Making large AI models cheaper, faster and more accessible
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#!/usr/bin/env python
from collections import OrderedDict
from typing import Union
from .base_accelerator import BaseAccelerator
from .cpu_accelerator import CpuAccelerator
from .cuda_accelerator import CudaAccelerator
from .npu_accelerator import NpuAccelerator
__all__ = ["set_accelerator", "auto_set_accelerator", "get_accelerator"]
_ACCELERATOR = None
# we use ordered dictionary here to associate the
# order with device check priority
# i.e. auto_set_accelerator will check cuda first
_ACCELERATOR_MAPPING = OrderedDict(cuda=CudaAccelerator, npu=NpuAccelerator, cpu=CpuAccelerator)
def set_accelerator(accelerator: Union[str, BaseAccelerator]) -> None:
"""
Set the global accelerator for the current process.
Args:
accelerator (Union[str, BaseAccelerator]): the type of accelerator to which the current device belongs.
"""
global _ACCELERATOR
if isinstance(accelerator, str):
_ACCELERATOR = _ACCELERATOR_MAPPING[accelerator]()
elif isinstance(accelerator, BaseAccelerator):
_ACCELERATOR = accelerator
else:
raise TypeError("accelerator must be either a string or an instance of BaseAccelerator")
def auto_set_accelerator() -> None:
"""
Automatically check if any accelerator is available.
If an accelerator is available, set it as the global accelerator.
"""
global _ACCELERATOR
for accelerator_name, accelerator_cls in _ACCELERATOR_MAPPING.items():
try:
accelerator = accelerator_cls()
if accelerator_name == "cpu" or accelerator.is_available():
_ACCELERATOR = accelerator
break
except:
pass
if _ACCELERATOR is None:
raise RuntimeError("No accelerator is available.")
def get_accelerator() -> BaseAccelerator:
"""
Return the accelerator for the current process. If the accelerator is not initialized, it will be initialized
to the default accelerator type.
Returns: the accelerator for the current process.
"""
global _ACCELERATOR
if _ACCELERATOR is None:
auto_set_accelerator()
return _ACCELERATOR