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import argparse
import os
import os.path as osp
import tempfile
import cupy
import rmm
import torch
from rmm.allocators.cupy import rmm_cupy_allocator
from rmm.allocators.torch import rmm_torch_allocator
# Must change allocators immediately upon import
# or else other imports will cause memory to be
# allocated and prevent changing the allocator
rmm.reinitialize(devices=[0], pool_allocator=True, managed_memory=True)
cupy.cuda.set_allocator(rmm_cupy_allocator)
torch.cuda.memory.change_current_allocator(rmm_torch_allocator)
import cugraph_pyg # noqa
import torch.nn.functional as F # noqa
# Enable cudf spilling to save gpu memory
from cugraph.testing.mg_utils import enable_spilling # noqa
from cugraph_pyg.loader import NeighborLoader # noqa
enable_spilling()
from ogb.nodeproppred import PygNodePropPredDataset # noqa
from tqdm import tqdm # noqa
from torch_geometric.nn import SAGEConv # noqa
from torch_geometric.utils import to_undirected # noqa
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--num_layers', type=int, default=3)
parser.add_argument('--batch_size', type=int, default=1024)
parser.add_argument('--num_neighbors', type=int, default=10)
parser.add_argument('--channels', type=int, default=256)
parser.add_argument('--lr', type=float, default=0.003)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--num_workers', type=int, default=12)
parser.add_argument('--root', type=str, default=None)
parser.add_argument('--tempdir_root', type=str, default=None)
args = parser.parse_args()
root = args.root
if root is None:
root = osp.dirname(osp.realpath(__file__))
root = osp.join(root, '..', 'data', 'papers100')
dataset = PygNodePropPredDataset('ogbn-papers100M', root)
split_idx = dataset.get_idx_split()
data = dataset[0]
data.edge_index = to_undirected(data.edge_index, reduce="mean")
graph_store = cugraph_pyg.data.GraphStore()
graph_store[dict(
edge_type=('node', 'rel', 'node'),
layout='coo',
is_sorted=False,
size=(data.num_nodes, data.num_nodes),
)] = data.edge_index
feature_store = cugraph_pyg.data.TensorDictFeatureStore()
feature_store['node', 'x'] = data.x
feature_store['node', 'y'] = data.y
data = (feature_store, graph_store)
class SAGE(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.convs = torch.nn.ModuleList()
self.convs.append(SAGEConv(in_channels, args.channels))
for _ in range(args.num_layers - 2):
self.convs.append(SAGEConv(args.channels, args.channels))
self.convs.append(SAGEConv(args.channels, out_channels))
def forward(self, x, edge_index):
for i, conv in enumerate(self.convs):
x = conv(x, edge_index)
if i != args.num_layers - 1:
x = x.relu()
x = F.dropout(x, p=args.dropout, training=self.training)
return x
model = SAGE(dataset.num_features, dataset.num_classes).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
def create_loader(
data,
num_neighbors,
input_nodes,
replace,
batch_size,
samples_dir,
stage_name,
shuffle=False,
):
directory = osp.join(samples_dir, stage_name)
os.mkdir(directory)
return NeighborLoader(
data,
num_neighbors=num_neighbors,
input_nodes=input_nodes,
replace=replace,
batch_size=batch_size,
directory=directory,
shuffle=shuffle,
)
with tempfile.TemporaryDirectory(dir=args.tempdir_root) as samples_dir:
loader_kwargs = dict(
data=data,
num_neighbors=[args.num_neighbors] * args.num_layers,
replace=False,
batch_size=args.batch_size,
samples_dir=samples_dir,
)
train_loader = create_loader(
input_nodes=split_idx['train'],
stage_name='train',
shuffle=True,
**loader_kwargs,
)
val_loader = create_loader(
input_nodes=split_idx['valid'],
stage_name='val',
**loader_kwargs,
)
test_loader = create_loader(
input_nodes=split_idx['test'],
stage_name='test',
**loader_kwargs,
)
def train():
model.train()
total_loss = total_correct = total_examples = 0
for i, batch in enumerate(train_loader):
batch = batch.cuda()
optimizer.zero_grad()
out = model(batch.x, batch.edge_index)[:batch.batch_size]
y = batch.y[:batch.batch_size].view(-1).to(torch.long)
loss = F.cross_entropy(out, y)
loss.backward()
optimizer.step()
total_loss += float(loss) * y.size(0)
total_correct += int(out.argmax(dim=-1).eq(y).sum())
total_examples += y.size(0)
if i % 10 == 0:
print(f"Epoch: {epoch:02d}, Iteration: {i}, Loss: {loss:.4f}")
return total_loss / total_examples, total_correct / total_examples
@torch.no_grad()
def test(loader):
model.eval()
total_correct = total_examples = 0
for batch in loader:
batch = batch.cuda()
out = model(batch.x, batch.edge_index)[:batch.batch_size]
y = batch.y[:batch.batch_size].view(-1).to(torch.long)
total_correct += int(out.argmax(dim=-1).eq(y).sum())
total_examples += y.size(0)
return total_correct / total_examples
for epoch in range(1, args.epochs + 1):
loss, train_acc = train()
print(f'Epoch {epoch:02d}, Loss: {loss:.4f}, Train: {train_acc:.4f}')
val_acc = test(val_loader)
print(f'Epoch {epoch:02d}, Val: {val_acc:.4f}')
test_acc = test(test_loader)
print(f'Epoch {epoch:02d}, Test: {test_acc:.4f}')
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