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# This script shows how to use Quiver in an existing PyG example:
# https://github.com/pyg-team/pytorch_geometric/blob/master/examples/multi_gpu/distributed_sampling.py
import os
import quiver
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel
from tqdm import tqdm
from torch_geometric.datasets import Reddit
from torch_geometric.loader import NeighborSampler
from torch_geometric.nn import SAGEConv
class SAGE(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels,
num_layers=2):
super().__init__()
self.num_layers = num_layers
self.convs = torch.nn.ModuleList()
self.convs.append(SAGEConv(in_channels, hidden_channels))
for _ in range(self.num_layers - 2):
self.convs.append(SAGEConv(hidden_channels, hidden_channels))
self.convs.append(SAGEConv(hidden_channels, out_channels))
def forward(self, x, adjs):
for i, (edge_index, _, size) in enumerate(adjs):
x_target = x[:size[1]] # Target nodes are always placed first.
x = self.convs[i]((x, x_target), edge_index)
if i != self.num_layers - 1:
x = F.relu(x)
x = F.dropout(x, p=0.5, training=self.training)
return x.log_softmax(dim=-1)
@torch.no_grad()
def inference(self, x_all, device, subgraph_loader):
pbar = tqdm(total=x_all.size(0) * self.num_layers)
pbar.set_description('Evaluating')
for i in range(self.num_layers):
xs = []
for batch_size, n_id, adj in subgraph_loader:
edge_index, _, size = adj.to(device)
x = x_all[n_id].to(device)
x_target = x[:size[1]]
x = self.convs[i]((x, x_target), edge_index)
if i != self.num_layers - 1:
x = F.relu(x)
xs.append(x.cpu())
pbar.update(batch_size)
x_all = torch.cat(xs, dim=0)
pbar.close()
return x_all
def run(rank, world_size, dataset, quiver_feature, quiver_sampler):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
dist.init_process_group('nccl', rank=rank, world_size=world_size)
torch.cuda.set_device(rank)
data = dataset[0]
train_idx = data.train_mask.nonzero(as_tuple=False).view(-1)
train_idx = train_idx.split(train_idx.size(0) // world_size)[rank]
train_loader = torch.utils.data.DataLoader(train_idx, batch_size=1024,
shuffle=True, num_workers=0)
if rank == 0:
subgraph_loader = NeighborSampler(data.edge_index, node_idx=None,
sizes=[-1], batch_size=2048,
shuffle=False, num_workers=6)
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=0.01)
y = data.y.to(rank)
for epoch in range(1, 21):
model.train()
for seeds in train_loader:
n_id, batch_size, adjs = quiver_sampler.sample(seeds)
adjs = [adj.to(rank) for adj in adjs]
optimizer.zero_grad()
out = model(quiver_feature[n_id].to(rank), adjs)
loss = F.nll_loss(out, y[n_id[:batch_size]])
loss.backward()
optimizer.step()
dist.barrier()
if rank == 0:
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')
if rank == 0 and epoch % 5 == 0: # We evaluate on a single GPU for now
model.eval()
with torch.no_grad():
out = model.module.inference(quiver_feature, rank,
subgraph_loader)
res = out.argmax(dim=-1) == data.y
acc1 = int(res[data.train_mask].sum()) / int(data.train_mask.sum())
acc2 = int(res[data.val_mask].sum()) / int(data.val_mask.sum())
acc3 = int(res[data.test_mask].sum()) / int(data.test_mask.sum())
print(f'Train: {acc1:.4f}, Val: {acc2:.4f}, Test: {acc3:.4f}')
dist.barrier()
dist.destroy_process_group()
if __name__ == '__main__':
dataset = Reddit('../../data/Reddit')
data = dataset[0]
world_size = torch.cuda.device_count()
print('Let\'s use', world_size, 'GPUs!')
########################################################################
# The below code enable Quiver for PyG.
# Please refer to: https://torch-quiver.readthedocs.io/en/latest/api/ for
# how to configure the CSRTopo, Sampler and Feature of Quiver.
########################################################################
csr_topo = quiver.CSRTopo(data.edge_index) # Quiver
quiver_sampler = quiver.pyg.GraphSageSampler(csr_topo, [25, 10], 0,
mode='GPU') # Quiver
quiver_feature = quiver.Feature(rank=0,
device_list=list(range(world_size)),
device_cache_size="2G",
cache_policy="device_replicate",
csr_topo=csr_topo) # Quiver
quiver_feature.from_cpu_tensor(data.x) # Quiver
mp.spawn(run, args=(world_size, dataset, quiver_feature, quiver_sampler),
nprocs=world_size, join=True)
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