configuration_auto.py

# 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. """


import logging

from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig


logger = logging.getLogger(__name__)


ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
    (key, value)
    for pretrained_map in [
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ]
    for key, value, in pretrained_map.items()
)


class AutoConfig(object):
    r""":class:`~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 `albert`: AlbertConfig (ALBERT model)
            - contains `camembert`: CamembertConfig (CamemBERT model)
            - contains `xlm-roberta`: XLMRobertaConfig (XLM-RoBERTa model)
            - contains `roberta`: RobertaConfig (RoBERTa 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 `ctrl` : CTRLConfig (CTRL 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."
        )

    @classmethod
    def for_model(cls, model_type, *args, **kwargs):
        if "distilbert" in model_type:
            return DistilBertConfig(*args, **kwargs)
        elif "roberta" in model_type:
            return RobertaConfig(*args, **kwargs)
        elif "bert" in model_type:
            return BertConfig(*args, **kwargs)
        elif "openai-gpt" in model_type:
            return OpenAIGPTConfig(*args, **kwargs)
        elif "gpt2" in model_type:
            return GPT2Config(*args, **kwargs)
        elif "transfo-xl" in model_type:
            return TransfoXLConfig(*args, **kwargs)
        elif "xlnet" in model_type:
            return XLNetConfig(*args, **kwargs)
        elif "xlm" in model_type:
            return XLMConfig(*args, **kwargs)
        elif "ctrl" in model_type:
            return CTRLConfig(*args, **kwargs)
        elif "albert" in model_type:
            return AlbertConfig(*args, **kwargs)
        elif "camembert" in model_type:
            return CamembertConfig(*args, **kwargs)
        raise ValueError(
            "Unrecognized model identifier in {}. Should contains one of "
            "'distilbert', 'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
            "'xlm', 'roberta', 'ctrl', 'camembert', 'albert'".format(model_type)
        )

    @classmethod
    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 `t5`: T5Config (T5 model)
            - contains `distilbert`: DistilBertConfig (DistilBERT model)
            - contains `albert`: AlbertConfig (ALBERT model)
            - contains `camembert`: CamembertConfig (CamemBERT model)
            - contains `xlm-roberta`: XLMRobertaConfig (XLM-RoBERTa model)
            - contains `roberta`: RobertaConfig (RoBERTa 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 `ctrl` : CTRLConfig (CTRL 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 string with the `identifier name` of a pre-trained model configuration that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                - a path to a `directory` containing a configuration file saved using the :func:`~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.

            resume_download: (`optional`) boolean, default False:
                Do not delete incompletely recieved file. Attempt to resume the download if such a file 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 "t5" in pretrained_model_name_or_path:
            return T5Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
        elif "distilbert" in pretrained_model_name_or_path:
            return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
        elif "albert" in pretrained_model_name_or_path:
            return AlbertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
        elif "camembert" in pretrained_model_name_or_path:
            return CamembertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
        elif "xlm-roberta" in pretrained_model_name_or_path:
            return XLMRobertaConfig.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)
        elif "ctrl" in pretrained_model_name_or_path:
            return CTRLConfig.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', 'xlm', 'roberta', 'distilbert', 'camembert', 'ctrl', 'albert'".format(
                pretrained_model_name_or_path
            )
        )