utils#

fn_args_to_dict(func, *args, **kwargs)[源代码]#

Inspect function func and its arguments for running, and extract a dict mapping between argument names and keys.

adapt_stale_fwd_patch(self, name, value)[源代码]#

Since there are some monkey patches for forward of PretrainedModel, such as model compression, we make these patches compatible with the latest forward method.

class InitTrackerMeta(name, bases, attrs)[源代码]#

基类：type

This metaclass wraps the __init__ method of a class to add init_config attribute for instances of that class, and init_config use a dict to track the initial configuration. If the class has _pre_init or _post_init method, it would be hooked before or after __init__ and called as _pre_init(self, init_fn, init_args) or _post_init(self, init_fn, init_args). Since InitTrackerMeta would be used as metaclass for pretrained model classes, which always are Layer and type(Layer) is not type, thus use type(Layer) rather than type as base class for it to avoid inheritance metaclass conflicts.

static init_and_track_conf(init_func, pre_init_func=None, post_init_func=None)[源代码]#

wraps init_func which is __init__ method of a class to add init_config attribute for instances of that class. :param init_func: It should be the __init__ method of a class.

warning: self always is the class type of down-stream model, eg: BertForTokenClassification

参数:

  • pre_init_func (callable, optional) -- If provided, it would be hooked after init_func and called as pre_init_func(self, init_func, *init_args, **init_args). Default None.

  • post_init_func (callable, optional) -- If provided, it would be hooked after init_func and called as post_init_func(self, init_func, *init_args, **init_args). Default None.

返回:

the wrapped function

返回类型:

function

param_in_func(func, param_field: str) → bool[源代码]#

check if the param_field is in func method, eg: if the bert param is in __init__ method

参数:

  • cls (type) -- the class of PretrainedModel

  • param_field (str) -- the name of field

返回:

the result of existence

返回类型:

bool

resolve_cache_dir(pretrained_model_name_or_path: str, from_hf_hub: bool, cache_dir: str | None = None) → str[源代码]#

resolve cache dir for PretrainedModel and PretrainedConfig

参数:

  • pretrained_model_name_or_path (str) -- the name or path of pretrained model

  • from_hf_hub (bool) -- if load from huggingface hub

  • cache_dir (str) -- cache_dir for models

find_transformer_model_type(model_class: Type) → str[源代码]#
get the model type from module name,
eg:

BertModel -> bert, RobertaForTokenClassification -> roberta

参数:

model_class (Type) -- the class of model

返回:

the type string

返回类型:

str

find_transformer_model_class_by_name(model_name: str) → Type[PretrainedModel] | None[源代码]#

find transformer model_class by name

参数:

model_name (str) -- the string of class name

返回:

optional pretrained-model class

返回类型:

Optional[Type[PretrainedModel]]

class ContextManagers(context_managers: List[AbstractContextManager])[源代码]#

基类：object

Wrapper for contextlib.ExitStack which enters a collection of context managers. Adaptation of ContextManagers in the fastcore library.