Adding torch accelerator to ddp-tutorial-series example

Signed-off-by: dggaytan <[email protected]>

Author: dggaytan

distributed/ddp-tutorial-series/multigpu.py

@@ -17,9 +17,24 @@ 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"] = "12453"
+
+    
+    if torch.accelerator.is_available():
+        device_type = torch.accelerator.current_accelerator()
+        torch.accelerator.set_device_idx(rank)
+        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, rank=rank, world_size=world_size, device_id=device)
+    else:
+        device = torch.device("cpu")
+        print(f"Running on device {device}")
+        torch.distributed.init_process_group(backend="gloo", device_id=device)
+
+    # torch.cuda.set_device(rank)
+    # init_process_group(backend="xccl", rank=rank, world_size=world_size)
 
 class Trainer:
     def __init__(
@@ -100,5 +115,6 @@ 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()
+    print(world_size)
     mp.spawn(main, args=(world_size, args.save_every, args.total_epochs, args.batch_size), nprocs=world_size)

distributed/ddp-tutorial-series/multigpu_torchrun.py

@@ -11,8 +11,21 @@
 
 
 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 = 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__(
@@ -22,6 +35,7 @@ def __init__(
         optimizer: torch.optim.Optimizer,
         save_every: int,
         snapshot_path: str,
+        device 
     ) -> None:
         self.gpu_id = int(os.environ["LOCAL_RANK"])
         self.model = model.to(self.gpu_id)
@@ -30,14 +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.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"]
@@ -92,10 +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()
 

distributed/ddp-tutorial-series/multinode.py

@@ -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 = 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__(
@@ -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"])
@@ -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"]
@@ -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()
 

distributed/ddp-tutorial-series/requirements.txt

@@ -1 +1 @@
-torch>=1.11.0
+torch>=2.7

distributed/ddp-tutorial-series/run_example.sh

@@ -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}

run_distributed_examples.sh

@@ -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"
 }