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Adding torch accelerator to ddp-tutorial-series example #1376

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19 changes: 15 additions & 4 deletions distributed/ddp-tutorial-series/multigpu.py
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Expand Up @@ -17,9 +17,20 @@ def ddp_setup(rank, world_size):
world_size: Total number of processes
"""
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
torch.cuda.set_device(rank)
init_process_group(backend="nccl", rank=rank, world_size=world_size)
os.environ["MASTER_PORT"] = "12455"
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It's still different port number.



rank = int(os.environ["LOCAL_RANK"])
if torch.accelerator.is_available():
device_type = torch.accelerator.current_accelerator()
device = torch.device(f"{device_type}:{rank}")
torch.accelerator.device_index(rank)
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There is no such API device_index() in 2.7: https://docs.pytorch.org/docs/stable/accelerator.html

What is it doing? You did set index 2 lines above...

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Ok, device_index() will appear only in 2.8: https://docs.pytorch.org/docs/main/generated/torch.accelerator.device_index.html#torch.accelerator.device_index. And this is a context manager, i.e. you need to use it as with device_index(). I don't see why you are using it here. And recently merged #1375 attempts to do the same. I think it will need a fix as well.

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It does not make sense to call context manager without with. Did you intend to call set_device_index() instead?

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yes, I'm making the changes, thanks

print(f"Running on rank {rank} on device {device}")
else:
device = torch.device("cpu")
print(f"Running on device {device}")

backend = torch.distributed.get_default_backend_for_device(device)

class Trainer:
def __init__(
Expand Down Expand Up @@ -100,5 +111,5 @@ def main(rank: int, world_size: int, save_every: int, total_epochs: int, batch_s
parser.add_argument('--batch_size', default=32, type=int, help='Input batch size on each device (default: 32)')
args = parser.parse_args()

world_size = torch.cuda.device_count()
world_size = torch.accelerator.device_count()
mp.spawn(main, args=(world_size, args.save_every, args.total_epochs, args.batch_size), nprocs=world_size)
24 changes: 19 additions & 5 deletions distributed/ddp-tutorial-series/multigpu_torchrun.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,20 @@


def ddp_setup():
torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
init_process_group(backend="nccl")
rank = int(os.environ["LOCAL_RANK"])
if torch.accelerator.is_available():
device_type = torch.accelerator.current_accelerator()
device = torch.device(f"{device_type}:{rank}")
torch.accelerator.device_index(rank)
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same as above

print(f"Running on rank {rank} on device {device}")
else:
device = torch.device("cpu")
print(f"Running on device {device}")

backend = torch.distributed.get_default_backend_for_device(device)
torch.distributed.init_process_group(backend=backend, device_id=device)
return device


class Trainer:
def __init__(
Expand All @@ -22,6 +34,7 @@ def __init__(
optimizer: torch.optim.Optimizer,
save_every: int,
snapshot_path: str,
device
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would be nice to have type designation here:

Suggested change
device
device: torch.device,

) -> None:
self.gpu_id = int(os.environ["LOCAL_RANK"])
self.model = model.to(self.gpu_id)
Expand All @@ -30,14 +43,15 @@ def __init__(
self.save_every = save_every
self.epochs_run = 0
self.snapshot_path = snapshot_path
self.device = device
if os.path.exists(snapshot_path):
print("Loading snapshot")
self._load_snapshot(snapshot_path)

self.model = DDP(self.model, device_ids=[self.gpu_id])

def _load_snapshot(self, snapshot_path):
loc = f"cuda:{self.gpu_id}"
loc = str(self.device)
snapshot = torch.load(snapshot_path, map_location=loc)
self.model.load_state_dict(snapshot["MODEL_STATE"])
self.epochs_run = snapshot["EPOCHS_RUN"]
Expand Down Expand Up @@ -92,10 +106,10 @@ def prepare_dataloader(dataset: Dataset, batch_size: int):


def main(save_every: int, total_epochs: int, batch_size: int, snapshot_path: str = "snapshot.pt"):
ddp_setup()
device = ddp_setup()
dataset, model, optimizer = load_train_objs()
train_data = prepare_dataloader(dataset, batch_size)
trainer = Trainer(model, train_data, optimizer, save_every, snapshot_path)
trainer = Trainer(model, train_data, optimizer, save_every, snapshot_path, device)
trainer.train(total_epochs)
destroy_process_group()

Expand Down
24 changes: 19 additions & 5 deletions distributed/ddp-tutorial-series/multinode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,20 @@


def ddp_setup():
torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
init_process_group(backend="nccl")
rank = int(os.environ["LOCAL_RANK"])
if torch.accelerator.is_available():
device_type = torch.accelerator.current_accelerator()
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same comments as above

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not addressed.

device: torch.device = torch.device(f"{device_type}:{rank}")
torch.accelerator.device_index(rank)
print(f"Running on rank {rank} on device {device}")
backend = torch.distributed.get_default_backend_for_device(device)
torch.distributed.init_process_group(backend=backend)
return device_type
else:
device = torch.device("cpu")
print(f"Running on device {device}")
torch.distributed.init_process_group(backend="gloo")
return device

class Trainer:
def __init__(
Expand All @@ -22,6 +34,7 @@ def __init__(
optimizer: torch.optim.Optimizer,
save_every: int,
snapshot_path: str,
device
) -> None:
self.local_rank = int(os.environ["LOCAL_RANK"])
self.global_rank = int(os.environ["RANK"])
Expand All @@ -31,14 +44,15 @@ def __init__(
self.save_every = save_every
self.epochs_run = 0
self.snapshot_path = snapshot_path
self.device = device
if os.path.exists(snapshot_path):
print("Loading snapshot")
self._load_snapshot(snapshot_path)

self.model = DDP(self.model, device_ids=[self.local_rank])

def _load_snapshot(self, snapshot_path):
loc = f"cuda:{self.local_rank}"
loc = str(self.device)
snapshot = torch.load(snapshot_path, map_location=loc)
self.model.load_state_dict(snapshot["MODEL_STATE"])
self.epochs_run = snapshot["EPOCHS_RUN"]
Expand Down Expand Up @@ -93,10 +107,10 @@ def prepare_dataloader(dataset: Dataset, batch_size: int):


def main(save_every: int, total_epochs: int, batch_size: int, snapshot_path: str = "snapshot.pt"):
ddp_setup()
device = ddp_setup()
dataset, model, optimizer = load_train_objs()
train_data = prepare_dataloader(dataset, batch_size)
trainer = Trainer(model, train_data, optimizer, save_every, snapshot_path)
trainer = Trainer(model, train_data, optimizer, save_every, snapshot_path, device)
trainer.train(total_epochs)
destroy_process_group()

Expand Down
2 changes: 1 addition & 1 deletion distributed/ddp-tutorial-series/requirements.txt
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@@ -1 +1 @@
torch>=1.11.0
torch>=2.7
10 changes: 10 additions & 0 deletions distributed/ddp-tutorial-series/run_example.sh
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Original file line number Diff line number Diff line change
@@ -0,0 +1,10 @@
# /bin/bash
# bash run_example.sh {file_to_run.py} {num_gpus}
# where file_to_run = example to run. Default = 'example.py'
# num_gpus = num local gpus to use (must be at least 2). Default = 2

# samples to run include:
# example.py

echo "Launching ${1:-example.py} with ${2:-2} gpus"
torchrun --nnodes=1 --nproc_per_node=${2:-2} --rdzv_id=101 --rdzv_endpoint="localhost:5972" ${1:-example.py}
6 changes: 6 additions & 0 deletions run_distributed_examples.sh
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Expand Up @@ -50,6 +50,12 @@ function distributed_tensor_parallelism() {
uv run bash run_example.sh fsdp_tp_example.py || error "2D parallel example failed"
}

function distributed_ddp-tutorial-series() {
uv run bash run_example.sh multigpu.py || error "ddp tutorial series multigpu example failed"
uv run bash run_example.sh multigpu_torchrun.py || error "ddp tutorial series multigpu torchrun example failed"
uv run bash run_example.sh multinode.py || error "ddp tutorial series multinode example failed"
}

function distributed_ddp() {
uv run main.py || error "ddp example failed"
}
Expand Down