1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
|
import os
import os.path as osp
from math import ceil
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 tqdm import tqdm
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,
) -> None:
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: NeighborLoader,
model: DistributedDataParallel,
rank: int,
) -> Tensor:
model.eval()
total_correct = torch.tensor(0, dtype=torch.long, device=rank)
total_examples = 0
for batch in 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 += (pred == y).sum()
total_examples += batch.batch_size
return total_correct / total_examples
def run(rank: int, world_size: int, dataset: Reddit) -> None:
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(ceil(train_idx.size(0) / world_size))[rank]
val_idx = data.val_mask.nonzero(as_tuple=False).view(-1)
val_idx = val_idx.split(ceil(val_idx.size(0) / world_size))[rank]
test_idx = data.val_mask.nonzero(as_tuple=False).view(-1)
test_idx = test_idx.split(ceil(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 tqdm(
train_loader,
desc=f'Epoch {epoch:02d}',
disable=rank != 0,
):
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()
optimizer.zero_grad()
if rank == 0:
print(f'Epoch {epoch:02d}: Train 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.AVG)
dist.all_reduce(val_acc, op=dist.ReduceOp.AVG)
dist.all_reduce(test_acc, op=dist.ReduceOp.AVG)
if rank == 0:
print(f'Train acc: {train_acc:.4f}, '
f'Val acc: {val_acc:.4f}, '
f'Test acc: {test_acc:.4f}')
dist.destroy_process_group()
if __name__ == '__main__':
path = osp.join(
osp.dirname(__file__),
'..',
'..',
'data',
'Reddit',
)
dataset = Reddit(path)
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)
|
