Source code for paddlenlp.transformers.utils

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import functools
import inspect

import paddle
from paddle.nn import Layer


def fn_args_to_dict(func, *args, **kwargs):
    """
    Inspect function `func` and its arguments for running, and extract a
    dict mapping between argument names and keys. 
    """
    if hasattr(inspect, 'getfullargspec'):
        (spec_args, spec_varargs, spec_varkw, spec_defaults, _, _,
         _) = inspect.getfullargspec(func)
    else:
        (spec_args, spec_varargs, spec_varkw,
         spec_defaults) = inspect.getargspec(func)
    # add positional argument values
    init_dict = dict(zip(spec_args, args))
    # add default argument values
    kwargs_dict = dict(zip(spec_args[-len(spec_defaults):],
                           spec_defaults)) if spec_defaults else {}
    kwargs_dict.update(kwargs)
    init_dict.update(kwargs_dict)
    return init_dict


class InitTrackerMeta(type(Layer)):
    """
    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 `_wrap_init` method, it would be
    hooked after `__init__` and called as `_wrap_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.
    """

    def __init__(cls, name, bases, attrs):
        init_func = cls.__init__
        # If attrs has `__init__`, wrap it using accessable `_wrap_init`.
        # Otherwise, no need to wrap again since the super cls has been wraped.
        # TODO: remove reduplicated tracker if using super cls `__init__`
        help_func = getattr(cls, '_wrap_init',
                            None) if '__init__' in attrs else None
        cls.__init__ = InitTrackerMeta.init_and_track_conf(init_func, help_func)
        super(InitTrackerMeta, cls).__init__(name, bases, attrs)

    @staticmethod
    def init_and_track_conf(init_func, help_func=None):
        """
        wraps `init_func` which is `__init__` method of a class to add `init_config`
        attribute for instances of that class.
        Args:
            init_func (callable): It should be the `__init__` method of a class.
            help_func (callable, optional): If provided, it would be hooked after
                `init_func` and called as `_wrap_init(self, init_func, *init_args, **init_args)`.
                Default None.
        
        Returns:
            function: the wrapped function
        """

        @functools.wraps(init_func)
        def __impl__(self, *args, **kwargs):
            # keep full configuration
            init_func(self, *args, **kwargs)
            # registed helper by `_wrap_init`
            if help_func:
                help_func(self, init_func, *args, **kwargs)
            self.init_config = kwargs
            if args:
                kwargs['init_args'] = args
            kwargs['init_class'] = self.__class__.__name__

        return __impl__