101 lines
1.9 KiB
Python
Executable File
101 lines
1.9 KiB
Python
Executable File
"""
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# https://github.com/enhuiz/pytorch-training-utilities
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"""
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import os
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import socket
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from functools import cache, wraps
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from typing import Callable
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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def get_free_port():
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sock = socket.socket()
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sock.bind(("", 0))
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return sock.getsockname()[1]
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_distributed_initialized = False
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def init_distributed( fn, *args, **kwargs ):
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#print("Initializing distributed...")
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torch.cuda.set_device(local_rank())
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fn(*args, **kwargs)
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_distributed_initialized = True
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def distributed_initialized():
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return _distributed_initialized
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@cache
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def fix_unset_envs():
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envs = dict(
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RANK="0",
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WORLD_SIZE="1",
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MASTER_ADDR="localhost",
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MASTER_PORT=str(get_free_port()),
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LOCAL_RANK="0",
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)
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for key in envs:
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value = os.getenv(key)
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if value is not None:
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return
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for key, value in envs.items():
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os.environ[key] = value
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def local_rank():
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return int(os.getenv("LOCAL_RANK", 0))
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def global_rank():
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return int(os.getenv("RANK", 0))
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def world_size():
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return int(os.getenv("WORLD_SIZE", 1))
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def is_local_leader():
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return local_rank() == 0
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def is_global_leader():
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return global_rank() == 0
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def local_leader_only(fn=None, *, default=None) -> Callable:
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def wrapper(fn):
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@wraps(fn)
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def wrapped(*args, **kwargs):
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if is_local_leader():
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return fn(*args, **kwargs)
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return default
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return wrapped
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if fn is None:
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return wrapper
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return wrapper(fn)
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def global_leader_only(fn: Callable | None = None, *, default=None) -> Callable:
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def wrapper(fn):
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@wraps(fn)
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def wrapped(*args, **kwargs):
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if is_global_leader():
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return fn(*args, **kwargs)
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return default
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return wrapped
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if fn is None:
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return wrapper
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return wrapper(fn)
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def ddp_model(model):
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return DDP(model.to(device='cuda'), [local_rank()])
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