mirror of https://github.com/hpcaitech/ColossalAI
109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
import torch
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import functools
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from typing import Optional
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def substitute_init_recursively(cls, func):
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for subcls in cls.__subclasses__():
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substitute_init_recursively(subcls, func)
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func(subcls)
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def call_to_str(base, *args, **kwargs):
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"""Construct a string representation of a call.
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Args:
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base (str): name of the call
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args (tuple, optional): args to ``base``
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kwargs (dict, optional): kwargs supplied to ``base``
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Returns:
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str: A string representation of base(*args, **kwargs)
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"""
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name = f'{base}('
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if args:
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name += ', '.join(repr(arg) for arg in args)
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if kwargs:
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name += ', '
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if kwargs:
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name += ', '.join(f'{key}={repr(arg)}' for key, arg in kwargs.items())
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name += ')'
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return name
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class InsertPostInitMethodToModuleSubClasses(object):
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def __init__(self, default_dtype: Optional[torch.dtype] = None):
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self._old_default_dtype = None
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self._default_dtype = default_dtype
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def __enter__(self):
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r"""
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Enter the context scope.
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"""
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if self._default_dtype is not None:
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self._old_default_dtype = torch.get_default_dtype()
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torch.set_default_dtype(self._default_dtype)
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def preprocess_after(f):
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@functools.wraps(f)
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def wrapper(module: torch.nn.Module, *args, **kwargs):
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f(module, *args, **kwargs)
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self._post_init_method(module, *args, **kwargs)
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return wrapper
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def _enable_class(cls):
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cls._old_init = cls.__init__
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cls.__init__ = preprocess_after(cls.__init__)
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# The function is called during init subclass.
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def _init_subclass(cls, **kwargs):
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cls.__init__ = preprocess_after(cls.__init__)
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# Replace .__init__() for all existing subclasses of torch.nn.Module
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# Excution self._post_init_method after the default init function.
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substitute_init_recursively(torch.nn.modules.module.Module, _enable_class)
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# holding on to the current __init__subclass__ for exit
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torch.nn.modules.module.Module._old_init_subclass = (torch.nn.modules.module.Module.__init_subclass__)
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# Replace .__init__() for future subclasses of torch.nn.Module
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torch.nn.modules.module.Module.__init_subclass__ = classmethod(_init_subclass)
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self._pre_context_exec()
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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if self._default_dtype is not None:
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torch.set_default_dtype(self._old_default_dtype)
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def _disable_class(cls):
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if not hasattr(cls, '_old_init'):
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raise AttributeError(
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f"_old_init is not found in the {cls.__name__}, please make sure that you have imported {cls.__name__} before entering the context."
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)
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cls.__init__ = cls._old_init
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# Replace .__init__() for all existing subclasses of torch.nn.Module
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substitute_init_recursively(torch.nn.modules.module.Module, _disable_class)
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# Replace .__init__() for future subclasses of torch.nn.Module
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torch.nn.modules.module.Module.__init_subclass__ = (torch.nn.modules.module.Module._old_init_subclass)
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self._post_context_exec()
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# Now that we cleaned up the metaclass injection, raise the exception.
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if exc_type is not None:
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return False
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# To be implemented by inheriting classes
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def _post_init_method(self, module, *args, **kwargs):
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pass
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def _pre_context_exec(self):
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pass
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def _post_context_exec(self):
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pass
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