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import socket
import warnings
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,
DistNeighborLoader,
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_neighbor_loader_homo(
world_size: int,
tmp_path: str,
rank: int,
master_addr: str,
master_port: int,
num_workers: int,
async_sampling: bool,
):
part_data = create_dist_data(tmp_path, rank)
input_nodes = part_data[0].get_global_id(None)
current_ctx = DistContext(
rank=rank,
global_rank=rank,
world_size=world_size,
global_world_size=world_size,
group_name='dist-loader-test',
)
loader = DistNeighborLoader(
part_data,
num_neighbors=[1],
batch_size=10,
num_workers=num_workers,
input_nodes=input_nodes,
master_addr=master_addr,
master_port=master_port,
current_ctx=current_ctx,
concurrency=10,
drop_last=True,
async_sampling=async_sampling,
)
edge_index = part_data[1]._edge_index[(None, 'coo')]
assert str(loader).startswith('DistNeighborLoader')
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.input_id.numel() == batch.batch_size == 10
assert batch.edge_index.min() >= 0
assert batch.edge_index.max() < batch.num_nodes
assert torch.equal(
batch.n_id[batch.edge_index],
edge_index[:, batch.e_id],
)
assert loader.channel.empty()
def dist_neighbor_loader_hetero(
world_size: int,
tmp_path: str,
rank: int,
master_addr: str,
master_port: int,
num_workers: int,
async_sampling: bool,
):
part_data = create_dist_data(tmp_path, rank)
input_nodes = ('v0', part_data[0].get_global_id('v0'))
current_ctx = DistContext(
rank=rank,
global_rank=rank,
world_size=world_size,
global_world_size=world_size,
group_name='dist-loader-test',
)
loader = DistNeighborLoader(
part_data,
num_neighbors=[1],
batch_size=10,
num_workers=num_workers,
input_nodes=input_nodes,
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('DistNeighborLoader')
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 batch['v0'].input_id.numel() == batch['v0'].batch_size == 10
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 edge_type in batch.edge_types:
num_edges = batch[edge_type].edge_index.size(1)
if num_edges > 0: # Test edge mapping:
assert batch[edge_type].edge_attr.size(0) == num_edges
src, _, dst = edge_type
edge_index = part_data[1]._edge_index[(edge_type, "coo")]
global_edge_index1 = torch.stack([
batch[src].n_id[batch[edge_type].edge_index[0]],
batch[dst].n_id[batch[edge_type].edge_index[1]],
], dim=0)
# TODO There is a current known flake, which we need to fix:
e_id = batch[edge_type].e_id
if e_id.numel() > 0 and e_id.max() >= edge_index.size(1):
warnings.warn("Known test flake")
else:
global_edge_index2 = edge_index[:, e_id]
if not torch.equal(global_edge_index1, global_edge_index2):
warnings.warn("Known test flake")
assert loader.channel.empty()
@withMETIS
@onlyDistributedTest
@pytest.mark.parametrize('num_parts', [2])
@pytest.mark.parametrize('num_workers', [0])
@pytest.mark.parametrize('async_sampling', [True])
def test_dist_neighbor_loader_homo(
tmp_path,
num_parts,
num_workers,
async_sampling,
):
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 = 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_neighbor_loader_homo,
args=(tmp_path, part, addr, port, num_workers, async_sampling),
) 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])
def test_dist_neighbor_loader_hetero(
tmp_path,
num_parts,
num_workers,
async_sampling,
):
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_neighbor_loader_hetero,
args=(tmp_path, part, addr, port, num_workers, async_sampling),
) for part in range(num_parts)
]
assert_run_mproc(mp_context, procs)
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