Making large AI models cheaper, faster and more accessible
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import importlib
import os
import time
from abc import abstractmethod
from pathlib import Path
from typing import List
from .base_extension import _Extension
__all__ = ["_CppExtension"]
class _CppExtension(_Extension):
def __init__(self, name: str, priority: int = 1):
super().__init__(name, support_aot=True, support_jit=True, priority=priority)
# we store the op as an attribute to avoid repeated building and loading
self.cached_op = None
# build-related variables
self.prebuilt_module_path = "colossalai._C"
self.prebuilt_import_path = f"{self.prebuilt_module_path}.{self.name}"
self.version_dependent_macros = ["-DVERSION_GE_1_1", "-DVERSION_GE_1_3", "-DVERSION_GE_1_5"]
def csrc_abs_path(self, path):
return os.path.join(self.relative_to_abs_path("csrc"), path)
def relative_to_abs_path(self, code_path: str) -> str:
"""
This function takes in a path relative to the colossalai root directory and return the absolute path.
"""
# get the current file path
# iteratively check the parent directory
# if the parent directory is "extensions", then the current file path is the root directory
# otherwise, the current file path is inside the root directory
current_file_path = Path(__file__)
while True:
if current_file_path.name == "extensions":
break
else:
current_file_path = current_file_path.parent
extension_module_path = current_file_path
code_abs_path = extension_module_path.joinpath(code_path)
return str(code_abs_path)
# functions must be overrided over
def strip_empty_entries(self, args):
"""
Drop any empty strings from the list of compile and link flags
"""
return [x for x in args if len(x) > 0]
def import_op(self):
"""
This function will import the op module by its string name.
"""
return importlib.import_module(self.prebuilt_import_path)
def build_aot(self) -> "CppExtension":
from torch.utils.cpp_extension import CppExtension
return CppExtension(
name=self.prebuilt_import_path,
sources=self.strip_empty_entries(self.sources_files()),
include_dirs=self.strip_empty_entries(self.include_dirs()),
extra_compile_args=self.strip_empty_entries(self.cxx_flags()),
)
def build_jit(self) -> None:
from torch.utils.cpp_extension import load
build_directory = _Extension.get_jit_extension_folder_path()
build_directory = Path(build_directory)
build_directory.mkdir(parents=True, exist_ok=True)
# check if the kernel has been built
compiled_before = False
kernel_file_path = build_directory.joinpath(f"{self.name}.o")
if kernel_file_path.exists():
compiled_before = True
# load the kernel
if compiled_before:
print(f"[extension] Loading the JIT-built {self.name} kernel during runtime now")
else:
print(f"[extension] Compiling the JIT {self.name} kernel during runtime now")
build_start = time.time()
op_kernel = load(
name=self.name,
sources=self.strip_empty_entries(self.sources_files()),
extra_include_paths=self.strip_empty_entries(self.include_dirs()),
extra_cflags=self.cxx_flags(),
extra_ldflags=[],
build_directory=str(build_directory),
)
build_duration = time.time() - build_start
if compiled_before:
print(f"[extension] Time taken to load {self.name} op: {build_duration} seconds")
else:
print(f"[extension] Time taken to compile {self.name} op: {build_duration} seconds")
return op_kernel
# functions must be overrided begin
@abstractmethod
def sources_files(self) -> List[str]:
"""
This function should return a list of source files for extensions.
"""
@abstractmethod
def include_dirs(self) -> List[str]:
"""
This function should return a list of include files for extensions.
"""
@abstractmethod
def cxx_flags(self) -> List[str]:
"""
This function should return a list of cxx compilation flags for extensions.
"""
def load(self):
try:
op_kernel = self.import_op()
except (ImportError, ModuleNotFoundError):
# if import error occurs, it means that the kernel is not pre-built
# so we build it jit
op_kernel = self.build_jit()
return op_kernel