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import argparse
import os
import tempfile
import time
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional as F
from ogb.nodeproppred import PygNodePropPredDataset
from torch.nn.parallel import DistributedDataParallel
from torchmetrics import Accuracy
import torch_geometric
from torch_geometric.loader import NeighborLoader
def get_num_workers(world_size):
num_work = None
if hasattr(os, "sched_getaffinity"):
try:
num_work = len(os.sched_getaffinity(0)) / (2 * world_size)
except Exception:
pass
if num_work is None:
num_work = os.cpu_count() / (2 * world_size)
return int(num_work)
def run_train(rank, data, world_size, model, epochs, batch_size, fan_out,
split_idx, num_classes, wall_clock_start, tempdir=None,
num_layers=3):
# init pytorch worker
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
dist.init_process_group('nccl', rank=rank, world_size=world_size)
if world_size > 1:
split_idx['train'] = split_idx['train'].split(
split_idx['train'].size(0) // world_size, dim=0)[rank].clone()
split_idx['valid'] = split_idx['valid'].split(
split_idx['valid'].size(0) // world_size, dim=0)[rank].clone()
split_idx['test'] = split_idx['test'].split(
split_idx['test'].size(0) // world_size, dim=0)[rank].clone()
model = model.to(rank)
model = DistributedDataParallel(model, device_ids=[rank])
optimizer = torch.optim.Adam(model.parameters(), lr=0.01,
weight_decay=0.0005)
kwargs = dict(
num_neighbors=[fan_out] * num_layers,
batch_size=batch_size,
)
num_work = get_num_workers(world_size)
train_loader = NeighborLoader(data, input_nodes=split_idx['train'],
num_workers=num_work, shuffle=True,
drop_last=True, **kwargs)
val_loader = NeighborLoader(data, input_nodes=split_idx['valid'],
num_workers=num_work, **kwargs)
test_loader = NeighborLoader(data, input_nodes=split_idx['test'],
num_workers=num_work, **kwargs)
eval_steps = 1000
warmup_steps = 20
acc = Accuracy(task="multiclass", num_classes=num_classes).to(rank)
dist.barrier()
torch.cuda.synchronize()
if rank == 0:
prep_time = round(time.perf_counter() - wall_clock_start, 2)
print("Total time before training begins (prep_time) =", prep_time,
"seconds")
print("Beginning training...")
for epoch in range(epochs):
for i, batch in enumerate(train_loader):
if i == warmup_steps:
torch.cuda.synchronize()
start = time.time()
batch = batch.to(rank)
batch_size = batch.num_sampled_nodes[0]
batch.y = batch.y.to(torch.long)
optimizer.zero_grad()
out = model(batch.x, batch.edge_index)
loss = F.cross_entropy(out[:batch_size], batch.y[:batch_size])
loss.backward()
optimizer.step()
if rank == 0 and i % 10 == 0:
print("Epoch: " + str(epoch) + ", Iteration: " + str(i) +
", Loss: " + str(loss))
nb = i + 1.0
dist.barrier()
torch.cuda.synchronize()
if rank == 0:
print("Average Training Iteration Time:",
(time.time() - start) / (nb - warmup_steps), "s/iter")
with torch.no_grad():
for i, batch in enumerate(val_loader):
if i >= eval_steps:
break
batch = batch.to(rank)
batch_size = batch.num_sampled_nodes[0]
batch.y = batch.y.to(torch.long)
out = model(batch.x, batch.edge_index)
acc_i = acc( # noqa
out[:batch_size].softmax(dim=-1), batch.y[:batch_size])
acc_sum = acc.compute()
if rank == 0:
print(f"Validation Accuracy: {acc_sum * 100.0:.4f}%", )
dist.barrier()
acc.reset()
with torch.no_grad():
for i, batch in enumerate(test_loader):
batch = batch.to(rank)
batch_size = batch.num_sampled_nodes[0]
batch.y = batch.y.to(torch.long)
out = model(batch.x, batch.edge_index)
acc_i = acc( # noqa
out[:batch_size].softmax(dim=-1), batch.y[:batch_size])
acc_sum = acc.compute()
if rank == 0:
print(f"Test Accuracy: {acc_sum * 100.0:.4f}%", )
dist.barrier()
acc.reset()
if rank == 0:
total_time = round(time.perf_counter() - wall_clock_start, 2)
print("Total Program Runtime (total_time) =", total_time, "seconds")
print("total_time - prep_time =", total_time - prep_time, "seconds")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--hidden_channels', type=int, default=256)
parser.add_argument('--num_layers', type=int, default=2)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--epochs', type=int, default=20)
parser.add_argument('--batch_size', type=int, default=1024)
parser.add_argument('--fan_out', type=int, default=30)
parser.add_argument(
"--use_gat_conv",
action='store_true',
help="Whether or not to use GATConv. (Defaults to using GCNConv)",
)
parser.add_argument(
"--n_gat_conv_heads",
type=int,
default=4,
help="If using GATConv, number of attention heads to use",
)
parser.add_argument(
"--n_devices", type=int, default=-1,
help="1-8 to use that many GPUs. Defaults to all available GPUs")
args = parser.parse_args()
wall_clock_start = time.perf_counter()
if args.n_devices == -1:
world_size = torch.cuda.device_count()
else:
world_size = args.n_devices
import psutil
gb_ram_needed = 190 + 200 * world_size
if (psutil.virtual_memory().total / (1024**3)) < gb_ram_needed:
print("Warning: may not have enough RAM to use this many GPUs.")
print("Consider upgrading RAM or using less GPUs if an error occurs.")
print("Estimated RAM Needed: ~" + str(gb_ram_needed))
print('Let\'s use', world_size, 'GPUs!')
dataset = PygNodePropPredDataset(name='ogbn-papers100M',
root='/datasets/ogb_datasets')
split_idx = dataset.get_idx_split()
data = dataset[0]
data.y = data.y.reshape(-1)
if args.use_gat_conv:
model = torch_geometric.nn.models.GAT(dataset.num_features,
args.hidden_channels,
args.num_layers,
dataset.num_classes,
heads=args.n_gat_conv_heads)
else:
model = torch_geometric.nn.models.GCN(
dataset.num_features,
args.hidden_channels,
args.num_layers,
dataset.num_classes,
)
print("Data =", data)
with tempfile.TemporaryDirectory() as tempdir:
if world_size > 1:
mp.spawn(
run_train,
args=(data, world_size, model, args.epochs, args.batch_size,
args.fan_out, split_idx, dataset.num_classes,
wall_clock_start, tempdir, args.num_layers),
nprocs=world_size, join=True)
else:
run_train(0, data, world_size, model, args.epochs, args.batch_size,
args.fan_out, split_idx, dataset.num_classes,
wall_clock_start, tempdir, args.num_layers)
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