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modeling_flax_auto.py
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modeling_flax_auto.py
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# coding=utf-8
# Copyright 2018 The Google Flax Team Authors and 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 collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bert.modeling_flax_bert import FlaxBertModel
from ..roberta.modeling_flax_roberta import FlaxRobertaModel
from .configuration_auto import AutoConfig, BertConfig, RobertaConfig
logger = logging.get_logger(__name__)
FLAX_MODEL_MAPPING = OrderedDict(
[
(RobertaConfig, FlaxRobertaModel),
(BertConfig, FlaxBertModel),
]
)
class FlaxAutoModel(object):
r"""
:class:`~transformers.FlaxAutoModel` is a generic model class that will be instantiated as one of the base model
classes of the library when created with the `FlaxAutoModel.from_pretrained(pretrained_model_name_or_path)` or the
`FlaxAutoModel.from_config(config)` class methods.
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"FlaxAutoModel is designed to be instantiated "
"using the `FlaxAutoModel.from_pretrained(pretrained_model_name_or_path)` or "
"`FlaxAutoModel.from_config(config)` methods."
)
@classmethod
def from_config(cls, config):
r"""
Instantiates one of the base model classes of the library from a configuration.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
- isInstance of `roberta` configuration class: :class:`~transformers.FlaxRobertaModel` (RoBERTa model)
- isInstance of `bert` configuration class: :class:`~transformers.FlaxBertModel` (Bert model
Examples::
config = BertConfig.from_pretrained('bert-base-uncased')
# Download configuration from huggingface.co and cache.
model = FlaxAutoModel.from_config(config)
# E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in FLAX_MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
f"Unrecognized configuration class {config.__class__} "
f"for this kind of FlaxAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in FLAX_MODEL_MAPPING.keys())}."
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Instantiates one of the base model classes of the library from a pre-trained model configuration.
The `from_pretrained()` method takes care of returning the correct model class instance based on the
`model_type` property of the config object, or when it's missing, falling back to 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 `roberta`: :class:`~transformers.FlaxRobertaModel` (RoBERTa model)
- contains `bert`: :class:`~transformers.FlaxBertModel` (Bert model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) To
train the model, you should first set it back in training mode with `model.train()`
Args:
pretrained_model_name_or_path: either:
- a string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co. Valid
model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a user or
organization name, like ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing model weights saved using
:func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `pytorch index checkpoint file` (e.g. `./pt_model/pytorch_model.bin`). In this
case, ``from_pt`` should be set to True and a configuration object should be provided as ``config``
argument.
model_args: (`optional`) Sequence of positional arguments:
All remaining positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a
pretrained model), or
- the model was saved using :func:`~transformers.FlaxPreTrainedModel.save_pretrained` and is reloaded
by supplying the save directory.
- the model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
configuration JSON file named `config.json` is found in the directory.
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.
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 received 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.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionary containing missing keys, unexpected keys and error
messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
These arguments will be passed to the configuration and the model.
Examples::
model = FlaxAutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from huggingface.co and cache.
model = FlaxAutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
assert model.config.output_attention == True
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
for config_class, model_class in FLAX_MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
raise ValueError(
f"Unrecognized configuration class {config.__class__} "
f"for this kind of FlaxAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in FLAX_MODEL_MAPPING.keys())}"
)

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