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Unverified Commit 408eba24 authored by Rhett Ying's avatar Rhett Ying Committed by GitHub
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Multiple dist dl sampler (#4704) 

* [Dist] enable iterate multiple dist dataloaders simultaneously

* format file

* add support for any number of dataloaders

* fix lint

* refine code
parent ea48ce7a
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Showing with 576 additions and 265 deletions
python/dgl/distributed/dist_context.py
View file @ 408eba24
......@@ -12,6 +12,7 @@ import traceback
from enum import Enum
from .. import utils
from ..base import DGLError
from . import rpc
from .constants import MAX_QUEUE_SIZE
from .kvstore import close_kvstore, init_kvstore
......@@ -122,8 +123,11 @@ class CustomPool:
"""
ctx = mp.get_context("spawn")
self.num_workers = num_workers
self.queue_size = num_workers * 4
# As pool could be used by any number of dataloaders, queues
# should be able to take infinite elements to avoid dead lock.
self.queue_size = 0
self.result_queue = ctx.Queue(self.queue_size)
self.results = {} # key is dataloader name, value is fetched batch.
self.task_queues = []
self.process_list = []
self.current_proc_id = 0
......@@ -149,6 +153,7 @@ class CustomPool:
self.task_queues[i].put(
(MpCommand.SET_COLLATE_FN, (dataloader_name, func))
)
self.results[dataloader_name] = []
def submit_task(self, dataloader_name, args):
"""Submit task to workers"""
......@@ -167,9 +172,14 @@ class CustomPool:
def get_result(self, dataloader_name, timeout=1800):
"""Get result from result queue"""
result_dataloader_name, result = self.result_queue.get(timeout=timeout)
assert result_dataloader_name == dataloader_name
return result
if dataloader_name not in self.results:
raise DGLError(
f"Got result from an unknown dataloader {dataloader_name}."
)
while len(self.results[dataloader_name]) == 0:
dl_name, data = self.result_queue.get(timeout=timeout)
self.results[dl_name].append(data)
return self.results[dataloader_name].pop(0)
def delete_collate_fn(self, dataloader_name):
"""Delete collate function"""
......@@ -177,6 +187,7 @@ class CustomPool:
self.task_queues[i].put(
(MpCommand.DELETE_COLLATE_FN, (dataloader_name,))
)
del self.results[dataloader_name]
def call_barrier(self):
"""Call barrier at all workers"""
......
tests/distributed/test_mp_dataloader.py
View file @ 408eba24
import dgl
import unittest
import os
from scipy import sparse as spsp
from dgl.data import CitationGraphDataset
from dgl.distributed import sample_neighbors
from dgl.distributed import partition_graph, load_partition, load_partition_book
import sys
import multiprocessing as mp
import numpy as np
import os
import tempfile
import time
from utils import generate_ip_config, reset_envs
from pathlib import Path
from dgl.distributed import DistGraphServer, DistGraph, DistDataLoader
import pytest
import backend as F
import dgl
import numpy as np
import pytest
import torch as th
from dgl.data import CitationGraphDataset
from dgl.distributed import (
DistDataLoader,
DistGraph,
DistGraphServer,
load_partition,
partition_graph,
)
from scipy import sparse as spsp
from utils import generate_ip_config, reset_envs
class NeighborSampler(object):
def __init__(self, g, fanouts, sample_neighbors):
......@@ -23,13 +28,16 @@ class NeighborSampler(object):
def sample_blocks(self, seeds):
import torch as th
seeds = th.LongTensor(np.asarray(seeds))
blocks = []
for fanout in self.fanouts:
# For each seed node, sample ``fanout`` neighbors.
frontier = self.sample_neighbors(
self.g, seeds, fanout, replace=True)
# Then we compact the frontier into a bipartite graph for message passing.
self.g, seeds, fanout, replace=True
)
# Then we compact the frontier into a bipartite graph for
# message passing.
block = dgl.to_block(frontier, seeds)
# Obtain the seed nodes for next layer.
seeds = block.srcdata[dgl.NID]
......@@ -38,35 +46,59 @@ class NeighborSampler(object):
return blocks
def start_server(rank, tmpdir, disable_shared_mem, num_clients, keep_alive=False):
import dgl
print('server: #clients=' + str(num_clients))
g = DistGraphServer(rank, "mp_ip_config.txt", 1, num_clients,
tmpdir / 'test_sampling.json', disable_shared_mem=disable_shared_mem,
graph_format=['csc', 'coo'], keep_alive=keep_alive)
def start_server(
rank,
ip_config,
part_config,
disable_shared_mem,
num_clients,
keep_alive=False,
):
print("server: #clients=" + str(num_clients))
g = DistGraphServer(
rank,
ip_config,
1,
num_clients,
part_config,
disable_shared_mem=disable_shared_mem,
graph_format=["csc", "coo"],
keep_alive=keep_alive,
)
g.start()
def start_dist_dataloader(rank, tmpdir, num_server, drop_last, orig_nid, orig_eid, group_id=0):
def start_dist_dataloader(
rank,
ip_config,
part_config,
num_server,
drop_last,
orig_nid,
orig_eid,
group_id=0,
):
import dgl
import torch as th
os.environ['DGL_GROUP_ID'] = str(group_id)
dgl.distributed.initialize("mp_ip_config.txt")
os.environ["DGL_GROUP_ID"] = str(group_id)
dgl.distributed.initialize(ip_config)
gpb = None
disable_shared_mem = num_server > 0
if disable_shared_mem:
_, _, _, gpb, _, _, _ = load_partition(tmpdir / 'test_sampling.json', rank)
_, _, _, gpb, _, _, _ = load_partition(part_config, rank)
num_nodes_to_sample = 202
batch_size = 32
train_nid = th.arange(num_nodes_to_sample)
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=tmpdir / 'test_sampling.json')
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=part_config)
for i in range(num_server):
part, _, _, _, _, _, _ = load_partition(tmpdir / 'test_sampling.json', i)
part, _, _, _, _, _, _ = load_partition(part_config, i)
# Create sampler
sampler = NeighborSampler(dist_graph, [5, 10],
dgl.distributed.sample_neighbors)
sampler = NeighborSampler(
dist_graph, [5, 10], dgl.distributed.sample_neighbors
)
# We need to test creating DistDataLoader multiple times.
for i in range(2):
......@@ -76,13 +108,16 @@ def start_dist_dataloader(rank, tmpdir, num_server, drop_last, orig_nid, orig_ei
batch_size=batch_size,
collate_fn=sampler.sample_blocks,
shuffle=False,
drop_last=drop_last)
drop_last=drop_last,
)
groundtruth_g = CitationGraphDataset("cora")[0]
max_nid = []
for epoch in range(2):
for idx, blocks in zip(range(0, num_nodes_to_sample, batch_size), dataloader):
for idx, blocks in zip(
range(0, num_nodes_to_sample, batch_size), dataloader
):
block = blocks[-1]
o_src, o_dst = block.edges()
src_nodes_id = block.srcdata[dgl.NID][o_src]
......@@ -91,48 +126,75 @@ def start_dist_dataloader(rank, tmpdir, num_server, drop_last, orig_nid, orig_ei
src_nodes_id = orig_nid[src_nodes_id]
dst_nodes_id = orig_nid[dst_nodes_id]
has_edges = groundtruth_g.has_edges_between(src_nodes_id, dst_nodes_id)
has_edges = groundtruth_g.has_edges_between(
src_nodes_id, dst_nodes_id
)
assert np.all(F.asnumpy(has_edges))
# assert np.all(np.unique(np.sort(F.asnumpy(dst_nodes_id))) == np.arange(idx, batch_size))
if drop_last:
assert np.max(max_nid) == num_nodes_to_sample - 1 - num_nodes_to_sample % batch_size
assert (
np.max(max_nid)
== num_nodes_to_sample
- 1
- num_nodes_to_sample % batch_size
)
else:
assert np.max(max_nid) == num_nodes_to_sample - 1
del dataloader
dgl.distributed.exit_client() # this is needed since there's two test here in one process
# this is needed since there's two test here in one process
dgl.distributed.exit_client()
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
def test_standalone(tmpdir):
def test_standalone():
reset_envs()
generate_ip_config("mp_ip_config.txt", 1, 1)
with tempfile.TemporaryDirectory() as test_dir:
ip_config = os.path.join(test_dir, "ip_config.txt")
generate_ip_config(ip_config, 1, 1)
g = CitationGraphDataset("cora")[0]
print(g.idtype)
num_parts = 1
num_hops = 1
orig_nid, orig_eid = partition_graph(g, 'test_sampling', num_parts, tmpdir,
num_hops=num_hops, part_method='metis', reshuffle=True,
return_mapping=True)
os.environ['DGL_DIST_MODE'] = 'standalone'
orig_nid, orig_eid = partition_graph(
g,
"test_sampling",
num_parts,
test_dir,
num_hops=num_hops,
part_method="metis",
reshuffle=True,
return_mapping=True,
)
part_config = os.path.join(test_dir, "test_sampling.json")
os.environ["DGL_DIST_MODE"] = "standalone"
try:
start_dist_dataloader(0, tmpdir, 1, True, orig_nid, orig_eid)
start_dist_dataloader(
0, ip_config, part_config, 1, True, orig_nid, orig_eid
)
except Exception as e:
print(e)
def start_dist_neg_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, groundtruth_g):
def start_dist_neg_dataloader(
rank,
ip_config,
part_config,
num_server,
num_workers,
orig_nid,
groundtruth_g,
):
import dgl
import torch as th
dgl.distributed.initialize("mp_ip_config.txt")
dgl.distributed.initialize(ip_config)
gpb = None
disable_shared_mem = num_server > 1
if disable_shared_mem:
_, _, _, gpb, _, _, _ = load_partition(tmpdir / 'test_sampling.json', rank)
_, _, _, gpb, _, _, _ = load_partition(part_config, rank)
num_edges_to_sample = 202
batch_size = 32
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=tmpdir / 'test_sampling.json')
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=part_config)
assert len(dist_graph.ntypes) == len(groundtruth_g.ntypes)
assert len(dist_graph.etypes) == len(groundtruth_g.etypes)
if len(dist_graph.etypes) == 1:
......@@ -141,21 +203,25 @@ def start_dist_neg_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, g
train_eid = {dist_graph.etypes[0]: th.arange(num_edges_to_sample)}
for i in range(num_server):
part, _, _, _, _, _, _ = load_partition(tmpdir / 'test_sampling.json', i)
part, _, _, _, _, _, _ = load_partition(part_config, i)
num_negs = 5
sampler = dgl.dataloading.MultiLayerNeighborSampler([5,10])
negative_sampler=dgl.dataloading.negative_sampler.Uniform(num_negs)
dataloader = dgl.dataloading.DistEdgeDataLoader(dist_graph,
sampler = dgl.dataloading.MultiLayerNeighborSampler([5, 10])
negative_sampler = dgl.dataloading.negative_sampler.Uniform(num_negs)
dataloader = dgl.dataloading.DistEdgeDataLoader(
dist_graph,
train_eid,
sampler,
batch_size=batch_size,
negative_sampler=negative_sampler,
shuffle=True,
drop_last=False,
num_workers=num_workers)
num_workers=num_workers,
)
for _ in range(2):
for _, (_, pos_graph, neg_graph, blocks) in zip(range(0, num_edges_to_sample, batch_size), dataloader):
for _, (_, pos_graph, neg_graph, blocks) in zip(
range(0, num_edges_to_sample, batch_size), dataloader
):
block = blocks[-1]
for src_type, etype, dst_type in block.canonical_etypes:
o_src, o_dst = block.edges(etype=etype)
......@@ -163,42 +229,80 @@ def start_dist_neg_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, g
dst_nodes_id = block.dstnodes[dst_type].data[dgl.NID][o_dst]
src_nodes_id = orig_nid[src_type][src_nodes_id]
dst_nodes_id = orig_nid[dst_type][dst_nodes_id]
has_edges = groundtruth_g.has_edges_between(src_nodes_id, dst_nodes_id, etype=etype)
has_edges = groundtruth_g.has_edges_between(
src_nodes_id, dst_nodes_id, etype=etype
)
assert np.all(F.asnumpy(has_edges))
assert np.all(F.asnumpy(block.dstnodes[dst_type].data[dgl.NID]) == F.asnumpy(pos_graph.nodes[dst_type].data[dgl.NID]))
assert np.all(F.asnumpy(block.dstnodes[dst_type].data[dgl.NID]) == F.asnumpy(neg_graph.nodes[dst_type].data[dgl.NID]))
assert np.all(
F.asnumpy(block.dstnodes[dst_type].data[dgl.NID])
== F.asnumpy(pos_graph.nodes[dst_type].data[dgl.NID])
)
assert np.all(
F.asnumpy(block.dstnodes[dst_type].data[dgl.NID])
== F.asnumpy(neg_graph.nodes[dst_type].data[dgl.NID])
)
assert pos_graph.num_edges() * num_negs == neg_graph.num_edges()
del dataloader
dgl.distributed.exit_client() # this is needed since there's two test here in one process
# this is needed since there's two test here in one process
dgl.distributed.exit_client()
def check_neg_dataloader(g, tmpdir, num_server, num_workers):
generate_ip_config("mp_ip_config.txt", num_server, num_server)
def check_neg_dataloader(g, num_server, num_workers):
with tempfile.TemporaryDirectory() as test_dir:
ip_config = "ip_config.txt"
generate_ip_config(ip_config, num_server, num_server)
num_parts = num_server
num_hops = 1
orig_nid, orig_eid = partition_graph(g, 'test_sampling', num_parts, tmpdir,
num_hops=num_hops, part_method='metis',
reshuffle=True, return_mapping=True)
orig_nid, orig_eid = partition_graph(
g,
"test_sampling",
num_parts,
test_dir,
num_hops=num_hops,
part_method="metis",
reshuffle=True,
return_mapping=True,
)
part_config = os.path.join(test_dir, "test_sampling.json")
if not isinstance(orig_nid, dict):
orig_nid = {g.ntypes[0]: orig_nid}
if not isinstance(orig_eid, dict):
orig_eid = {g.etypes[0]: orig_eid}
pserver_list = []
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_server):
p = ctx.Process(target=start_server, args=(
i, tmpdir, num_server > 1, num_workers+1))
p = ctx.Process(
target=start_server,
args=(
i,
ip_config,
part_config,
num_server > 1,
num_workers + 1,
),
)
p.start()
time.sleep(1)
pserver_list.append(p)
os.environ['DGL_DIST_MODE'] = 'distributed'
os.environ['DGL_NUM_SAMPLER'] = str(num_workers)
os.environ["DGL_DIST_MODE"] = "distributed"
os.environ["DGL_NUM_SAMPLER"] = str(num_workers)
ptrainer_list = []
p = ctx.Process(target=start_dist_neg_dataloader, args=(
0, tmpdir, num_server, num_workers, orig_nid, g))
p = ctx.Process(
target=start_dist_neg_dataloader,
args=(
0,
ip_config,
part_config,
num_server,
num_workers,
orig_nid,
g,
),
)
p.start()
ptrainer_list.append(p)
......@@ -207,50 +311,83 @@ def check_neg_dataloader(g, tmpdir, num_server, num_workers):
for p in ptrainer_list:
p.join()
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
@pytest.mark.parametrize("num_server", [3])
@pytest.mark.parametrize("num_workers", [0, 4])
@pytest.mark.parametrize("drop_last", [True, False])
@pytest.mark.parametrize("reshuffle", [True, False])
@pytest.mark.parametrize("num_groups", [1])
def test_dist_dataloader(tmpdir, num_server, num_workers, drop_last, reshuffle, num_groups):
def test_dist_dataloader(
num_server, num_workers, drop_last, reshuffle, num_groups
):
reset_envs()
# No multiple partitions on single machine for
# multiple client groups in case of race condition.
if num_groups > 1:
num_server = 1
generate_ip_config("mp_ip_config.txt", num_server, num_server)
with tempfile.TemporaryDirectory() as test_dir:
ip_config = "ip_config.txt"
generate_ip_config(ip_config, num_server, num_server)
g = CitationGraphDataset("cora")[0]
print(g.idtype)
num_parts = num_server
num_hops = 1
orig_nid, orig_eid = partition_graph(g, 'test_sampling', num_parts, tmpdir,
num_hops=num_hops, part_method='metis',
reshuffle=reshuffle, return_mapping=True)
orig_nid, orig_eid = partition_graph(
g,
"test_sampling",
num_parts,
test_dir,
num_hops=num_hops,
part_method="metis",
reshuffle=reshuffle,
return_mapping=True,
)
part_config = os.path.join(test_dir, "test_sampling.json")
pserver_list = []
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
keep_alive = num_groups > 1
for i in range(num_server):
p = ctx.Process(target=start_server, args=(
i, tmpdir, num_server > 1, num_workers+1, keep_alive))
p = ctx.Process(
target=start_server,
args=(
i,
ip_config,
part_config,
num_server > 1,
num_workers + 1,
keep_alive,
),
)
p.start()
time.sleep(1)
pserver_list.append(p)
os.environ['DGL_DIST_MODE'] = 'distributed'
os.environ['DGL_NUM_SAMPLER'] = str(num_workers)
os.environ["DGL_DIST_MODE"] = "distributed"
os.environ["DGL_NUM_SAMPLER"] = str(num_workers)
ptrainer_list = []
num_trainers = 1
for trainer_id in range(num_trainers):
for group_id in range(num_groups):
p = ctx.Process(target=start_dist_dataloader, args=(
trainer_id, tmpdir, num_server, drop_last, orig_nid, orig_eid, group_id))
p = ctx.Process(
target=start_dist_dataloader,
args=(
trainer_id,
ip_config,
part_config,
num_server,
drop_last,
orig_nid,
orig_eid,
group_id,
),
)
p.start()
time.sleep(1) # avoid race condition when instantiating DistGraph
time.sleep(
1
) # avoid race condition when instantiating DistGraph
ptrainer_list.append(p)
for p in ptrainer_list:
......@@ -263,32 +400,45 @@ def test_dist_dataloader(tmpdir, num_server, num_workers, drop_last, reshuffle,
for p in pserver_list:
p.join()
def start_node_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, orig_eid, groundtruth_g):
import dgl
import torch as th
dgl.distributed.initialize("mp_ip_config.txt")
def start_node_dataloader(
rank,
ip_config,
part_config,
num_server,
num_workers,
orig_nid,
orig_eid,
groundtruth_g,
):
dgl.distributed.initialize(ip_config)
gpb = None
disable_shared_mem = num_server > 1
if disable_shared_mem:
_, _, _, gpb, _, _, _ = load_partition(tmpdir / 'test_sampling.json', rank)
_, _, _, gpb, _, _, _ = load_partition(part_config, rank)
num_nodes_to_sample = 202
batch_size = 32
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=tmpdir / 'test_sampling.json')
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=part_config)
assert len(dist_graph.ntypes) == len(groundtruth_g.ntypes)
assert len(dist_graph.etypes) == len(groundtruth_g.etypes)
if len(dist_graph.etypes) == 1:
train_nid = th.arange(num_nodes_to_sample)
else:
train_nid = {'n3': th.arange(num_nodes_to_sample)}
train_nid = {"n3": th.arange(num_nodes_to_sample)}
for i in range(num_server):
part, _, _, _, _, _, _ = load_partition(tmpdir / 'test_sampling.json', i)
part, _, _, _, _, _, _ = load_partition(part_config, i)
# Create sampler
sampler = dgl.dataloading.MultiLayerNeighborSampler([
sampler = dgl.dataloading.MultiLayerNeighborSampler(
[
# test dict for hetero
{etype: 5 for etype in dist_graph.etypes} if len(dist_graph.etypes) > 1 else 5,
10]) # test int for hetero
{etype: 5 for etype in dist_graph.etypes}
if len(dist_graph.etypes) > 1
else 5,
10,
]
) # test int for hetero
# We need to test creating DistDataLoader multiple times.
for i in range(2):
......@@ -300,10 +450,13 @@ def start_node_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, orig_
batch_size=batch_size,
shuffle=True,
drop_last=False,
num_workers=num_workers)
num_workers=num_workers,
)
for epoch in range(2):
for idx, (_, _, blocks) in zip(range(0, num_nodes_to_sample, batch_size), dataloader):
for idx, (_, _, blocks) in zip(
range(0, num_nodes_to_sample, batch_size), dataloader
):
block = blocks[-1]
for src_type, etype, dst_type in block.canonical_etypes:
o_src, o_dst = block.edges(etype=etype)
......@@ -311,23 +464,33 @@ def start_node_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, orig_
dst_nodes_id = block.dstnodes[dst_type].data[dgl.NID][o_dst]
src_nodes_id = orig_nid[src_type][src_nodes_id]
dst_nodes_id = orig_nid[dst_type][dst_nodes_id]
has_edges = groundtruth_g.has_edges_between(src_nodes_id, dst_nodes_id, etype=etype)
has_edges = groundtruth_g.has_edges_between(
src_nodes_id, dst_nodes_id, etype=etype
)
assert np.all(F.asnumpy(has_edges))
# assert np.all(np.unique(np.sort(F.asnumpy(dst_nodes_id))) == np.arange(idx, batch_size))
del dataloader
dgl.distributed.exit_client() # this is needed since there's two test here in one process
def start_edge_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, orig_eid, groundtruth_g):
import dgl
import torch as th
dgl.distributed.initialize("mp_ip_config.txt")
# this is needed since there's two test here in one process
dgl.distributed.exit_client()
def start_edge_dataloader(
rank,
ip_config,
part_config,
num_server,
num_workers,
orig_nid,
orig_eid,
groundtruth_g,
):
dgl.distributed.initialize(ip_config)
gpb = None
disable_shared_mem = num_server > 1
if disable_shared_mem:
_, _, _, gpb, _, _, _ = load_partition(tmpdir / 'test_sampling.json', rank)
_, _, _, gpb, _, _, _ = load_partition(part_config, rank)
num_edges_to_sample = 202
batch_size = 32
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=tmpdir / 'test_sampling.json')
dist_graph = DistGraph("test_mp", gpb=gpb, part_config=part_config)
assert len(dist_graph.ntypes) == len(groundtruth_g.ntypes)
assert len(dist_graph.etypes) == len(groundtruth_g.etypes)
if len(dist_graph.etypes) == 1:
......@@ -336,7 +499,7 @@ def start_edge_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, orig_
train_eid = {dist_graph.etypes[0]: th.arange(num_edges_to_sample)}
for i in range(num_server):
part, _, _, _, _, _, _ = load_partition(tmpdir / 'test_sampling.json', i)
part, _, _, _, _, _, _ = load_partition(part_config, i)
# Create sampler
sampler = dgl.dataloading.MultiLayerNeighborSampler([5, 10])
......@@ -351,10 +514,13 @@ def start_edge_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, orig_
batch_size=batch_size,
shuffle=True,
drop_last=False,
num_workers=num_workers)
num_workers=num_workers,
)
for epoch in range(2):
for idx, (input_nodes, pos_pair_graph, blocks) in zip(range(0, num_edges_to_sample, batch_size), dataloader):
for idx, (input_nodes, pos_pair_graph, blocks) in zip(
range(0, num_edges_to_sample, batch_size), dataloader
):
block = blocks[-1]
for src_type, etype, dst_type in block.canonical_etypes:
o_src, o_dst = block.edges(etype=etype)
......@@ -362,46 +528,93 @@ def start_edge_dataloader(rank, tmpdir, num_server, num_workers, orig_nid, orig_
dst_nodes_id = block.dstnodes[dst_type].data[dgl.NID][o_dst]
src_nodes_id = orig_nid[src_type][src_nodes_id]
dst_nodes_id = orig_nid[dst_type][dst_nodes_id]
has_edges = groundtruth_g.has_edges_between(src_nodes_id, dst_nodes_id, etype=etype)
has_edges = groundtruth_g.has_edges_between(
src_nodes_id, dst_nodes_id, etype=etype
)
assert np.all(F.asnumpy(has_edges))
assert np.all(F.asnumpy(block.dstnodes[dst_type].data[dgl.NID]) == F.asnumpy(pos_pair_graph.nodes[dst_type].data[dgl.NID]))
# assert np.all(np.unique(np.sort(F.asnumpy(dst_nodes_id))) == np.arange(idx, batch_size))
assert np.all(
F.asnumpy(block.dstnodes[dst_type].data[dgl.NID])
== F.asnumpy(
pos_pair_graph.nodes[dst_type].data[dgl.NID]
)
)
del dataloader
dgl.distributed.exit_client() # this is needed since there's two test here in one process
dgl.distributed.exit_client()
def check_dataloader(g, tmpdir, num_server, num_workers, dataloader_type):
generate_ip_config("mp_ip_config.txt", num_server, num_server)
def check_dataloader(g, num_server, num_workers, dataloader_type):
with tempfile.TemporaryDirectory() as test_dir:
ip_config = "ip_config.txt"
generate_ip_config(ip_config, num_server, num_server)
num_parts = num_server
num_hops = 1
orig_nid, orig_eid = partition_graph(g, 'test_sampling', num_parts, tmpdir,
num_hops=num_hops, part_method='metis',
reshuffle=True, return_mapping=True)
orig_nid, orig_eid = partition_graph(
g,
"test_sampling",
num_parts,
test_dir,
num_hops=num_hops,
part_method="metis",
reshuffle=True,
return_mapping=True,
)
part_config = os.path.join(test_dir, "test_sampling.json")
if not isinstance(orig_nid, dict):
orig_nid = {g.ntypes[0]: orig_nid}
if not isinstance(orig_eid, dict):
orig_eid = {g.etypes[0]: orig_eid}
pserver_list = []
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_server):
p = ctx.Process(target=start_server, args=(
i, tmpdir, num_server > 1, num_workers+1))
p = ctx.Process(
target=start_server,
args=(
i,
ip_config,
part_config,
num_server > 1,
num_workers + 1,
),
)
p.start()
time.sleep(1)
pserver_list.append(p)
os.environ['DGL_DIST_MODE'] = 'distributed'
os.environ['DGL_NUM_SAMPLER'] = str(num_workers)
os.environ["DGL_DIST_MODE"] = "distributed"
os.environ["DGL_NUM_SAMPLER"] = str(num_workers)
ptrainer_list = []
if dataloader_type == 'node':
p = ctx.Process(target=start_node_dataloader, args=(
0, tmpdir, num_server, num_workers, orig_nid, orig_eid, g))
if dataloader_type == "node":
p = ctx.Process(
target=start_node_dataloader,
args=(
0,
ip_config,
part_config,
num_server,
num_workers,
orig_nid,
orig_eid,
g,
),
)
p.start()
ptrainer_list.append(p)
elif dataloader_type == 'edge':
p = ctx.Process(target=start_edge_dataloader, args=(
0, tmpdir, num_server, num_workers, orig_nid, orig_eid, g))
elif dataloader_type == "edge":
p = ctx.Process(
target=start_edge_dataloader,
args=(
0,
ip_config,
part_config,
num_server,
num_workers,
orig_nid,
orig_eid,
g,
),
)
p.start()
ptrainer_list.append(p)
for p in pserver_list:
......@@ -409,55 +622,142 @@ def check_dataloader(g, tmpdir, num_server, num_workers, dataloader_type):
for p in ptrainer_list:
p.join()
def create_random_hetero():
num_nodes = {'n1': 10000, 'n2': 10010, 'n3': 10020}
etypes = [('n1', 'r1', 'n2'),
('n1', 'r2', 'n3'),
('n2', 'r3', 'n3')]
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)
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)
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
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
@pytest.mark.parametrize("num_server", [3])
@pytest.mark.parametrize("num_workers", [0, 4])
@pytest.mark.parametrize("dataloader_type", ["node", "edge"])
def test_dataloader(tmpdir, num_server, num_workers, dataloader_type):
def test_dataloader(num_server, num_workers, dataloader_type):
reset_envs()
g = CitationGraphDataset("cora")[0]
check_dataloader(g, tmpdir, num_server, num_workers, dataloader_type)
check_dataloader(g, num_server, num_workers, dataloader_type)
g = create_random_hetero()
check_dataloader(g, tmpdir, num_server, num_workers, dataloader_type)
check_dataloader(g, num_server, num_workers, dataloader_type)
@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", [3])
@pytest.mark.parametrize("num_workers", [0, 4])
def test_neg_dataloader(tmpdir, num_server, num_workers):
def test_neg_dataloader(num_server, num_workers):
reset_envs()
g = CitationGraphDataset("cora")[0]
check_neg_dataloader(g, tmpdir, num_server, num_workers)
check_neg_dataloader(g, num_server, num_workers)
g = create_random_hetero()
check_neg_dataloader(g, tmpdir, num_server, num_workers)
if __name__ == "__main__":
import tempfile
with tempfile.TemporaryDirectory() as tmpdirname:
test_standalone(Path(tmpdirname))
test_dataloader(Path(tmpdirname), 3, 4, 'node')
test_dataloader(Path(tmpdirname), 3, 4, 'edge')
test_neg_dataloader(Path(tmpdirname), 3, 4)
for num_groups in [1]:
test_dist_dataloader(Path(tmpdirname), 3, 0, True, True, num_groups)
test_dist_dataloader(Path(tmpdirname), 3, 4, True, True, num_groups)
test_dist_dataloader(Path(tmpdirname), 3, 0, True, False, num_groups)
test_dist_dataloader(Path(tmpdirname), 3, 4, True, False, num_groups)
check_neg_dataloader(g, num_server, num_workers)
def start_multiple_dataloaders(
ip_config, part_config, graph_name, orig_g, num_dataloaders, dataloader_type
):
dgl.distributed.initialize(ip_config)
dist_g = dgl.distributed.DistGraph(graph_name, part_config=part_config)
if dataloader_type == "node":
train_ids = th.arange(orig_g.num_nodes())
batch_size = orig_g.num_nodes() // 100
else:
train_ids = th.arange(orig_g.num_edges())
batch_size = orig_g.num_edges() // 100
sampler = dgl.dataloading.NeighborSampler([-1])
dataloaders = []
dl_iters = []
for _ in range(num_dataloaders):
if dataloader_type == "node":
dataloader = dgl.dataloading.DistNodeDataLoader(
dist_g, train_ids, sampler, batch_size=batch_size
)
else:
dataloader = dgl.dataloading.DistEdgeDataLoader(
dist_g, train_ids, sampler, batch_size=batch_size
)
dataloaders.append(dataloader)
dl_iters.append(iter(dataloader))
# iterate on multiple dataloaders randomly
while len(dl_iters) > 0:
next_dl = np.random.choice(len(dl_iters), 1)[0]
try:
_ = next(dl_iters[next_dl])
except StopIteration:
dl_iters.pop(next_dl)
del dataloaders[next_dl]
dgl.distributed.exit_client()
@pytest.mark.parametrize("num_dataloaders", [1, 4])
@pytest.mark.parametrize("num_workers", [0, 1, 4])
@pytest.mark.parametrize("dataloader_type", ["node", "edge"])
def test_multiple_dist_dataloaders(
num_dataloaders, num_workers, dataloader_type
):
reset_envs()
os.environ["DGL_DIST_MODE"] = "distributed"
os.environ["DGL_NUM_SAMPLER"] = str(num_workers)
num_parts = 1
num_servers = 1
with tempfile.TemporaryDirectory() as test_dir:
ip_config = os.path.join(test_dir, "ip_config.txt")
generate_ip_config(ip_config, num_parts, num_servers)
orig_g = dgl.rand_graph(1000, 10000)
graph_name = "test"
partition_graph(orig_g, graph_name, num_parts, test_dir)
part_config = os.path.join(test_dir, f"{graph_name}.json")
p_servers = []
ctx = mp.get_context("spawn")
for i in range(num_servers):
p = ctx.Process(
target=start_server,
args=(
i,
ip_config,
part_config,
num_servers > 1,
num_workers + 1,
),
)
p.start()
time.sleep(1)
p_servers.append(p)
p_client = ctx.Process(
target=start_multiple_dataloaders,
args=(
ip_config,
part_config,
graph_name,
orig_g,
num_dataloaders,
dataloader_type,
),
)
p_client.start()
p_client.join()
for p in p_servers:
p.join()
reset_envs()
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