| |
| |
| |
| |
|
|
| import functools |
| import tempfile |
|
|
| import torch |
|
|
|
|
| def spawn_and_init(fn, world_size, args=None): |
| if args is None: |
| args = () |
| with tempfile.NamedTemporaryFile(delete=False) as tmp_file: |
| torch.multiprocessing.spawn( |
| fn=functools.partial(init_and_run, fn, args), |
| args=( |
| world_size, |
| tmp_file.name, |
| ), |
| nprocs=world_size, |
| join=True, |
| ) |
|
|
|
|
| def distributed_init(rank, world_size, tmp_file): |
| torch.distributed.init_process_group( |
| backend="nccl", |
| init_method="file://{}".format(tmp_file), |
| world_size=world_size, |
| rank=rank, |
| ) |
| torch.cuda.set_device(rank) |
|
|
|
|
| def init_and_run(fn, args, rank, world_size, tmp_file): |
| distributed_init(rank, world_size, tmp_file) |
| group = torch.distributed.new_group() |
| fn(rank, group, *args) |
|
|
|
|
| def objects_are_equal(a, b) -> bool: |
| if type(a) is not type(b): |
| return False |
| if isinstance(a, dict): |
| if set(a.keys()) != set(b.keys()): |
| return False |
| for k in a.keys(): |
| if not objects_are_equal(a[k], b[k]): |
| return False |
| return True |
| elif isinstance(a, (list, tuple, set)): |
| if len(a) != len(b): |
| return False |
| return all(objects_are_equal(x, y) for x, y in zip(a, b)) |
| elif torch.is_tensor(a): |
| return ( |
| a.size() == b.size() |
| and a.dtype == b.dtype |
| and a.device == b.device |
| and torch.all(a == b) |
| ) |
| else: |
| return a == b |
|
|
