Making large AI models cheaper, faster and more accessible
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 

51 lines
1.4 KiB

from typing import Any, List, Union
import torch
from ..proxy import ColoProxy
from .meta_patch import meta_patched_function
__all__ = ["is_element_in_list", "extract_meta"]
def is_element_in_list(elements: Union[List[Any], Any], list_: List[Any]):
if isinstance(elements, (tuple, list, set)):
for ele in elements:
if ele not in list_:
return False, ele
else:
if elements not in list_:
return False, elements
return True, None
def extract_meta(*args, **kwargs):
def _convert(val):
if isinstance(val, ColoProxy):
return val.meta_data
elif isinstance(val, (list, tuple)):
return type(val)([_convert(ele) for ele in val])
return val
new_args = [_convert(val) for val in args]
new_kwargs = {k: _convert(v) for k, v in kwargs.items()}
return new_args, new_kwargs
def compute_meta_data_for_functions_proxy(target, args, kwargs):
args_metas, kwargs_metas = extract_meta(*args, **kwargs)
# fetch patched function
if meta_patched_function.has(target):
meta_target = meta_patched_function.get(target)
elif meta_patched_function.has(target.__name__):
meta_target = meta_patched_function.get(target.__name__)
else:
meta_target = target
meta_out = meta_target(*args_metas, **kwargs_metas)
if isinstance(meta_out, torch.Tensor):
meta_out = meta_out.to(device="meta")
return meta_out