config

mindnlp.peft.config

configs

mindnlp.peft.config.PeftConfig dataclass

Bases: PeftConfigMixin

This is the base configuration class to store the configuration of a [PeftModel].

PARAMETER	DESCRIPTION
peft_type	The type of Peft method to use. TYPE: Union[[`~peft.utils.config.PeftType`], `str`] DEFAULT: None
task_type	The type of task to perform. TYPE: Union[[`~peft.utils.config.TaskType`], `str`] DEFAULT: None
inference_mode	Whether to use the Peft model in inference mode. TYPE: `bool`, defaults to `False` DEFAULT: False

Source code in mindnlp/peft/config.py

@dataclass
class PeftConfig(PeftConfigMixin):
    """
    This is the base configuration class to store the configuration of a [`PeftModel`].

    Args:
        peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use.
        task_type (Union[[`~peft.utils.config.TaskType`], `str`]): The type of task to perform.
        inference_mode (`bool`, defaults to `False`): Whether to use the Peft model in inference mode.
    """
    base_model_name_or_path: str = field(default=None, metadata={"help": "The name of the base model to use."})
    peft_type: Union[str, PeftType] = field(default=None, metadata={"help": "Peft type"})
    task_type: Union[str, TaskType] = field(default=None, metadata={"help": "Task type"})
    inference_mode: bool = field(default=False, metadata={"help": "Whether to use inference mode"})

mindnlp.peft.config.PeftConfigMixin dataclass

This is the base configuration class for PEFT adapter models. It contains all the methods that are common to all PEFT adapter models. The method save_pretrained will save the configuration of your adapter model in a directory. The method from_pretrained will load the configuration of your adapter model from a directory.

PARAMETER	DESCRIPTION
peft_type	The type of Peft method to use. TYPE: Union[[`~peft.utils.config.PeftType`], `str`] DEFAULT: None

Source code in mindnlp/peft/config.py

@dataclass
class PeftConfigMixin():
    r"""
    This is the base configuration class for PEFT adapter models. 
    It contains all the methods that are common to all PEFT adapter models.
    The method `save_pretrained` will save the configuration of your adapter model in a directory.
    The method `from_pretrained` will load the configuration of your adapter model from a directory.

    Args:
        peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use.
    """
    peft_type: Optional[PeftType] = field(default=None, metadata={"help": "The type of PEFT model."})

    @property
    def __dict__(self):
        r"""
        Method '__dict__' in the class 'PeftConfigMixin' returns a dictionary representation of the object using the 'asdict' function.

        Args:
            self: The instance of the class. This parameter represents the object for which the dictionary representation is generated.

        Returns:
            None. The method does not return any value explicitly, as the dictionary representation is retrieved internally.

        Raises:
            No exceptions are explicitly raised by this method.
        """
        return asdict(self)

    def to_dict(self):
        """to dict"""
        return self.__dict__

    def save_pretrained(self, save_directory, **kwargs):
        r"""
        This method saves the configuration of your adapter model in a directory.

        Args:
            save_directory (`str`):
                The directory where the configuration will be saved.
            kwargs (additional keyword arguments, *optional*):
                Additional keyword arguments passed along to the 
                [`~transformers.utils.PushToHubMixin.push_to_hub`] method.
        """
        if os.path.isfile(save_directory):
            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

        os.makedirs(save_directory, exist_ok=True)

        output_dict = asdict(self)
        # converting set type to list
        for key, value in output_dict.items():
            if isinstance(value, set):
                output_dict[key] = list(value)
        output_path = os.path.join(save_directory, CONFIG_NAME)

        # save it
        with open(output_path, "w", encoding='utf-8') as writer:
            writer.write(json.dumps(output_dict, indent=2, sort_keys=True))

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, subfolder=None, **kwargs):
        r"""
        This method loads the configuration of your adapter model from a directory.

        Args:
            pretrained_model_name_or_path (`str`):
                The directory or the Hub repository id where the configuration is saved.
            kwargs (additional keyword arguments, *optional*):
                Additional keyword arguments passed along to the child class initialization.
        """
        path = (
            os.path.join(pretrained_model_name_or_path, subfolder)
            if subfolder is not None
            else pretrained_model_name_or_path
        )
        # read config file
        if os.path.isfile(os.path.join(path, CONFIG_NAME)):
            config_file = os.path.join(path, CONFIG_NAME)
        else:
            raise ValueError(f"Can't find '{CONFIG_NAME}' at '{pretrained_model_name_or_path}'")

        loaded_attributes = cls.from_json_file(config_file)

        config = cls(**kwargs)

        for key, value in loaded_attributes.items():
            if hasattr(config, key):
                setattr(config, key, value)

        return config

    @classmethod
    def from_json_file(cls, path_json_file, **kwargs):
        r"""
        Loads a configuration file from a json file.

        Args:
            path_json_file (`str`):
                The path to the json file.
        """
        with open(path_json_file, "r", encoding='utf-8') as file:
            json_object = json.load(file)

        return json_object

    @property
    def is_prompt_learning(self):
        r"""
        Utility method to check if the configuration is for prompt learning.
        """
        return False

mindnlp.peft.config.PeftConfigMixin.__dict__ property

Method 'dict' in the class 'PeftConfigMixin' returns a dictionary representation of the object using the 'asdict' function.

PARAMETER	DESCRIPTION
self	The instance of the class. This parameter represents the object for which the dictionary representation is generated.

RETURNS	DESCRIPTION
	None. The method does not return any value explicitly, as the dictionary representation is retrieved internally.

mindnlp.peft.config.PeftConfigMixin.is_prompt_learning property

Utility method to check if the configuration is for prompt learning.

mindnlp.peft.config.PeftConfigMixin.from_json_file(path_json_file, **kwargs) classmethod

Loads a configuration file from a json file.

PARAMETER	DESCRIPTION
path_json_file	The path to the json file. TYPE: `str`

Source code in mindnlp/peft/config.py

@classmethod
def from_json_file(cls, path_json_file, **kwargs):
    r"""
    Loads a configuration file from a json file.

    Args:
        path_json_file (`str`):
            The path to the json file.
    """
    with open(path_json_file, "r", encoding='utf-8') as file:
        json_object = json.load(file)

    return json_object

mindnlp.peft.config.PeftConfigMixin.from_pretrained(pretrained_model_name_or_path, subfolder=None, **kwargs) classmethod

This method loads the configuration of your adapter model from a directory.

PARAMETER	DESCRIPTION
pretrained_model_name_or_path	The directory or the Hub repository id where the configuration is saved. TYPE: `str`
kwargs	Additional keyword arguments passed along to the child class initialization. TYPE: additional keyword arguments, *optional* DEFAULT: {}

Source code in mindnlp/peft/config.py

@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, subfolder=None, **kwargs):
    r"""
    This method loads the configuration of your adapter model from a directory.

    Args:
        pretrained_model_name_or_path (`str`):
            The directory or the Hub repository id where the configuration is saved.
        kwargs (additional keyword arguments, *optional*):
            Additional keyword arguments passed along to the child class initialization.
    """
    path = (
        os.path.join(pretrained_model_name_or_path, subfolder)
        if subfolder is not None
        else pretrained_model_name_or_path
    )
    # read config file
    if os.path.isfile(os.path.join(path, CONFIG_NAME)):
        config_file = os.path.join(path, CONFIG_NAME)
    else:
        raise ValueError(f"Can't find '{CONFIG_NAME}' at '{pretrained_model_name_or_path}'")

    loaded_attributes = cls.from_json_file(config_file)

    config = cls(**kwargs)

    for key, value in loaded_attributes.items():
        if hasattr(config, key):
            setattr(config, key, value)

    return config

mindnlp.peft.config.PeftConfigMixin.save_pretrained(save_directory, **kwargs)

This method saves the configuration of your adapter model in a directory.

PARAMETER	DESCRIPTION
save_directory	The directory where the configuration will be saved. TYPE: `str`
kwargs	Additional keyword arguments passed along to the [~transformers.utils.PushToHubMixin.push_to_hub] method. TYPE: additional keyword arguments, *optional* DEFAULT: {}

Source code in mindnlp/peft/config.py

def save_pretrained(self, save_directory, **kwargs):
    r"""
    This method saves the configuration of your adapter model in a directory.

    Args:
        save_directory (`str`):
            The directory where the configuration will be saved.
        kwargs (additional keyword arguments, *optional*):
            Additional keyword arguments passed along to the 
            [`~transformers.utils.PushToHubMixin.push_to_hub`] method.
    """
    if os.path.isfile(save_directory):
        raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

    os.makedirs(save_directory, exist_ok=True)

    output_dict = asdict(self)
    # converting set type to list
    for key, value in output_dict.items():
        if isinstance(value, set):
            output_dict[key] = list(value)
    output_path = os.path.join(save_directory, CONFIG_NAME)

    # save it
    with open(output_path, "w", encoding='utf-8') as writer:
        writer.write(json.dumps(output_dict, indent=2, sort_keys=True))

mindnlp.peft.config.PeftConfigMixin.to_dict()

to dict

Source code in mindnlp/peft/config.py

def to_dict(self):
    """to dict"""
    return self.__dict__

mindnlp.peft.config.PromptLearningConfig dataclass

Bases: PeftConfig

This is the base configuration class to store the configuration of [PrefixTuning], [PromptEncoder], or [PromptTuning].

PARAMETER	DESCRIPTION
num_virtual_tokens	The number of virtual tokens to use. TYPE: `int` DEFAULT: None
token_dim	The hidd-en embedding dimension of the base transformer model. TYPE: `int` DEFAULT: None
num_transformer_submodules	The number of transformer subcells in the base transformer model. TYPE: `int` DEFAULT: None
num_attention_heads	The number of attention heads in the base transformer model. TYPE: `int` DEFAULT: None
num_layers	The number of layers in the base transformer model. TYPE: `int` DEFAULT: None

Source code in mindnlp/peft/config.py

@dataclass
class PromptLearningConfig(PeftConfig):
    """
    This is the base configuration class to store the configuration of [`PrefixTuning`], [`PromptEncoder`], or
    [`PromptTuning`].

    Args:
        num_virtual_tokens (`int`): The number of virtual tokens to use.
        token_dim (`int`): The hidd-en embedding dimension of the base transformer model.
        num_transformer_submodules (`int`): The number of transformer subcells in the base transformer model.
        num_attention_heads (`int`): The number of attention heads in the base transformer model.
        num_layers (`int`): The number of layers in the base transformer model.
    """
    num_virtual_tokens: int = field(default=None, metadata={"help": "Number of virtual tokens"})
    token_dim: int = field(
        default=None, metadata={"help": "The hidden embedding dimension of the base transformer model"}
    )
    num_transformer_submodules: Optional[int] = field(
        default=None, metadata={"help": "Number of transformer subcells"}
    )
    num_attention_heads: Optional[int] = field(default=None, metadata={"help": "Number of attention heads"})
    num_layers: Optional[int] = field(default=None, metadata={"help": "Number of transformer layers"})
    @property
    def is_prompt_learning(self):
        r"""
        Utility method to check if the configuration is for prompt learning.
        """
        return True

mindnlp.peft.config.PromptLearningConfig.is_prompt_learning property

Utility method to check if the configuration is for prompt learning.