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206 lines
10 KiB
Python
206 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Configuration base class and utilities."""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import copy
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import json
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import logging
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import os
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from io import open
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from .file_utils import cached_path, CONFIG_NAME
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logger = logging.getLogger(__name__)
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class PretrainedConfig(object):
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r""" Base class for all configuration classes.
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Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations.
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Note:
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A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights.
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It only affects the model's configuration.
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Class attributes (overridden by derived classes):
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- ``pretrained_config_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained model configurations as values.
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Parameters:
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``finetuning_task``: string, default `None`. Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
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``num_labels``: integer, default `2`. Number of classes to use when the model is a classification model (sequences/tokens)
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``output_attentions``: boolean, default `False`. Should the model returns attentions weights.
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``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
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``torchscript``: string, default `False`. Is the model used with Torchscript.
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"""
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pretrained_config_archive_map = {}
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def __init__(self, **kwargs):
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self.finetuning_task = kwargs.pop('finetuning_task', None)
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self.num_labels = kwargs.pop('num_labels', 2)
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self.output_attentions = kwargs.pop('output_attentions', False)
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self.output_hidden_states = kwargs.pop('output_hidden_states', False)
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self.torchscript = kwargs.pop('torchscript', False)
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self.pruned_heads = kwargs.pop('pruned_heads', {})
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def save_pretrained(self, save_directory):
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""" Save a configuration object to the directory `save_directory`, so that it
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can be re-loaded using the :func:`~pytorch_transformers.PretrainedConfig.from_pretrained` class method.
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"""
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assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
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# If we save using the predefined names, we can load using `from_pretrained`
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output_config_file = os.path.join(save_directory, CONFIG_NAME)
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self.to_json_file(output_config_file)
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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r""" Instantiate a :class:`~pytorch_transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
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Parameters:
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pretrained_model_name_or_path: either:
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- a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
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- a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
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cache_dir: (`optional`) string:
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Path to a directory in which a downloaded pre-trained model
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configuration should be cached if the standard cache should not be used.
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kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
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- The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
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- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
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force_download: (`optional`) boolean, default False:
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Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
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proxies: (`optional`) dict, default None:
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A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
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The proxies are used on each request.
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return_unused_kwargs: (`optional`) bool:
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- If False, then this function returns just the final configuration object.
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- If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
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Examples::
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# We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
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# derived class: BertConfig
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config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
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config = BertConfig.from_pretrained('./test/saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
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config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
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config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
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assert config.output_attention == True
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config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
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foo=False, return_unused_kwargs=True)
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assert config.output_attention == True
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assert unused_kwargs == {'foo': False}
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"""
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cache_dir = kwargs.pop('cache_dir', None)
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force_download = kwargs.pop('force_download', False)
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proxies = kwargs.pop('proxies', None)
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return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
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if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
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config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
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elif os.path.isdir(pretrained_model_name_or_path):
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config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
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else:
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config_file = pretrained_model_name_or_path
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# redirect to the cache, if necessary
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try:
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resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
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except EnvironmentError as e:
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if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
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logger.error(
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"Couldn't reach server at '{}' to download pretrained model configuration file.".format(
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config_file))
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else:
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logger.error(
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"Model name '{}' was not found in model name list ({}). "
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"We assumed '{}' was a path or url but couldn't find any file "
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"associated to this path or url.".format(
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pretrained_model_name_or_path,
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', '.join(cls.pretrained_config_archive_map.keys()),
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config_file))
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raise e
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if resolved_config_file == config_file:
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logger.info("loading configuration file {}".format(config_file))
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else:
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logger.info("loading configuration file {} from cache at {}".format(
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config_file, resolved_config_file))
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# Load config
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config = cls.from_json_file(resolved_config_file)
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if hasattr(config, 'pruned_heads'):
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config.pruned_heads = dict((int(key), set(value)) for key, value in config.pruned_heads.items())
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# Update config with kwargs if needed
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to_remove = []
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for key, value in kwargs.items():
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if hasattr(config, key):
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setattr(config, key, value)
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to_remove.append(key)
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for key in to_remove:
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kwargs.pop(key, None)
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logger.info("Model config %s", config)
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if return_unused_kwargs:
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return config, kwargs
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else:
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return config
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@classmethod
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def from_dict(cls, json_object):
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"""Constructs a `Config` from a Python dictionary of parameters."""
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config = cls(vocab_size_or_config_json_file=-1)
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for key, value in json_object.items():
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config.__dict__[key] = value
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return config
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@classmethod
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def from_json_file(cls, json_file):
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"""Constructs a `BertConfig` from a json file of parameters."""
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with open(json_file, "r", encoding='utf-8') as reader:
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text = reader.read()
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return cls.from_dict(json.loads(text))
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def __eq__(self, other):
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return self.__dict__ == other.__dict__
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def __repr__(self):
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return str(self.to_json_string())
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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def to_json_file(self, json_file_path):
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""" Save this instance to a json file."""
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with open(json_file_path, "w", encoding='utf-8') as writer:
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writer.write(self.to_json_string())
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