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Add ondisk dataset to PCQM4M_v2 example #474

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Add ondisk dataset to PCQM4M_v2 example #474

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41 changes: 31 additions & 10 deletions examples/lsc/pcqm4m-v2/main_gnn_multi_gpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,8 @@
import argparse
import time

from torch_geometric.datasets import PCQM4Mv2

### importing OGB-LSC
try:
from ogb.lsc import PygPCQM4Mv2Dataset, PCQM4Mv2Evaluator
Expand Down Expand Up @@ -103,26 +105,41 @@ def run(rank, dataset, args):
os.environ["MASTER_PORT"] = "12355"
dist.init_process_group("nccl", rank=rank, world_size=num_devices)

split_idx = dataset.get_idx_split()
if args.on_disk_dataset:
train_idx = torch.arange(len(dataset.indices()))
else:
split_idx = dataset.get_idx_split()
train_idx = split_idx["train"]

train_idx = split_idx["train"]

if num_devices > 1:
train_idx = train_idx.split(train_idx.size(0) // num_devices)[rank]

if num_devices > 1:
train_idx = train_idx.split(train_idx.size(0) // num_devices)[rank]
if args.train_subset:
subset_ratio = 0.1
subset_idx = torch.randperm(len(train_idx))[:int(subset_ratio*len(split_idx["train"]))]
train_loader = DataLoader(dataset[train_idx[subset_idx]], batch_size=args.batch_size, shuffle=True, num_workers = args.num_workers)
subset_idx = torch.randperm(len(train_idx))[:int(subset_ratio*len(train_idx))]
train_dataset = dataset[train_idx[subset_idx]]
else:
train_loader = DataLoader(dataset[train_idx], batch_size=args.batch_size, shuffle=True, num_workers = args.num_workers)
train_dataset = dataset[train_idx]

if rank == 0:
valid_loader = DataLoader(dataset[split_idx["valid"]], batch_size=args.batch_size, shuffle=False, num_workers = args.num_workers)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers = args.num_workers)

if rank == 0:
if args.on_disk_dataset:
valid_dataset = PCQM4Mv2(root = 'on_disk_dataset/', split="val")
test_dev_dataset = PCQM4Mv2(root = 'on_disk_dataset/', split="test")
test_challenge_dataset = PCQM4Mv2(root = 'on_disk_dataset/', split="holdout")
else:
valid_dataset = dataset[split_idx["valid"]]
test_dev_dataset = dataset[split_idx["test-dev"]]
test_challenge_dataset = dataset[split_idx["test-challenge"]]

valid_loader = DataLoader(valid_dataset, batch_size=args.batch_size, shuffle=False, num_workers = args.num_workers)
if args.save_test_dir != '':
testdev_loader = DataLoader(dataset[split_idx["test-dev"]], batch_size=args.batch_size, shuffle=False, num_workers = args.num_workers)
testchallenge_loader = DataLoader(dataset[split_idx["test-challenge"]], batch_size=args.batch_size, shuffle=False, num_workers = args.num_workers)
testdev_loader = DataLoader(test_dev_dataset, batch_size=args.batch_size, shuffle=False, num_workers = args.num_workers)
testchallenge_loader = DataLoader(test_challenge_dataset, batch_size=args.batch_size, shuffle=False, num_workers = args.num_workers)

if args.checkpoint_dir != '':
os.makedirs(args.checkpoint_dir, exist_ok = True)
Expand Down Expand Up @@ -267,6 +284,7 @@ def run(rank, dataset, args):
parser.add_argument('--checkpoint_dir', type=str, default = '', help='directory to save checkpoint')
parser.add_argument('--save_test_dir', type=str, default = '', help='directory to save test submission file')
parser.add_argument('--num_devices', type=int, default='0', help="Number of GPUs, if 0 runs on the CPU")
parser.add_argument('--on_disk_dataset', action='store_true')
args = parser.parse_args()

print(args)
Expand All @@ -277,7 +295,10 @@ def run(rank, dataset, args):
assert args.num_devices <= available_gpus, f"Cannot train with {args.num_devices} GPUs: available GPUs count {available_gpus}"

### automatic dataloading and splitting
dataset = PygPCQM4Mv2Dataset(root = 'dataset/')
if args.on_disk_dataset:
dataset = PCQM4Mv2(root = 'on_disk_dataset/', split='train')
else:
dataset = PygPCQM4Mv2Dataset(root = 'dataset/')

if args.num_devices > 1:
print(f'Starting multi-GPU training with DDP with {args.num_devices} GPUs')
Expand Down