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Rhett Ying 9731e02325 [Dist] enable to chunk node/edge data into arbitrary number of chunks (#4930)
* [Dist] enable to chunk node/edge data into arbitrary number of chunks

* [Dist] enable to split node/edge data into arbitrary parts

* refine code

* Format boolean to uint8 forcely to avoid dist.scatter failure

* convert boolean to int8 before scatter and revert it after scatter

* refine code

* fix test

* refine code

* move test utilities into utils.py

* update comment

* fix empty data

* update

* update

* fix empty data issue

* release unnecessary mem

* release unnecessary mem

* release unnecessary mem

* release unnecessary mem

* release unnecessary mem

* remove unnecessary shuffle data

* separate array_split into standalone utility

* add example

Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
2022-12-15 10:01:39 +08:00

239 行
9.1 KiB
Python

import argparse
import json
import os
import sys
import tempfile
import unittest
import dgl
import numpy as np
import torch
from dgl.data.utils import load_graphs, load_tensors
from utils import create_chunked_dataset
from partition_algo.base import load_partition_meta
"""
TODO: skipping this test case since the dependency, mpirun, is
not yet configured in the CI framework.
"""
@unittest.skipIf(True, reason="mpi is not available in CI test framework.")
def test_parmetis_preprocessing():
with tempfile.TemporaryDirectory() as root_dir:
num_chunks = 2
g = create_chunked_dataset(root_dir, num_chunks)
# Trigger ParMETIS pre-processing here.
schema_path = os.path.join(root_dir, 'chunked-data/metadata.json')
results_dir = os.path.join(root_dir, 'parmetis-data')
os.system(
f'mpirun -np 2 python3 tools/distpartitioning/parmetis_preprocess.py '
f'--schema {schema_path} --output {results_dir}'
)
# Now add all the tests and check whether the test has passed or failed.
# Read parmetis_nfiles and ensure all files are present.
parmetis_data_dir = os.path.join(root_dir, 'parmetis-data')
assert os.path.isdir(parmetis_data_dir)
parmetis_nodes_file = os.path.join(
parmetis_data_dir, 'parmetis_nfiles.txt'
)
assert os.path.isfile(parmetis_nodes_file)
# `parmetis_nfiles.txt` should have each line in the following format.
# <filename> <global_id_start> <global_id_end>
with open(parmetis_nodes_file, 'r') as nodes_metafile:
lines = nodes_metafile.readlines()
total_node_count = 0
for line in lines:
tokens = line.split(" ")
assert len(tokens) == 3
assert os.path.isfile(tokens[0])
assert int(tokens[1]) == total_node_count
# check contents of each of the nodes files here
with open(tokens[0], 'r') as nodes_file:
node_lines = nodes_file.readlines()
for line in node_lines:
val = line.split(" ")
# <ntype_id> <weight_list> <mask_list> <type_node_id>
assert len(val) == 8
node_count = len(node_lines)
total_node_count += node_count
assert int(tokens[2]) == total_node_count
# Meta_data object.
output_dir = os.path.join(root_dir, 'chunked-data')
json_file = os.path.join(output_dir, 'metadata.json')
assert os.path.isfile(json_file)
with open(json_file, 'rb') as f:
meta_data = json.load(f)
# Count the total no. of nodes.
true_node_count = 0
num_nodes_per_chunk = meta_data['num_nodes_per_chunk']
for i in range(len(num_nodes_per_chunk)):
node_per_part = num_nodes_per_chunk[i]
for j in range(len(node_per_part)):
true_node_count += node_per_part[j]
assert total_node_count == true_node_count
# Read parmetis_efiles and ensure all files are present.
# This file contains a list of filenames.
parmetis_edges_file = os.path.join(
parmetis_data_dir, 'parmetis_efiles.txt'
)
assert os.path.isfile(parmetis_edges_file)
with open(parmetis_edges_file, 'r') as edges_metafile:
lines = edges_metafile.readlines()
total_edge_count = 0
for line in lines:
edges_filename = line.strip()
assert os.path.isfile(edges_filename)
with open(edges_filename, 'r') as edges_file:
edge_lines = edges_file.readlines()
total_edge_count += len(edge_lines)
for line in edge_lines:
val = line.split(" ")
assert len(val) == 2
# Count the total no. of edges
true_edge_count = 0
num_edges_per_chunk = meta_data['num_edges_per_chunk']
for i in range(len(num_edges_per_chunk)):
edges_per_part = num_edges_per_chunk[i]
for j in range(len(edges_per_part)):
true_edge_count += edges_per_part[j]
assert true_edge_count == total_edge_count
def test_parmetis_postprocessing():
with tempfile.TemporaryDirectory() as root_dir:
num_chunks = 2
g = create_chunked_dataset(root_dir, num_chunks)
num_nodes = g.number_of_nodes()
num_institutions = g.number_of_nodes('institution')
num_authors = g.number_of_nodes('author')
num_papers = g.number_of_nodes('paper')
# Generate random parmetis partition ids for the nodes in the graph.
# Replace this code with actual ParMETIS executable when it is ready
output_dir = os.path.join(root_dir, 'chunked-data')
parmetis_file = os.path.join(output_dir, 'parmetis_output.txt')
node_ids = np.arange(num_nodes)
partition_ids = np.random.randint(0, 2, (num_nodes,))
parmetis_output = np.column_stack([node_ids, partition_ids])
# Create parmetis output, this is mimicking running actual parmetis.
with open(parmetis_file, 'w') as f:
np.savetxt(f, parmetis_output)
# Check the post processing script here.
results_dir = os.path.join(output_dir, 'partitions_dir')
json_file = os.path.join(output_dir, 'metadata.json')
print(json_file)
print(results_dir)
print(parmetis_file)
os.system(
f'python3 tools/distpartitioning/parmetis_postprocess.py '
f'--schema_file {json_file} '
f'--parmetis_output_file {parmetis_file} '
f'--partitions_dir {results_dir}'
)
ntype_count = {
'author': num_authors,
'paper': num_papers,
'institution': num_institutions,
}
for ntype_name in ['author', 'paper', 'institution']:
fname = os.path.join(results_dir, f'{ntype_name}.txt')
print(fname)
assert os.path.isfile(fname)
# Load and check the partition ids in this file.
part_ids = np.loadtxt(fname)
assert part_ids.shape[0] == ntype_count[ntype_name]
assert np.min(part_ids) == 0
assert np.max(part_ids) == 1
# check partition meta file
part_meta_file = os.path.join(results_dir, "partition_meta.json")
assert os.path.isfile(part_meta_file)
part_meta = load_partition_meta(part_meta_file)
assert part_meta.num_parts == 2
assert part_meta.algo_name == "metis"
"""
TODO: skipping this test case since it depends on the dependency, mpi,
which is not yet configured in the CI framework.
"""
@unittest.skipIf(True, reason="mpi is not available in CI test framework.")
def test_parmetis_wrapper():
with tempfile.TemporaryDirectory() as root_dir:
num_chunks = 2
graph_name = "mag240m"
g = create_chunked_dataset(root_dir, num_chunks)
all_ntypes = g.ntypes
all_etypes = g.etypes
num_constraints = len(all_ntypes) + 3
num_institutions = g.number_of_nodes('institution')
num_authors = g.number_of_nodes('author')
num_papers = g.number_of_nodes('paper')
# Trigger ParMETIS.
schema_file = os.path.join(root_dir, 'chunked-data/metadata.json')
preproc_output_dir = os.path.join(
root_dir, 'chunked-data/preproc_output_dir'
)
parmetis_output_file = os.path.join(
os.getcwd(), f'{graph_name}_part.{num_chunks}'
)
partitions_dir = os.path.join(root_dir, 'chunked-data/partitions_dir')
hostfile = os.path.join(root_dir, 'ip_config.txt')
with open(hostfile, 'w') as f:
f.write('127.0.0.1\n')
f.write('127.0.0.1\n')
num_nodes = g.number_of_nodes()
num_edges = g.number_of_edges()
stats_file = f'{graph_name}_stats.txt'
with open(stats_file, 'w') as f:
f.write(f'{num_nodes} {num_edges} {num_constraints}')
parmetis_cmd = (
f'python3 tools/distpartitioning/parmetis_wrapper.py '
f'--schema_file {schema_file} '
f'--preproc_output_dir {preproc_output_dir} '
f'--hostfile {hostfile} '
f'--parmetis_output_file {parmetis_output_file} '
f'--partitions_dir {partitions_dir} '
)
print(f'Executing the following cmd: {parmetis_cmd}')
print(parmetis_cmd)
os.system(parmetis_cmd)
ntype_count = {
'author': num_authors,
'paper': num_papers,
'institution': num_institutions,
}
for ntype_name in ['author', 'paper', 'institution']:
fname = os.path.join(partitions_dir, f'{ntype_name}.txt')
print(fname)
assert os.path.isfile(fname)
# Load and check the partition ids in this file.
part_ids = np.loadtxt(fname)
assert part_ids.shape[0] == ntype_count[ntype_name]
assert np.min(part_ids) == 0
assert np.max(part_ids) == (num_chunks - 1)