Making large AI models cheaper, faster and more accessible
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from typing import Callable
import torch
TORCH_MAJOR = int(torch.__version__.split(".")[0])
TORCH_MINOR = int(torch.__version__.split(".")[1])
if TORCH_MAJOR == 1 and TORCH_MINOR < 12:
META_COMPATIBILITY = False
elif TORCH_MAJOR == 1 and TORCH_MINOR == 12:
META_COMPATIBILITY = True
elif TORCH_MAJOR == 1 and TORCH_MINOR == 13:
META_COMPATIBILITY = True
elif TORCH_MAJOR == 2:
META_COMPATIBILITY = True
def compatibility(is_backward_compatible: bool = False) -> Callable:
"""A decorator to make a function compatible with different versions of PyTorch.
Args:
is_backward_compatible (bool, optional): Whether the function is backward compatible. Defaults to False.
Returns:
Callable: The decorated function
"""
def decorator(func):
if META_COMPATIBILITY:
return func
else:
if is_backward_compatible:
return func
else:
def wrapper(*args, **kwargs):
raise RuntimeError(f"Function `{func.__name__}` is not compatible with PyTorch {torch.__version__}")
return wrapper
return decorator
def is_compatible_with_meta() -> bool:
"""Check the meta compatibility. Normally it should be called before importing some of the `colossalai.fx`
modules. If the meta compatibility is not satisfied, the `colossalai.fx` modules will be replaced by its
experimental counterparts.
Returns:
bool: The meta compatibility
"""
return META_COMPATIBILITY