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import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional as F
from torch import Tensor
from torch.nn.parallel import DistributedDataParallel
from torch_geometric.datasets import Reddit
from torch_geometric.loader import NeighborLoader
from torch_geometric.nn import SAGEConv
class SAGE(torch.nn.Module):
def __init__(self, in_channels: int, hidden_channels: int,
out_channels: int, num_layers: int = 2):
super().__init__()
self.convs = torch.nn.ModuleList()
self.convs.append(SAGEConv(in_channels, hidden_channels))
for _ in range(num_layers - 2):
self.convs.append(SAGEConv(hidden_channels, hidden_channels))
self.convs.append(SAGEConv(hidden_channels, out_channels))
def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
for i, conv in enumerate(self.convs):
x = conv(x, edge_index)
if i < len(self.convs) - 1:
x = x.relu()
x = F.dropout(x, p=0.5, training=self.training)
return x
@torch.no_grad()
def test(loader, model, rank):
model.eval()
total_correct = total_examples = 0
for i, batch in enumerate(loader):
out = model(batch.x, batch.edge_index.to(rank))
pred = out[:batch.batch_size].argmax(dim=-1)
y = batch.y[:batch.batch_size].to(rank)
total_correct += int((pred == y).sum())
total_examples += batch.batch_size
return torch.tensor(total_correct / total_examples, device=rank)
def run(rank, world_size, dataset):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
dist.init_process_group('nccl', rank=rank, world_size=world_size)
data = dataset[0]
data = data.to(rank, 'x', 'y') # Move to device for faster feature fetch.
# Split indices into `world_size` many chunks:
train_idx = data.train_mask.nonzero(as_tuple=False).view(-1)
train_idx = train_idx.split(train_idx.size(0) // world_size)[rank]
val_idx = data.val_mask.nonzero(as_tuple=False).view(-1)
val_idx = val_idx.split(val_idx.size(0) // world_size)[rank]
test_idx = data.val_mask.nonzero(as_tuple=False).view(-1)
test_idx = test_idx.split(test_idx.size(0) // world_size)[rank]
kwargs = dict(
data=data,
batch_size=1024,
num_neighbors=[25, 10],
drop_last=True,
num_workers=4,
persistent_workers=True,
)
train_loader = NeighborLoader(
input_nodes=train_idx,
shuffle=True,
**kwargs,
)
val_loader = NeighborLoader(
input_nodes=val_idx,
shuffle=False,
**kwargs,
)
test_loader = NeighborLoader(
input_nodes=test_idx,
shuffle=False,
**kwargs,
)
torch.manual_seed(12345)
model = SAGE(dataset.num_features, 256, dataset.num_classes).to(rank)
model = DistributedDataParallel(model, device_ids=[rank])
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
for epoch in range(1, 21):
model.train()
for batch in train_loader:
optimizer.zero_grad()
out = model(batch.x, batch.edge_index.to(rank))[:batch.batch_size]
loss = F.cross_entropy(out, batch.y[:batch.batch_size])
loss.backward()
optimizer.step()
dist.barrier()
if rank == 0:
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}')
if epoch % 5 == 0:
train_acc = test(train_loader, model, rank)
val_acc = test(val_loader, model, rank)
test_acc = test(test_loader, model, rank)
if world_size > 1:
dist.all_reduce(train_acc, op=dist.ReduceOp.SUM)
dist.all_reduce(train_acc, op=dist.ReduceOp.SUM)
dist.all_reduce(train_acc, op=dist.ReduceOp.SUM)
train_acc /= world_size
val_acc /= world_size
test_acc /= world_size
if rank == 0:
print(f'Train: {train_acc:.4f}, Val: {val_acc:.4f}, '
f'Test: {test_acc:.4f}')
dist.barrier()
dist.destroy_process_group()
if __name__ == '__main__':
dataset = Reddit('../../data/Reddit')
world_size = torch.cuda.device_count()
print("Let's use", world_size, "GPUs!")
mp.spawn(run, args=(world_size, dataset), nprocs=world_size, join=True)
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