Making large AI models cheaper, faster and more accessible
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import hashlib
import os
from abc import ABC, abstractmethod
from typing import Callable, Union
__all__ = ["_Extension"]
class _Extension(ABC):
def __init__(self, name: str, support_aot: bool, support_jit: bool, priority: int = 1):
self._name = name
self._support_aot = support_aot
self._support_jit = support_jit
self.priority = priority
@property
def name(self):
return self._name
@property
def support_aot(self):
return self._support_aot
@property
def support_jit(self):
return self._support_jit
@staticmethod
def get_jit_extension_folder_path():
"""
Kernels which are compiled during runtime will be stored in the same cache folder for reuse.
The folder is in the path ~/.cache/colossalai/torch_extensions/<cache-folder>.
The name of the <cache-folder> follows a common format:
torch<torch_version_major>.<torch_version_minor>_<device_name><device_version>-<hash>
The <hash> suffix is the hash value of the path of the `colossalai` file.
"""
import torch
import colossalai
from colossalai.accelerator import get_accelerator
# get torch version
torch_version_major = torch.__version__.split(".")[0]
torch_version_minor = torch.__version__.split(".")[1]
# get device version
device_name = get_accelerator().name
device_version = get_accelerator().get_version()
# use colossalai's file path as hash
hash_suffix = hashlib.sha256(colossalai.__file__.encode()).hexdigest()
# concat
home_directory = os.path.expanduser("~")
extension_directory = f".cache/colossalai/torch_extensions/torch{torch_version_major}.{torch_version_minor}_{device_name}-{device_version}-{hash_suffix}"
cache_directory = os.path.join(home_directory, extension_directory)
return cache_directory
@abstractmethod
def is_available(self) -> bool:
"""
Check if the hardware required by the kernel is available.
"""
@abstractmethod
def assert_compatible(self) -> None:
"""
Check if the hardware required by the kernel is compatible.
"""
@abstractmethod
def build_aot(self) -> Union["CppExtension", "CUDAExtension"]:
pass
@abstractmethod
def build_jit(self) -> Callable:
pass
@abstractmethod
def load(self) -> Callable:
pass