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import socket
from typing import Tuple
import pytest
import torch
import torch.multiprocessing as mp
from torch_geometric.data import Data, HeteroData
from torch_geometric.datasets import FakeDataset, FakeHeteroDataset
from torch_geometric.distributed import (
DistContext,
DistLinkNeighborLoader,
DistNeighborSampler,
LocalFeatureStore,
LocalGraphStore,
Partitioner,
)
from torch_geometric.testing import onlyDistributedTest, withMETIS
from torch_geometric.testing.distributed import ProcArgs, assert_run_mproc
def create_dist_data(tmp_path: str, rank: int):
graph_store = LocalGraphStore.from_partition(tmp_path, pid=rank)
feat_store = LocalFeatureStore.from_partition(tmp_path, pid=rank)
return feat_store, graph_store
def dist_link_neighbor_loader_homo(
world_size: int,
tmp_path: str,
rank: int,
master_addr: str,
master_port: int,
num_workers: int,
async_sampling: bool,
neg_ratio: float,
):
part_data = create_dist_data(tmp_path, rank)
current_ctx = DistContext(
rank=rank,
global_rank=rank,
world_size=world_size,
global_world_size=world_size,
group_name='dist-loader-test',
)
edge_label_index = part_data[1].get_edge_index(None, 'coo')
edge_label = torch.randint(high=2, size=(edge_label_index.size(1), ))
loader = DistLinkNeighborLoader(
data=part_data,
edge_label_index=(None, edge_label_index),
edge_label=edge_label if neg_ratio is not None else None,
num_neighbors=[1],
batch_size=10,
num_workers=num_workers,
master_addr=master_addr,
master_port=master_port,
current_ctx=current_ctx,
concurrency=10,
drop_last=True,
async_sampling=async_sampling,
)
assert str(loader).startswith('DistLinkNeighborLoader')
assert str(mp.current_process().pid) in str(loader)
assert isinstance(loader.dist_sampler, DistNeighborSampler)
assert not part_data[0].meta['is_hetero']
for batch in loader:
assert isinstance(batch, Data)
assert batch.n_id.size() == (batch.num_nodes, )
assert batch.edge_index.min() >= 0
assert batch.edge_index.max() < batch.num_nodes
assert loader.channel.empty()
def dist_link_neighbor_loader_hetero(
world_size: int,
tmp_path: str,
rank: int,
master_addr: str,
master_port: int,
num_workers: int,
async_sampling: bool,
neg_ratio: float,
edge_type: Tuple[str, str, str],
):
part_data = create_dist_data(tmp_path, rank)
current_ctx = DistContext(
rank=rank,
global_rank=rank,
world_size=world_size,
global_world_size=world_size,
group_name="dist-loader-test",
)
edge_label_index = part_data[1].get_edge_index(edge_type, 'coo')
edge_label = torch.randint(high=2, size=(edge_label_index.size(1), ))
loader = DistLinkNeighborLoader(
data=part_data,
edge_label_index=(edge_type, edge_label_index),
edge_label=edge_label if neg_ratio is not None else None,
num_neighbors=[1],
batch_size=10,
num_workers=num_workers,
master_addr=master_addr,
master_port=master_port,
current_ctx=current_ctx,
concurrency=10,
drop_last=True,
async_sampling=async_sampling,
)
assert str(loader).startswith('DistLinkNeighborLoader')
assert str(mp.current_process().pid) in str(loader)
assert isinstance(loader.dist_sampler, DistNeighborSampler)
assert part_data[0].meta['is_hetero']
for batch in loader:
assert isinstance(batch, HeteroData)
assert len(batch.node_types) == 2
for node_type in batch.node_types:
assert torch.equal(batch[node_type].x, batch.x_dict[node_type])
assert batch.x_dict[node_type].size(0) >= 0
assert batch[node_type].n_id.size(0) == batch[node_type].num_nodes
assert len(batch.edge_types) == 4
for key in batch.edge_types:
if key[-1] == 'v0':
assert batch[key].num_sampled_edges[0] > 0
assert batch[key].edge_attr.size(0) == batch[key].num_edges
else:
batch[key].num_sampled_edges[0] == 0
assert loader.channel.empty()
@withMETIS
@onlyDistributedTest
@pytest.mark.parametrize('num_parts', [2])
@pytest.mark.parametrize('num_workers', [0])
@pytest.mark.parametrize('async_sampling', [True])
@pytest.mark.parametrize('neg_ratio', [None])
def test_dist_link_neighbor_loader_homo(
tmp_path,
num_parts,
num_workers,
async_sampling,
neg_ratio,
):
addr = '127.0.0.1'
mp_context = torch.multiprocessing.get_context('spawn')
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.settimeout(1)
s.bind(('', 0))
port = s.getsockname()[1]
data = FakeDataset(
num_graphs=1,
avg_num_nodes=100,
avg_degree=3,
edge_dim=2,
)[0]
partitioner = Partitioner(data, num_parts, tmp_path)
partitioner.generate_partition()
procs = [
ProcArgs(
target=dist_link_neighbor_loader_homo,
args=(tmp_path, part, addr, port, num_workers, async_sampling,
neg_ratio),
) for part in range(num_parts)
]
assert_run_mproc(mp_context, procs)
@withMETIS
@onlyDistributedTest
@pytest.mark.parametrize('num_parts', [2])
@pytest.mark.parametrize('num_workers', [0])
@pytest.mark.parametrize('async_sampling', [True])
@pytest.mark.parametrize('neg_ratio', [None])
@pytest.mark.parametrize('edge_type', [('v0', 'e0', 'v0')])
def test_dist_link_neighbor_loader_hetero(
tmp_path,
num_parts,
num_workers,
async_sampling,
neg_ratio,
edge_type,
):
mp_context = torch.multiprocessing.get_context('spawn')
addr = '127.0.0.1'
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.settimeout(1)
s.bind(('', 0))
port = s.getsockname()[1]
data = FakeHeteroDataset(
num_graphs=1,
avg_num_nodes=100,
avg_degree=3,
num_node_types=2,
num_edge_types=4,
edge_dim=2,
)[0]
partitioner = Partitioner(data, num_parts, tmp_path)
partitioner.generate_partition()
procs = [
ProcArgs(
target=dist_link_neighbor_loader_hetero,
args=(tmp_path, part, addr, port, num_workers, async_sampling,
neg_ratio, edge_type),
) for part in range(num_parts)
]
assert_run_mproc(mp_context, procs)
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