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"""Multi-node multi-GPU example on ogbn-papers100m.
Example way to run using srun:
srun -l -N<num_nodes> --ntasks-per-node=<ngpu_per_node> \
--container-name=cont --container-image=<image_url> \
--container-mounts=/ogb-papers100m/:/workspace/dataset
python3 path_to_script.py
"""
import os
import time
from typing import Optional
import torch
import torch.distributed as dist
import torch.nn.functional as F
from ogb.nodeproppred import PygNodePropPredDataset
from torch.nn.parallel import DistributedDataParallel
from torchmetrics import Accuracy
from torch_geometric.loader import NeighborLoader
from torch_geometric.nn import GCN
def get_num_workers() -> int:
num_workers = None
if hasattr(os, "sched_getaffinity"):
try:
num_workers = len(os.sched_getaffinity(0)) // 2
except Exception:
pass
if num_workers is None:
num_workers = os.cpu_count() // 2
return num_workers
def run(world_size, data, split_idx, model, acc, wall_clock_start):
local_id = int(os.environ['LOCAL_RANK'])
rank = torch.distributed.get_rank()
torch.cuda.set_device(local_id)
device = torch.device(local_id)
if rank == 0:
print(f'Using {nprocs} GPUs...')
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 = DistributedDataParallel(model.to(device), device_ids=[local_id])
optimizer = torch.optim.Adam(model.parameters(), lr=0.001,
weight_decay=5e-4)
kwargs = dict(
data=data,
batch_size=1024,
num_workers=get_num_workers(),
num_neighbors=[30, 30],
)
train_loader = NeighborLoader(
input_nodes=split_idx['train'],
shuffle=True,
drop_last=True,
**kwargs,
)
val_loader = NeighborLoader(input_nodes=split_idx['valid'], **kwargs)
test_loader = NeighborLoader(input_nodes=split_idx['test'], **kwargs)
val_steps = 1000
warmup_steps = 100
acc = acc.to(device)
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(1, 21):
model.train()
for i, batch in enumerate(train_loader):
if i == warmup_steps:
torch.cuda.synchronize()
start = time.time()
batch = batch.to(device)
optimizer.zero_grad()
y = batch.y[:batch.batch_size].view(-1).to(torch.long)
out = model(batch.x, batch.edge_index)[:batch.batch_size]
loss = F.cross_entropy(out, y)
loss.backward()
optimizer.step()
if rank == 0 and i % 10 == 0:
print(f'Epoch: {epoch:02d}, Iteration: {i}, Loss: {loss:.4f}')
dist.barrier()
torch.cuda.synchronize()
if rank == 0:
sec_per_iter = (time.time() - start) / (i + 1 - warmup_steps)
print(f"Avg Training Iteration Time: {sec_per_iter:.6f} s/iter")
@torch.no_grad()
def test(loader: NeighborLoader, num_steps: Optional[int] = None):
model.eval()
for j, batch in enumerate(loader):
if num_steps is not None and j >= num_steps:
break
batch = batch.to(device)
out = model(batch.x, batch.edge_index)[:batch.batch_size]
y = batch.y[:batch.batch_size].view(-1).to(torch.long)
acc(out, y)
acc_sum = acc.compute()
return acc_sum
eval_acc = test(val_loader, num_steps=val_steps)
if rank == 0:
print(f"Val Accuracy: {eval_acc:.4f}%", )
acc.reset()
dist.barrier()
test_acc = test(test_loader)
if rank == 0:
print(f"Test Accuracy: {test_acc:.4f}%", )
dist.barrier()
acc.reset()
torch.cuda.synchronize()
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__':
wall_clock_start = time.perf_counter()
# Setup multi-node:
torch.distributed.init_process_group("nccl")
nprocs = dist.get_world_size()
assert dist.is_initialized(), "Distributed cluster not initialized"
dataset = PygNodePropPredDataset(name='ogbn-papers100M')
split_idx = dataset.get_idx_split()
model = GCN(dataset.num_features, 256, 2, dataset.num_classes)
acc = Accuracy(task="multiclass", num_classes=dataset.num_classes)
data = dataset[0]
data.y = data.y.reshape(-1)
run(nprocs, data, split_idx, model, acc, wall_clock_start)
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