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import argparse
import os
import os.path as osp
import torch
from ogb.nodeproppred import PygNodePropPredDataset
import torch_geometric.transforms as T
from torch_geometric.datasets import OGB_MAG, MovieLens, Reddit
from torch_geometric.distributed import Partitioner
from torch_geometric.utils import mask_to_index
def partition_dataset(
dataset_name: str,
root_dir: str,
num_parts: int,
recursive: bool = False,
use_sparse_tensor: bool = False,
):
if not osp.isabs(root_dir):
path = osp.dirname(osp.realpath(__file__))
root_dir = osp.join(path, root_dir)
dataset_dir = osp.join(root_dir, 'dataset', dataset_name)
dataset = get_dataset(dataset_name, dataset_dir, use_sparse_tensor)
data = dataset[0]
save_dir = osp.join(root_dir, 'partitions', dataset_name,
f'{num_parts}-parts')
partitions_dir = osp.join(save_dir, f'{dataset_name}-partitions')
partitioner = Partitioner(data, num_parts, partitions_dir, recursive)
partitioner.generate_partition()
print('-- Saving label ...')
label_dir = osp.join(save_dir, f'{dataset_name}-label')
os.makedirs(label_dir, exist_ok=True)
if dataset_name == 'ogbn-mag':
split_data = data['paper']
label = split_data.y
else:
split_data = data
if dataset_name == 'ogbn-products':
label = split_data.y.squeeze()
elif dataset_name == 'Reddit':
label = split_data.y
elif dataset_name == 'MovieLens':
label = split_data[data.edge_types[0]].edge_label
torch.save(label, osp.join(label_dir, 'label.pt'))
split_idx = get_idx_split(dataset, dataset_name, split_data)
if dataset_name == 'MovieLens':
save_link_partitions(split_idx, data, dataset_name, num_parts,
save_dir)
else:
save_partitions(split_idx, dataset_name, num_parts, save_dir)
def get_dataset(name, dataset_dir, use_sparse_tensor=False):
transforms = []
if use_sparse_tensor:
transforms = [T.ToSparseTensor(remove_edge_index=False)]
if name == 'ogbn-mag':
transforms = [T.ToUndirected(merge=True)] + transforms
return OGB_MAG(
root=dataset_dir,
preprocess='metapath2vec',
transform=T.Compose(transforms),
)
elif name == 'ogbn-products':
transforms = [T.RemoveDuplicatedEdges()] + transforms
return PygNodePropPredDataset(
'ogbn-products',
root=dataset_dir,
transform=T.Compose(transforms),
)
elif name == 'MovieLens':
transforms = [T.ToUndirected(merge=True)] + transforms
return MovieLens(
root=dataset_dir,
model_name='all-MiniLM-L6-v2',
transform=T.Compose(transforms),
)
elif name == 'Reddit':
return Reddit(
root=dataset_dir,
transform=T.Compose(transforms),
)
def get_idx_split(dataset, dataset_name, split_data):
if dataset_name == 'ogbn-mag' or dataset_name == 'Reddit':
train_idx = mask_to_index(split_data.train_mask)
test_idx = mask_to_index(split_data.test_mask)
val_idx = mask_to_index(split_data.val_mask)
elif dataset_name == 'ogbn-products':
split_idx = dataset.get_idx_split()
train_idx = split_idx['train']
test_idx = split_idx['test']
val_idx = split_idx['valid']
elif dataset_name == 'MovieLens':
# Perform a 80/10/10 temporal link-level split:
perm = torch.argsort(dataset[0][('user', 'rates', 'movie')].time)
train_idx = perm[:int(0.8 * perm.size(0))]
val_idx = perm[int(0.8 * perm.size(0)):int(0.9 * perm.size(0))]
test_idx = perm[int(0.9 * perm.size(0)):]
return {'train': train_idx, 'val': val_idx, 'test': test_idx}
def save_partitions(split_idx, dataset_name, num_parts, save_dir):
for key, idx in split_idx.items():
print(f'-- Partitioning {key} indices ...')
idx = idx.split(idx.size(0) // num_parts)
part_dir = osp.join(save_dir, f'{dataset_name}-{key}-partitions')
os.makedirs(part_dir, exist_ok=True)
for i in range(num_parts):
torch.save(idx[i], osp.join(part_dir, f'partition{i}.pt'))
def save_link_partitions(split_idx, data, dataset_name, num_parts, save_dir):
edge_type = data.edge_types[0]
for key, idx in split_idx.items():
print(f'-- Partitioning {key} indices ...')
edge_index = data[edge_type].edge_index[:, idx]
edge_index = edge_index.split(edge_index.size(1) // num_parts, dim=1)
label = data[edge_type].edge_label[idx]
label = label.split(label.size(0) // num_parts)
edge_time = data[edge_type].time[idx]
edge_time = edge_time.split(edge_time.size(0) // num_parts)
part_dir = osp.join(save_dir, f'{dataset_name}-{key}-partitions')
os.makedirs(part_dir, exist_ok=True)
for i in range(num_parts):
partition = {
'edge_label_index': edge_index[i],
'edge_label': label[i],
'edge_label_time': edge_time[i] - 1,
}
torch.save(partition, osp.join(part_dir, f'partition{i}.pt'))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
add = parser.add_argument
add('--dataset', type=str,
choices=['ogbn-mag', 'ogbn-products', 'MovieLens',
'Reddit'], default='ogbn-products')
add('--root_dir', default='../../../data', type=str)
add('--num_partitions', type=int, default=2)
add('--recursive', action='store_true')
# TODO (kgajdamo) Add support for arguments below:
# add('--use-sparse-tensor', action='store_true')
# add('--bf16', action='store_true')
args = parser.parse_args()
partition_dataset(args.dataset, args.root_dir, args.num_partitions,
args.recursive)
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