def start_sample_client(rank, tmpdir, disable_shared_mem):
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gpb = None
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if disable_shared_mem:
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_, _, _, gpb, _, _, _ = load_partition(
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tmpdir / "test_sampling.json", rank
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)
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dgl.distributed.initialize("rpc_ip_config.txt")
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dist_graph = DistGraph("test_sampling", gpb=gpb)
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try:
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sampled_graph = sample_neighbors(
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dist_graph, [0, 10, 99, 66, 1024, 2008], 3
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)
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except Exception as e:
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print(traceback.format_exc())
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sampled_graph = None
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dgl.distributed.exit_client()
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return sampled_graph
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def start_sample_client_shuffle(
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rank,
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tmpdir,
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disable_shared_mem,
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g,
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num_servers,
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group_id,
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orig_nid,
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orig_eid,
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):
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os.environ["DGL_GROUP_ID"] = str(group_id)
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gpb = None
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if disable_shared_mem:
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_, _, _, gpb, _, _, _ = load_partition(
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tmpdir / "test_sampling.json", rank
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)
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dgl.distributed.initialize("rpc_ip_config.txt")
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dist_graph = DistGraph("test_sampling", gpb=gpb)
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sampled_graph = sample_neighbors(dist_graph, [0, 10, 99, 66, 1024, 2008], 3)
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src, dst = sampled_graph.edges()
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src = orig_nid[src]
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dst = orig_nid[dst]
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assert sampled_graph.number_of_nodes() == g.number_of_nodes()
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assert np.all(F.asnumpy(g.has_edges_between(src, dst)))
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eids = g.edge_ids(src, dst)
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eids1 = orig_eid[sampled_graph.edata[dgl.EID]]
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assert np.array_equal(F.asnumpy(eids1), F.asnumpy(eids))
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def start_find_edges_client(rank, tmpdir, disable_shared_mem, eids, etype=None):
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gpb = None
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if disable_shared_mem:
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_, _, _, gpb, _, _, _ = load_partition(
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tmpdir / "test_find_edges.json", rank
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)
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dgl.distributed.initialize("rpc_ip_config.txt")
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dist_graph = DistGraph("test_find_edges", gpb=gpb)
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try:
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u, v = dist_graph.find_edges(eids, etype=etype)
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except Exception as e:
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print(traceback.format_exc())
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u, v = None, None
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dgl.distributed.exit_client()
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return u, v
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def start_get_degrees_client(rank, tmpdir, disable_shared_mem, nids=None):
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gpb = None
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if disable_shared_mem:
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_, _, _, gpb, _, _, _ = load_partition(
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tmpdir / "test_get_degrees.json", rank
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)
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dgl.distributed.initialize("rpc_ip_config.txt")
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dist_graph = DistGraph("test_get_degrees", gpb=gpb)
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try:
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in_deg = dist_graph.in_degrees(nids)
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all_in_deg = dist_graph.in_degrees()
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out_deg = dist_graph.out_degrees(nids)
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all_out_deg = dist_graph.out_degrees()
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except Exception as e:
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print(traceback.format_exc())
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in_deg, out_deg, all_in_deg, all_out_deg = None, None, None, None
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dgl.distributed.exit_client()
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return in_deg, out_deg, all_in_deg, all_out_deg
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def check_rpc_sampling(tmpdir, num_server):
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generate_ip_config("rpc_ip_config.txt", num_server, num_server)
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g = CitationGraphDataset("cora")[0]
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print(g.idtype)
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num_parts = num_server
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num_hops = 1
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partition_graph(
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g,
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"test_sampling",
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num_parts,
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tmpdir,
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num_hops=num_hops,
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part_method="metis",
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)
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pserver_list = []
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ctx = mp.get_context("spawn")
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for i in range(num_server):
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p = ctx.Process(
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target=start_server,
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args=(i, tmpdir, num_server > 1, "test_sampling"),
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)
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p.start()
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time.sleep(1)
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pserver_list.append(p)
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sampled_graph = start_sample_client(0, tmpdir, num_server > 1)
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print("Done sampling")
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for p in pserver_list:
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p.join()
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assert p.exitcode == 0
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src, dst = sampled_graph.edges()
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assert sampled_graph.number_of_nodes() == g.number_of_nodes()
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assert np.all(F.asnumpy(g.has_edges_between(src, dst)))
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eids = g.edge_ids(src, dst)
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assert np.array_equal(
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F.asnumpy(sampled_graph.edata[dgl.EID]), F.asnumpy(eids)
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)
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def check_rpc_find_edges_shuffle(tmpdir, num_server):
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generate_ip_config("rpc_ip_config.txt", num_server, num_server)
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g = CitationGraphDataset("cora")[0]
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num_parts = num_server
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orig_nid, orig_eid = partition_graph(
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g,
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"test_find_edges",
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num_parts,
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tmpdir,
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num_hops=1,
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part_method="metis",
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return_mapping=True,
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)
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pserver_list = []
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ctx = mp.get_context("spawn")
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for i in range(num_server):
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p = ctx.Process(
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target=start_server,
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args=(i, tmpdir, num_server > 1, "test_find_edges", ["csr", "coo"]),
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)
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p.start()
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time.sleep(1)
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pserver_list.append(p)
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eids = F.tensor(np.random.randint(g.number_of_edges(), size=100))
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u, v = g.find_edges(orig_eid[eids])
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du, dv = start_find_edges_client(0, tmpdir, num_server > 1, eids)
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du = orig_nid[du]
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dv = orig_nid[dv]
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assert F.array_equal(u, du)
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assert F.array_equal(v, dv)
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def create_random_hetero(dense=False, empty=False):
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num_nodes = (
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{"n1": 210, "n2": 200, "n3": 220}
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if dense
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else {"n1": 1010, "n2": 1000, "n3": 1020}
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)
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etypes = [("n1", "r12", "n2"), ("n1", "r13", "n3"), ("n2", "r23", "n3")]
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edges = {}
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random.seed(42)
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for etype in etypes:
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src_ntype, _, dst_ntype = etype
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arr = spsp.random(
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num_nodes[src_ntype] - 10 if empty else num_nodes[src_ntype],
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num_nodes[dst_ntype] - 10 if empty else num_nodes[dst_ntype],
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density=0.1 if dense else 0.001,
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format="coo",
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random_state=100,
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)
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edges[etype] = (arr.row, arr.col)
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g = dgl.heterograph(edges, num_nodes)
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g.nodes["n1"].data["feat"] = F.ones(
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(g.number_of_nodes("n1"), 10), F.float32, F.cpu()
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)
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return g
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def check_rpc_hetero_find_edges_shuffle(tmpdir, num_server):
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generate_ip_config("rpc_ip_config.txt", num_server, num_server)
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g = create_random_hetero()
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num_parts = num_server
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orig_nid, orig_eid = partition_graph(
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g,
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"test_find_edges",
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num_parts,
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tmpdir,
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num_hops=1,
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part_method="metis",
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return_mapping=True,
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)
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pserver_list = []
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ctx = mp.get_context("spawn")
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for i in range(num_server):
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p = ctx.Process(
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target=start_server,
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args=(i, tmpdir, num_server > 1, "test_find_edges", ["csr", "coo"]),
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)
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p.start()
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time.sleep(1)
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pserver_list.append(p)
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test_etype = g.to_canonical_etype("r12")
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eids = F.tensor(np.random.randint(g.num_edges(test_etype), size=100))
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expect_except = False
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try:
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_, _ = g.find_edges(orig_eid[test_etype][eids], etype=("n1", "r12"))
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except:
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expect_except = True
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assert expect_except
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u, v = g.find_edges(orig_eid[test_etype][eids], etype="r12")
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u1, v1 = g.find_edges(orig_eid[test_etype][eids], etype=("n1", "r12", "n2"))
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assert F.array_equal(u, u1)
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assert F.array_equal(v, v1)
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du, dv = start_find_edges_client(
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0, tmpdir, num_server > 1, eids, etype="r12"
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)
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du = orig_nid["n1"][du]
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dv = orig_nid["n2"][dv]
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assert F.array_equal(u, du)
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assert F.array_equal(v, dv)
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# Wait non shared memory graph store
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@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
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@unittest.skipIf(
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dgl.backend.backend_name == "tensorflow",
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reason="Not support tensorflow for now",
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)
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@unittest.skipIf(
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dgl.backend.backend_name == "mxnet", reason="Turn off Mxnet support"
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)
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@pytest.mark.parametrize("num_server", [1, 2])
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def test_rpc_find_edges_shuffle(num_server):
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reset_envs()
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import tempfile
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os.environ["DGL_DIST_MODE"] = "distributed"
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with tempfile.TemporaryDirectory() as tmpdirname:
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check_rpc_hetero_find_edges_shuffle(Path(tmpdirname), num_server)
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check_rpc_find_edges_shuffle(Path(tmpdirname), num_server)
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def check_rpc_get_degree_shuffle(tmpdir, num_server):
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generate_ip_config("rpc_ip_config.txt", num_server, num_server)
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g = CitationGraphDataset("cora")[0]
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num_parts = num_server
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orig_nid, _ = partition_graph(
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g,
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"test_get_degrees",
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num_parts,
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tmpdir,
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num_hops=1,
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part_method="metis",
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return_mapping=True,
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)
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pserver_list = []
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ctx = mp.get_context("spawn")
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for i in range(num_server):
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p = ctx.Process(
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target=start_server,
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args=(i, tmpdir, num_server > 1, "test_get_degrees"),
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)
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p.start()
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time.sleep(1)
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pserver_list.append(p)
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nids = F.tensor(np.random.randint(g.number_of_nodes(), size=100))
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in_degs, out_degs, all_in_degs, all_out_degs = start_get_degrees_client(
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0, tmpdir, num_server > 1, nids
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)
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print("Done get_degree")
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for p in pserver_list:
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p.join()
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assert p.exitcode == 0
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print("check results")
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assert F.array_equal(g.in_degrees(orig_nid[nids]), in_degs)
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assert F.array_equal(g.in_degrees(orig_nid), all_in_degs)
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assert F.array_equal(g.out_degrees(orig_nid[nids]), out_degs)
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assert F.array_equal(g.out_degrees(orig_nid), all_out_degs)
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# Wait non shared memory graph store
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@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
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@unittest.skipIf(
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dgl.backend.backend_name == "tensorflow",
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reason="Not support tensorflow for now",
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)
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@unittest.skipIf(
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dgl.backend.backend_name == "mxnet", reason="Turn off Mxnet support"
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)
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@pytest.mark.parametrize("num_server", [1, 2])
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def test_rpc_get_degree_shuffle(num_server):
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reset_envs()
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import tempfile
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os.environ["DGL_DIST_MODE"] = "distributed"
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with tempfile.TemporaryDirectory() as tmpdirname:
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check_rpc_get_degree_shuffle(Path(tmpdirname), num_server)
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# @unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
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# @unittest.skipIf(dgl.backend.backend_name == 'tensorflow', reason='Not support tensorflow for now')
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@unittest.skip("Only support partition with shuffle")
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def test_rpc_sampling():
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reset_envs()
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import tempfile
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os.environ["DGL_DIST_MODE"] = "distributed"
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with tempfile.TemporaryDirectory() as tmpdirname:
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check_rpc_sampling(Path(tmpdirname), 2)
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def check_rpc_sampling_shuffle(tmpdir, num_server, num_groups=1):
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generate_ip_config("rpc_ip_config.txt", num_server, num_server)
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g = CitationGraphDataset("cora")[0]
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num_parts = num_server
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num_hops = 1
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orig_nids, orig_eids = partition_graph(
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g,
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"test_sampling",
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num_parts,
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tmpdir,
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num_hops=num_hops,
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part_method="metis",
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return_mapping=True,
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)
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pserver_list = []
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ctx = mp.get_context("spawn")
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keep_alive = num_groups > 1
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for i in range(num_server):
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p = ctx.Process(
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target=start_server,
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args=(
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i,
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tmpdir,
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num_server > 1,
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"test_sampling",
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["csc", "coo"],
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keep_alive,
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),
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)
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p.start()
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time.sleep(1)
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pserver_list.append(p)
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pclient_list = []
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num_clients = 1
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for client_id in range(num_clients):
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for group_id in range(num_groups):
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p = ctx.Process(
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target=start_sample_client_shuffle,
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args=(
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client_id,
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tmpdir,
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num_server > 1,
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g,
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num_server,
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group_id,
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orig_nids,
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orig_eids,
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),
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)
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p.start()
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time.sleep(1) # avoid race condition when instantiating DistGraph
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pclient_list.append(p)
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for p in pclient_list:
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p.join()
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assert p.exitcode == 0
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if keep_alive:
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for p in pserver_list:
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assert p.is_alive()
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# force shutdown server
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dgl.distributed.shutdown_servers("rpc_ip_config.txt", 1)
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for p in pserver_list:
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p.join()
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assert p.exitcode == 0
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|
|
|
|
def start_hetero_sample_client(rank, tmpdir, disable_shared_mem, nodes):
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gpb = None
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if disable_shared_mem:
|
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_, _, _, gpb, _, _, _ = load_partition(
|
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tmpdir / "test_sampling.json", rank
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)
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dgl.distributed.initialize("rpc_ip_config.txt")
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dist_graph = DistGraph("test_sampling", gpb=gpb)
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assert "feat" in dist_graph.nodes["n1"].data
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assert "feat" not in dist_graph.nodes["n2"].data
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assert "feat" not in dist_graph.nodes["n3"].data
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if gpb is None:
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gpb = dist_graph.get_partition_book()
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try:
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sampled_graph = sample_neighbors(dist_graph, nodes, 3)
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block = dgl.to_block(sampled_graph, nodes)
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block.edata[dgl.EID] = sampled_graph.edata[dgl.EID]
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except Exception as e:
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print(traceback.format_exc())
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block = None
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dgl.distributed.exit_client()
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return block, gpb
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|
|
|
|
def start_hetero_etype_sample_client(
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rank,
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tmpdir,
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disable_shared_mem,
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fanout=3,
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nodes={"n3": [0, 10, 99, 66, 124, 208]},
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etype_sorted=False,
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):
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gpb = None
|
|
if disable_shared_mem:
|
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_, _, _, gpb, _, _, _ = load_partition(
|
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tmpdir / "test_sampling.json", rank
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)
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dgl.distributed.initialize("rpc_ip_config.txt")
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dist_graph = DistGraph("test_sampling", gpb=gpb)
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assert "feat" in dist_graph.nodes["n1"].data
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assert "feat" not in dist_graph.nodes["n2"].data
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assert "feat" not in dist_graph.nodes["n3"].data
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|
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if dist_graph.local_partition is not None:
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# Check whether etypes are sorted in dist_graph
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local_g = dist_graph.local_partition
|
|
local_nids = np.arange(local_g.num_nodes())
|
|
for lnid in local_nids:
|
|
leids = local_g.in_edges(lnid, form="eid")
|
|
letids = F.asnumpy(local_g.edata[dgl.ETYPE][leids])
|
|
_, idices = np.unique(letids, return_index=True)
|
|
assert np.all(idices[:-1] <= idices[1:])
|
|
|
|
if gpb is None:
|
|
gpb = dist_graph.get_partition_book()
|
|
try:
|
|
sampled_graph = sample_etype_neighbors(
|
|
dist_graph, nodes, fanout, etype_sorted=etype_sorted
|
|
)
|
|
block = dgl.to_block(sampled_graph, nodes)
|
|
block.edata[dgl.EID] = sampled_graph.edata[dgl.EID]
|
|
except Exception as e:
|
|
print(traceback.format_exc())
|
|
block = None
|
|
dgl.distributed.exit_client()
|
|
return block, gpb
|
|
|
|
|
|
def check_rpc_hetero_sampling_shuffle(tmpdir, num_server):
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_hetero()
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nid_map, orig_eid_map = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
block, gpb = start_hetero_sample_client(
|
|
0, tmpdir, num_server > 1, nodes={"n3": [0, 10, 99, 66, 124, 208]}
|
|
)
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
for c_etype in block.canonical_etypes:
|
|
src_type, etype, dst_type = c_etype
|
|
src, dst = block.edges(etype=etype)
|
|
# These are global Ids after shuffling.
|
|
shuffled_src = F.gather_row(block.srcnodes[src_type].data[dgl.NID], src)
|
|
shuffled_dst = F.gather_row(block.dstnodes[dst_type].data[dgl.NID], dst)
|
|
shuffled_eid = block.edges[etype].data[dgl.EID]
|
|
|
|
orig_src = F.asnumpy(F.gather_row(orig_nid_map[src_type], shuffled_src))
|
|
orig_dst = F.asnumpy(F.gather_row(orig_nid_map[dst_type], shuffled_dst))
|
|
orig_eid = F.asnumpy(F.gather_row(orig_eid_map[c_etype], shuffled_eid))
|
|
|
|
# Check the node Ids and edge Ids.
|
|
orig_src1, orig_dst1 = g.find_edges(orig_eid, etype=etype)
|
|
assert np.all(F.asnumpy(orig_src1) == orig_src)
|
|
assert np.all(F.asnumpy(orig_dst1) == orig_dst)
|
|
|
|
|
|
def get_degrees(g, nids, ntype):
|
|
deg = F.zeros((len(nids),), dtype=F.int64)
|
|
for srctype, etype, dsttype in g.canonical_etypes:
|
|
if srctype == ntype:
|
|
deg += g.out_degrees(u=nids, etype=etype)
|
|
elif dsttype == ntype:
|
|
deg += g.in_degrees(v=nids, etype=etype)
|
|
return deg
|
|
|
|
|
|
def check_rpc_hetero_sampling_empty_shuffle(tmpdir, num_server):
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_hetero(empty=True)
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nids, _ = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
deg = get_degrees(g, orig_nids["n3"], "n3")
|
|
empty_nids = F.nonzero_1d(deg == 0)
|
|
block, gpb = start_hetero_sample_client(
|
|
0, tmpdir, num_server > 1, nodes={"n3": empty_nids}
|
|
)
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
assert block.number_of_edges() == 0
|
|
assert len(block.etypes) == len(g.etypes)
|
|
|
|
|
|
def check_rpc_hetero_etype_sampling_shuffle(
|
|
tmpdir, num_server, graph_formats=None
|
|
):
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_hetero(dense=True)
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nid_map, orig_eid_map = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
graph_formats=graph_formats,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling", ["csc", "coo"]),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
fanout = {etype: 3 for etype in g.canonical_etypes}
|
|
etype_sorted = False
|
|
if graph_formats is not None:
|
|
etype_sorted = "csc" in graph_formats or "csr" in graph_formats
|
|
block, gpb = start_hetero_etype_sample_client(
|
|
0,
|
|
tmpdir,
|
|
num_server > 1,
|
|
fanout,
|
|
nodes={"n3": [0, 10, 99, 66, 124, 208]},
|
|
etype_sorted=etype_sorted,
|
|
)
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
src, dst = block.edges(etype=("n1", "r13", "n3"))
|
|
assert len(src) == 18
|
|
src, dst = block.edges(etype=("n2", "r23", "n3"))
|
|
assert len(src) == 18
|
|
|
|
for c_etype in block.canonical_etypes:
|
|
src_type, etype, dst_type = c_etype
|
|
src, dst = block.edges(etype=etype)
|
|
# These are global Ids after shuffling.
|
|
shuffled_src = F.gather_row(block.srcnodes[src_type].data[dgl.NID], src)
|
|
shuffled_dst = F.gather_row(block.dstnodes[dst_type].data[dgl.NID], dst)
|
|
shuffled_eid = block.edges[etype].data[dgl.EID]
|
|
|
|
orig_src = F.asnumpy(F.gather_row(orig_nid_map[src_type], shuffled_src))
|
|
orig_dst = F.asnumpy(F.gather_row(orig_nid_map[dst_type], shuffled_dst))
|
|
orig_eid = F.asnumpy(F.gather_row(orig_eid_map[c_etype], shuffled_eid))
|
|
|
|
# Check the node Ids and edge Ids.
|
|
orig_src1, orig_dst1 = g.find_edges(orig_eid, etype=etype)
|
|
assert np.all(F.asnumpy(orig_src1) == orig_src)
|
|
assert np.all(F.asnumpy(orig_dst1) == orig_dst)
|
|
|
|
|
|
def check_rpc_hetero_etype_sampling_empty_shuffle(tmpdir, num_server):
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_hetero(dense=True, empty=True)
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nids, _ = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
fanout = 3
|
|
deg = get_degrees(g, orig_nids["n3"], "n3")
|
|
empty_nids = F.nonzero_1d(deg == 0)
|
|
block, gpb = start_hetero_etype_sample_client(
|
|
0, tmpdir, num_server > 1, fanout, nodes={"n3": empty_nids}
|
|
)
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
assert block.number_of_edges() == 0
|
|
assert len(block.etypes) == len(g.etypes)
|
|
|
|
|
|
def create_random_bipartite():
|
|
g = dgl.rand_bipartite("user", "buys", "game", 500, 1000, 1000)
|
|
g.nodes["user"].data["feat"] = F.ones(
|
|
(g.num_nodes("user"), 10), F.float32, F.cpu()
|
|
)
|
|
g.nodes["game"].data["feat"] = F.ones(
|
|
(g.num_nodes("game"), 10), F.float32, F.cpu()
|
|
)
|
|
return g
|
|
|
|
|
|
def start_bipartite_sample_client(rank, tmpdir, disable_shared_mem, nodes):
|
|
gpb = None
|
|
if disable_shared_mem:
|
|
_, _, _, gpb, _, _, _ = load_partition(
|
|
tmpdir / "test_sampling.json", rank
|
|
)
|
|
dgl.distributed.initialize("rpc_ip_config.txt")
|
|
dist_graph = DistGraph("test_sampling", gpb=gpb)
|
|
assert "feat" in dist_graph.nodes["user"].data
|
|
assert "feat" in dist_graph.nodes["game"].data
|
|
if gpb is None:
|
|
gpb = dist_graph.get_partition_book()
|
|
sampled_graph = sample_neighbors(dist_graph, nodes, 3)
|
|
block = dgl.to_block(sampled_graph, nodes)
|
|
if sampled_graph.num_edges() > 0:
|
|
block.edata[dgl.EID] = sampled_graph.edata[dgl.EID]
|
|
dgl.distributed.exit_client()
|
|
return block, gpb
|
|
|
|
|
|
def start_bipartite_etype_sample_client(
|
|
rank, tmpdir, disable_shared_mem, fanout=3, nodes={}
|
|
):
|
|
gpb = None
|
|
if disable_shared_mem:
|
|
_, _, _, gpb, _, _, _ = load_partition(
|
|
tmpdir / "test_sampling.json", rank
|
|
)
|
|
dgl.distributed.initialize("rpc_ip_config.txt")
|
|
dist_graph = DistGraph("test_sampling", gpb=gpb)
|
|
assert "feat" in dist_graph.nodes["user"].data
|
|
assert "feat" in dist_graph.nodes["game"].data
|
|
|
|
if dist_graph.local_partition is not None:
|
|
# Check whether etypes are sorted in dist_graph
|
|
local_g = dist_graph.local_partition
|
|
local_nids = np.arange(local_g.num_nodes())
|
|
for lnid in local_nids:
|
|
leids = local_g.in_edges(lnid, form="eid")
|
|
letids = F.asnumpy(local_g.edata[dgl.ETYPE][leids])
|
|
_, idices = np.unique(letids, return_index=True)
|
|
assert np.all(idices[:-1] <= idices[1:])
|
|
|
|
if gpb is None:
|
|
gpb = dist_graph.get_partition_book()
|
|
sampled_graph = sample_etype_neighbors(dist_graph, nodes, fanout)
|
|
block = dgl.to_block(sampled_graph, nodes)
|
|
if sampled_graph.num_edges() > 0:
|
|
block.edata[dgl.EID] = sampled_graph.edata[dgl.EID]
|
|
dgl.distributed.exit_client()
|
|
return block, gpb
|
|
|
|
|
|
def check_rpc_bipartite_sampling_empty(tmpdir, num_server):
|
|
"""sample on bipartite via sample_neighbors() which yields empty sample results"""
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_bipartite()
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nids, _ = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
deg = get_degrees(g, orig_nids["game"], "game")
|
|
empty_nids = F.nonzero_1d(deg == 0)
|
|
block, _ = start_bipartite_sample_client(
|
|
0, tmpdir, num_server > 1, nodes={"game": empty_nids, "user": [1]}
|
|
)
|
|
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
assert block.number_of_edges() == 0
|
|
assert len(block.etypes) == len(g.etypes)
|
|
|
|
|
|
def check_rpc_bipartite_sampling_shuffle(tmpdir, num_server):
|
|
"""sample on bipartite via sample_neighbors() which yields non-empty sample results"""
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_bipartite()
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nid_map, orig_eid_map = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
deg = get_degrees(g, orig_nid_map["game"], "game")
|
|
nids = F.nonzero_1d(deg > 0)
|
|
block, gpb = start_bipartite_sample_client(
|
|
0, tmpdir, num_server > 1, nodes={"game": nids, "user": [0]}
|
|
)
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
for c_etype in block.canonical_etypes:
|
|
src_type, etype, dst_type = c_etype
|
|
src, dst = block.edges(etype=etype)
|
|
# These are global Ids after shuffling.
|
|
shuffled_src = F.gather_row(block.srcnodes[src_type].data[dgl.NID], src)
|
|
shuffled_dst = F.gather_row(block.dstnodes[dst_type].data[dgl.NID], dst)
|
|
shuffled_eid = block.edges[etype].data[dgl.EID]
|
|
|
|
orig_src = F.asnumpy(F.gather_row(orig_nid_map[src_type], shuffled_src))
|
|
orig_dst = F.asnumpy(F.gather_row(orig_nid_map[dst_type], shuffled_dst))
|
|
orig_eid = F.asnumpy(F.gather_row(orig_eid_map[c_etype], shuffled_eid))
|
|
|
|
# Check the node Ids and edge Ids.
|
|
orig_src1, orig_dst1 = g.find_edges(orig_eid, etype=etype)
|
|
assert np.all(F.asnumpy(orig_src1) == orig_src)
|
|
assert np.all(F.asnumpy(orig_dst1) == orig_dst)
|
|
|
|
|
|
def check_rpc_bipartite_etype_sampling_empty(tmpdir, num_server):
|
|
"""sample on bipartite via sample_etype_neighbors() which yields empty sample results"""
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_bipartite()
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nids, _ = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
deg = get_degrees(g, orig_nids["game"], "game")
|
|
empty_nids = F.nonzero_1d(deg == 0)
|
|
block, gpb = start_bipartite_etype_sample_client(
|
|
0, tmpdir, num_server > 1, nodes={"game": empty_nids, "user": [1]}
|
|
)
|
|
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
assert block is not None
|
|
assert block.number_of_edges() == 0
|
|
assert len(block.etypes) == len(g.etypes)
|
|
|
|
|
|
def check_rpc_bipartite_etype_sampling_shuffle(tmpdir, num_server):
|
|
"""sample on bipartite via sample_etype_neighbors() which yields non-empty sample results"""
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = create_random_bipartite()
|
|
num_parts = num_server
|
|
num_hops = 1
|
|
|
|
orig_nid_map, orig_eid_map = partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_sampling"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
fanout = 3
|
|
deg = get_degrees(g, orig_nid_map["game"], "game")
|
|
nids = F.nonzero_1d(deg > 0)
|
|
block, gpb = start_bipartite_etype_sample_client(
|
|
0, tmpdir, num_server > 1, fanout, nodes={"game": nids, "user": [0]}
|
|
)
|
|
print("Done sampling")
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
for c_etype in block.canonical_etypes:
|
|
src_type, etype, dst_type = c_etype
|
|
src, dst = block.edges(etype=etype)
|
|
# These are global Ids after shuffling.
|
|
shuffled_src = F.gather_row(block.srcnodes[src_type].data[dgl.NID], src)
|
|
shuffled_dst = F.gather_row(block.dstnodes[dst_type].data[dgl.NID], dst)
|
|
shuffled_eid = block.edges[etype].data[dgl.EID]
|
|
|
|
orig_src = F.asnumpy(F.gather_row(orig_nid_map[src_type], shuffled_src))
|
|
orig_dst = F.asnumpy(F.gather_row(orig_nid_map[dst_type], shuffled_dst))
|
|
orig_eid = F.asnumpy(F.gather_row(orig_eid_map[c_etype], shuffled_eid))
|
|
|
|
# Check the node Ids and edge Ids.
|
|
orig_src1, orig_dst1 = g.find_edges(orig_eid, etype=etype)
|
|
assert np.all(F.asnumpy(orig_src1) == orig_src)
|
|
assert np.all(F.asnumpy(orig_dst1) == orig_dst)
|
|
|
|
|
|
# Wait non shared memory graph store
|
|
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
|
|
@unittest.skipIf(
|
|
dgl.backend.backend_name == "tensorflow",
|
|
reason="Not support tensorflow for now",
|
|
)
|
|
@unittest.skipIf(
|
|
dgl.backend.backend_name == "mxnet", reason="Turn off Mxnet support"
|
|
)
|
|
@pytest.mark.parametrize("num_server", [1, 2])
|
|
def test_rpc_sampling_shuffle(num_server):
|
|
reset_envs()
|
|
import tempfile
|
|
|
|
os.environ["DGL_DIST_MODE"] = "distributed"
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
check_rpc_sampling_shuffle(Path(tmpdirname), num_server)
|
|
# [TODO][Rhett] Tests for multiple groups may fail sometimes and
|
|
# root cause is unknown. Let's disable them for now.
|
|
# check_rpc_sampling_shuffle(Path(tmpdirname), num_server, num_groups=2)
|
|
check_rpc_hetero_sampling_shuffle(Path(tmpdirname), num_server)
|
|
check_rpc_hetero_sampling_empty_shuffle(Path(tmpdirname), num_server)
|
|
check_rpc_hetero_etype_sampling_shuffle(Path(tmpdirname), num_server)
|
|
check_rpc_hetero_etype_sampling_shuffle(
|
|
Path(tmpdirname), num_server, ["csc"]
|
|
)
|
|
check_rpc_hetero_etype_sampling_shuffle(
|
|
Path(tmpdirname), num_server, ["csr"]
|
|
)
|
|
check_rpc_hetero_etype_sampling_shuffle(
|
|
Path(tmpdirname), num_server, ["csc", "coo"]
|
|
)
|
|
check_rpc_hetero_etype_sampling_empty_shuffle(
|
|
Path(tmpdirname), num_server
|
|
)
|
|
check_rpc_bipartite_sampling_empty(Path(tmpdirname), num_server)
|
|
check_rpc_bipartite_sampling_shuffle(Path(tmpdirname), num_server)
|
|
check_rpc_bipartite_etype_sampling_empty(Path(tmpdirname), num_server)
|
|
check_rpc_bipartite_etype_sampling_shuffle(Path(tmpdirname), num_server)
|
|
|
|
|
|
def check_standalone_sampling(tmpdir):
|
|
g = CitationGraphDataset("cora")[0]
|
|
prob = np.maximum(np.random.randn(g.num_edges()), 0)
|
|
mask = prob > 0
|
|
g.edata["prob"] = F.tensor(prob)
|
|
g.edata["mask"] = F.tensor(mask)
|
|
num_parts = 1
|
|
num_hops = 1
|
|
partition_graph(
|
|
g,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
)
|
|
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
dgl.distributed.initialize("rpc_ip_config.txt")
|
|
dist_graph = DistGraph(
|
|
"test_sampling", part_config=tmpdir / "test_sampling.json"
|
|
)
|
|
sampled_graph = sample_neighbors(dist_graph, [0, 10, 99, 66, 1024, 2008], 3)
|
|
|
|
src, dst = sampled_graph.edges()
|
|
assert sampled_graph.number_of_nodes() == g.number_of_nodes()
|
|
assert np.all(F.asnumpy(g.has_edges_between(src, dst)))
|
|
eids = g.edge_ids(src, dst)
|
|
assert np.array_equal(
|
|
F.asnumpy(sampled_graph.edata[dgl.EID]), F.asnumpy(eids)
|
|
)
|
|
|
|
sampled_graph = sample_neighbors(
|
|
dist_graph, [0, 10, 99, 66, 1024, 2008], 3, prob="mask"
|
|
)
|
|
eid = F.asnumpy(sampled_graph.edata[dgl.EID])
|
|
assert mask[eid].all()
|
|
|
|
sampled_graph = sample_neighbors(
|
|
dist_graph, [0, 10, 99, 66, 1024, 2008], 3, prob="prob"
|
|
)
|
|
eid = F.asnumpy(sampled_graph.edata[dgl.EID])
|
|
assert (prob[eid] > 0).all()
|
|
dgl.distributed.exit_client()
|
|
|
|
|
|
def check_standalone_etype_sampling(tmpdir):
|
|
hg = CitationGraphDataset("cora")[0]
|
|
prob = np.maximum(np.random.randn(hg.num_edges()), 0)
|
|
mask = prob > 0
|
|
hg.edata["prob"] = F.tensor(prob)
|
|
hg.edata["mask"] = F.tensor(mask)
|
|
num_parts = 1
|
|
num_hops = 1
|
|
|
|
partition_graph(
|
|
hg,
|
|
"test_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
)
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
dgl.distributed.initialize("rpc_ip_config.txt")
|
|
dist_graph = DistGraph(
|
|
"test_sampling", part_config=tmpdir / "test_sampling.json"
|
|
)
|
|
sampled_graph = sample_etype_neighbors(dist_graph, [0, 10, 99, 66, 1023], 3)
|
|
|
|
src, dst = sampled_graph.edges()
|
|
assert sampled_graph.number_of_nodes() == hg.number_of_nodes()
|
|
assert np.all(F.asnumpy(hg.has_edges_between(src, dst)))
|
|
eids = hg.edge_ids(src, dst)
|
|
assert np.array_equal(
|
|
F.asnumpy(sampled_graph.edata[dgl.EID]), F.asnumpy(eids)
|
|
)
|
|
|
|
sampled_graph = sample_etype_neighbors(
|
|
dist_graph, [0, 10, 99, 66, 1023], 3, prob="mask"
|
|
)
|
|
eid = F.asnumpy(sampled_graph.edata[dgl.EID])
|
|
assert mask[eid].all()
|
|
|
|
sampled_graph = sample_etype_neighbors(
|
|
dist_graph, [0, 10, 99, 66, 1023], 3, prob="prob"
|
|
)
|
|
eid = F.asnumpy(sampled_graph.edata[dgl.EID])
|
|
assert (prob[eid] > 0).all()
|
|
dgl.distributed.exit_client()
|
|
|
|
|
|
def check_standalone_etype_sampling_heterograph(tmpdir):
|
|
hg = CitationGraphDataset("cora")[0]
|
|
num_parts = 1
|
|
num_hops = 1
|
|
src, dst = hg.edges()
|
|
new_hg = dgl.heterograph(
|
|
{
|
|
("paper", "cite", "paper"): (src, dst),
|
|
("paper", "cite-by", "paper"): (dst, src),
|
|
},
|
|
{"paper": hg.number_of_nodes()},
|
|
)
|
|
partition_graph(
|
|
new_hg,
|
|
"test_hetero_sampling",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=num_hops,
|
|
part_method="metis",
|
|
)
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
dgl.distributed.initialize("rpc_ip_config.txt")
|
|
dist_graph = DistGraph(
|
|
"test_hetero_sampling", part_config=tmpdir / "test_hetero_sampling.json"
|
|
)
|
|
sampled_graph = sample_etype_neighbors(
|
|
dist_graph, [0, 1, 2, 10, 99, 66, 1023, 1024, 2700, 2701], 1
|
|
)
|
|
src, dst = sampled_graph.edges(etype=("paper", "cite", "paper"))
|
|
assert len(src) == 10
|
|
src, dst = sampled_graph.edges(etype=("paper", "cite-by", "paper"))
|
|
assert len(src) == 10
|
|
assert sampled_graph.number_of_nodes() == new_hg.number_of_nodes()
|
|
dgl.distributed.exit_client()
|
|
|
|
|
|
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
|
|
@unittest.skipIf(
|
|
dgl.backend.backend_name == "tensorflow",
|
|
reason="Not support tensorflow for now",
|
|
)
|
|
def test_standalone_sampling():
|
|
reset_envs()
|
|
import tempfile
|
|
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
check_standalone_sampling(Path(tmpdirname))
|
|
|
|
|
|
def start_in_subgraph_client(rank, tmpdir, disable_shared_mem, nodes):
|
|
gpb = None
|
|
dgl.distributed.initialize("rpc_ip_config.txt")
|
|
if disable_shared_mem:
|
|
_, _, _, gpb, _, _, _ = load_partition(
|
|
tmpdir / "test_in_subgraph.json", rank
|
|
)
|
|
dist_graph = DistGraph("test_in_subgraph", gpb=gpb)
|
|
try:
|
|
sampled_graph = dgl.distributed.in_subgraph(dist_graph, nodes)
|
|
except Exception as e:
|
|
print(traceback.format_exc())
|
|
sampled_graph = None
|
|
dgl.distributed.exit_client()
|
|
return sampled_graph
|
|
|
|
|
|
def check_rpc_in_subgraph_shuffle(tmpdir, num_server):
|
|
generate_ip_config("rpc_ip_config.txt", num_server, num_server)
|
|
|
|
g = CitationGraphDataset("cora")[0]
|
|
num_parts = num_server
|
|
|
|
orig_nid, orig_eid = partition_graph(
|
|
g,
|
|
"test_in_subgraph",
|
|
num_parts,
|
|
tmpdir,
|
|
num_hops=1,
|
|
part_method="metis",
|
|
return_mapping=True,
|
|
)
|
|
|
|
pserver_list = []
|
|
ctx = mp.get_context("spawn")
|
|
for i in range(num_server):
|
|
p = ctx.Process(
|
|
target=start_server,
|
|
args=(i, tmpdir, num_server > 1, "test_in_subgraph"),
|
|
)
|
|
p.start()
|
|
time.sleep(1)
|
|
pserver_list.append(p)
|
|
|
|
nodes = [0, 10, 99, 66, 1024, 2008]
|
|
sampled_graph = start_in_subgraph_client(0, tmpdir, num_server > 1, nodes)
|
|
for p in pserver_list:
|
|
p.join()
|
|
assert p.exitcode == 0
|
|
|
|
src, dst = sampled_graph.edges()
|
|
src = orig_nid[src]
|
|
dst = orig_nid[dst]
|
|
assert sampled_graph.number_of_nodes() == g.number_of_nodes()
|
|
assert np.all(F.asnumpy(g.has_edges_between(src, dst)))
|
|
|
|
subg1 = dgl.in_subgraph(g, orig_nid[nodes])
|
|
src1, dst1 = subg1.edges()
|
|
assert np.all(np.sort(F.asnumpy(src)) == np.sort(F.asnumpy(src1)))
|
|
assert np.all(np.sort(F.asnumpy(dst)) == np.sort(F.asnumpy(dst1)))
|
|
eids = g.edge_ids(src, dst)
|
|
eids1 = orig_eid[sampled_graph.edata[dgl.EID]]
|
|
assert np.array_equal(F.asnumpy(eids1), F.asnumpy(eids))
|
|
|
|
|
|
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
|
|
@unittest.skipIf(
|
|
dgl.backend.backend_name == "tensorflow",
|
|
reason="Not support tensorflow for now",
|
|
)
|
|
def test_rpc_in_subgraph():
|
|
reset_envs()
|
|
import tempfile
|
|
|
|
os.environ["DGL_DIST_MODE"] = "distributed"
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
check_rpc_in_subgraph_shuffle(Path(tmpdirname), 2)
|
|
|
|
|
|
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
|
|
@unittest.skipIf(
|
|
dgl.backend.backend_name == "tensorflow",
|
|
reason="Not support tensorflow for now",
|
|
)
|
|
@unittest.skipIf(
|
|
dgl.backend.backend_name == "mxnet", reason="Turn off Mxnet support"
|
|
)
|
|
def test_standalone_etype_sampling():
|
|
reset_envs()
|
|
import tempfile
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
check_standalone_etype_sampling_heterograph(Path(tmpdirname))
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
check_standalone_etype_sampling(Path(tmpdirname))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import tempfile
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
check_standalone_etype_sampling_heterograph(Path(tmpdirname))
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
os.environ["DGL_DIST_MODE"] = "standalone"
|
|
check_standalone_etype_sampling(Path(tmpdirname))
|
|
check_standalone_sampling(Path(tmpdirname))
|
|
os.environ["DGL_DIST_MODE"] = "distributed"
|
|
check_rpc_sampling(Path(tmpdirname), 2)
|
|
check_rpc_sampling(Path(tmpdirname), 1)
|
|
check_rpc_get_degree_shuffle(Path(tmpdirname), 1)
|
|
check_rpc_get_degree_shuffle(Path(tmpdirname), 2)
|
|
check_rpc_find_edges_shuffle(Path(tmpdirname), 2)
|
|
check_rpc_find_edges_shuffle(Path(tmpdirname), 1)
|
|
check_rpc_hetero_find_edges_shuffle(Path(tmpdirname), 1)
|
|
check_rpc_hetero_find_edges_shuffle(Path(tmpdirname), 2)
|
|
check_rpc_in_subgraph_shuffle(Path(tmpdirname), 2)
|
|
check_rpc_sampling_shuffle(Path(tmpdirname), 1)
|
|
check_rpc_hetero_sampling_shuffle(Path(tmpdirname), 1)
|
|
check_rpc_hetero_sampling_shuffle(Path(tmpdirname), 2)
|
|
check_rpc_hetero_sampling_empty_shuffle(Path(tmpdirname), 1)
|
|
check_rpc_hetero_etype_sampling_shuffle(Path(tmpdirname), 1)
|
|
check_rpc_hetero_etype_sampling_shuffle(Path(tmpdirname), 2)
|
|
check_rpc_hetero_etype_sampling_empty_shuffle(Path(tmpdirname), 1)
|