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torch_native.py
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torch_native.py
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import argparse
import torch
import torch.distributed as dist
from torch.multiprocessing import start_processes
from torch.nn import NLLLoss
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import SGD
from torch.utils.data import Dataset
from torchvision.models import wide_resnet50_2
class RndDataset(Dataset):
def __init__(self, nb_samples=128):
self._nb_samples = nb_samples
def __len__(self):
return self._nb_samples
def __getitem__(self, index):
x = torch.randn((3, 32, 32))
y = torch.randint(0, 100, (1,)).item()
return x, y
def training(rank, world_size, backend, config):
# Specific torch.distributed
dist.init_process_group(
backend, init_method="tcp://0.0.0.0:2233", world_size=world_size, rank=rank
)
print(dist.get_rank(), ": run with config:", config, " - backend=", backend)
torch.cuda.set_device(rank)
# Data preparation
dataset = RndDataset(nb_samples=config["nb_samples"])
# Specific torch.distributed
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
train_loader = torch.utils.data.DataLoader(
dataset,
batch_size=int(config["batch_size"] / world_size),
num_workers=1,
sampler=train_sampler,
)
# Model, criterion, optimizer setup
model = wide_resnet50_2(num_classes=100).cuda()
criterion = NLLLoss()
optimizer = SGD(model.parameters(), lr=0.01)
# Specific torch.distributed
model = DDP(model, device_ids=[rank])
# Training loop log param
log_interval = config["log_interval"]
def _train_step(batch_idx, data, target):
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = model(data)
# Add a softmax layer
probabilities = torch.nn.functional.softmax(output, dim=0)
loss_val = criterion(probabilities, target)
loss_val.backward()
optimizer.step()
if (batch_idx + 1) % (log_interval) == 0:
print(
"Process {}/{} Train Epoch: {} [{}/{}]\tLoss: {}".format(
dist.get_rank(),
dist.get_world_size(),
epoch,
(batch_idx + 1) * len(data),
len(train_sampler),
loss_val.item(),
)
)
return loss_val
# Running _train_step for n_epochs
n_epochs = 1
for epoch in range(n_epochs):
for batch_idx, (data, target) in enumerate(train_loader):
_train_step(batch_idx, data, target)
# Specific torch.distributed
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser("Torch Native - DDP")
parser.add_argument("--backend", type=str, default="nccl")
parser.add_argument("--nproc_per_node", type=int, default=2)
parser.add_argument("--log_interval", type=int, default=4)
parser.add_argument("--nb_samples", type=int, default=128)
parser.add_argument("--batch_size", type=int, default=16)
args_parsed = parser.parse_args()
assert dist.is_available()
if args_parsed.backend == "nccl":
assert torch.cuda.is_available()
assert dist.is_nccl_available()
elif args_parsed.backend == "gloo":
assert dist.is_gloo_available()
else:
raise ValueError(
f"unvalid backend `{args_parsed.backend}` (valid: `gloo` or `nccl`)"
)
config = {
"log_interval": args_parsed.log_interval,
"batch_size": args_parsed.batch_size,
"nb_samples": args_parsed.nb_samples,
}
args = (args_parsed.nproc_per_node, args_parsed.backend, config)
# Specific torch.distributed
start_processes(
training, args=args, nprocs=args_parsed.nproc_per_node, start_method="spawn"
)