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| # coding=utf-8 | |
| # Copyright 2018 The HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """ Auto Model class. """ | |
| from __future__ import absolute_import, division, print_function, unicode_literals | |
| import logging | |
| from .configuration_bert import BertConfig | |
| from .configuration_openai import OpenAIGPTConfig | |
| from .configuration_gpt2 import GPT2Config | |
| from .configuration_transfo_xl import TransfoXLConfig | |
| from .configuration_xlnet import XLNetConfig | |
| from .configuration_xlm import XLMConfig | |
| from .configuration_roberta import RobertaConfig | |
| from .configuration_distilbert import DistilBertConfig | |
| logger = logging.getLogger(__name__) | |
| class AutoConfig(object): | |
| r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class | |
| that will be instantiated as one of the configuration classes of the library | |
| when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` | |
| class method. | |
| The `from_pretrained()` method take care of returning the correct model class instance | |
| using pattern matching on the `pretrained_model_name_or_path` string. | |
| The base model class to instantiate is selected as the first pattern matching | |
| in the `pretrained_model_name_or_path` string (in the following order): | |
| - contains `distilbert`: DistilBertConfig (DistilBERT model) | |
| - contains `bert`: BertConfig (Bert model) | |
| - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model) | |
| - contains `gpt2`: GPT2Config (OpenAI GPT-2 model) | |
| - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model) | |
| - contains `xlnet`: XLNetConfig (XLNet model) | |
| - contains `xlm`: XLMConfig (XLM model) | |
| - contains `roberta`: RobertaConfig (RoBERTa model) | |
| This class cannot be instantiated using `__init__()` (throw an error). | |
| """ | |
| def __init__(self): | |
| raise EnvironmentError("AutoConfig is designed to be instantiated " | |
| "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method.") | |
| def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): | |
| r""" Instantiate a one of the configuration classes of the library | |
| from a pre-trained model configuration. | |
| The configuration class to instantiate is selected as the first pattern matching | |
| in the `pretrained_model_name_or_path` string (in the following order): | |
| - contains `distilbert`: DistilBertConfig (DistilBERT model) | |
| - contains `bert`: BertConfig (Bert model) | |
| - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model) | |
| - contains `gpt2`: GPT2Config (OpenAI GPT-2 model) | |
| - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model) | |
| - contains `xlnet`: XLNetConfig (XLNet model) | |
| - contains `xlm`: XLMConfig (XLM model) | |
| - contains `roberta`: RobertaConfig (RoBERTa model) | |
| Params: | |
| pretrained_model_name_or_path: either: | |
| - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``. | |
| - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``. | |
| - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``. | |
| cache_dir: (`optional`) string: | |
| Path to a directory in which a downloaded pre-trained model | |
| configuration should be cached if the standard cache should not be used. | |
| kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading. | |
| - The values in kwargs of any keys which are configuration attributes will be used to override the loaded values. | |
| - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter. | |
| force_download: (`optional`) boolean, default False: | |
| Force to (re-)download the model weights and configuration files and override the cached versions if they exists. | |
| proxies: (`optional`) dict, default None: | |
| A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. | |
| The proxies are used on each request. | |
| return_unused_kwargs: (`optional`) bool: | |
| - If False, then this function returns just the final configuration object. | |
| - 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. | |
| Examples:: | |
| config = AutoConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache. | |
| config = AutoConfig.from_pretrained('./test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')` | |
| config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json') | |
| config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False) | |
| assert config.output_attention == True | |
| config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, | |
| foo=False, return_unused_kwargs=True) | |
| assert config.output_attention == True | |
| assert unused_kwargs == {'foo': False} | |
| """ | |
| if 'distilbert' in pretrained_model_name_or_path: | |
| return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| elif 'roberta' in pretrained_model_name_or_path: | |
| return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| elif 'bert' in pretrained_model_name_or_path: | |
| return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| elif 'openai-gpt' in pretrained_model_name_or_path: | |
| return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| elif 'gpt2' in pretrained_model_name_or_path: | |
| return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| elif 'transfo-xl' in pretrained_model_name_or_path: | |
| return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| elif 'xlnet' in pretrained_model_name_or_path: | |
| return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| elif 'xlm' in pretrained_model_name_or_path: | |
| return XLMConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| raise ValueError("Unrecognized model identifier in {}. Should contains one of " | |
| "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " | |
| "'xlm', 'roberta'".format(pretrained_model_name_or_path)) | |