| import torch |
| import torch.distributed as dist |
|
|
| def init_process(): |
| dist.init_process_group(backend="nccl") |
| torch.cuda.set_device(dist.get_rank()) |
|
|
| def example_broadcast(): |
| if dist.get_rank() == 0: |
| tensor = torch.tensor([1, 2, 3, 4], dtype=torch.float32).cuda() |
| else: |
| tensor = torch.zeros(4, dtype=torch.float32).cuda() |
|
|
| print(f"Before broadcast on rank {dist.get_rank()}: {tensor}") |
| dist.broadcast(tensor, src=0) |
| print(f"After broadcast on rank {dist.get_rank()}: {tensor}") |
|
|
| init_process() |
| example_broadcast() |
| dist.destroy_process_group() |
