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add tf auto models + tests
This commit is contained in:
thomwolf
parent
600a42329b
commit
705237b4ec
@ -96,8 +96,15 @@ if _tf_available:
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logger.info("TensorFlow version {} available.".format(tf.__version__))
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from .modeling_tf_utils import TFPreTrainedModel
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from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
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TFAutoModelWithLMHead)
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from .modeling_tf_bert import (TFBertPreTrainedModel, TFBertModel, TFBertForPreTraining,
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TFBertForMaskedLM, TFBertForNextSentencePrediction, load_bert_pt_weights_in_tf)
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TFBertForMaskedLM, TFBertForNextSentencePrediction,
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TFBertForSequenceClassification, TFBertForMultipleChoice,
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TFBertForTokenClassification, TFBertForQuestionAnswering,
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load_bert_pt_weights_in_tf,
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TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
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# Files and general utilities
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488
pytorch_transformers/modeling_tf_auto.py
Normal file
488
pytorch_transformers/modeling_tf_auto.py
Normal file
@ -0,0 +1,488 @@
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# coding=utf-8
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# Copyright 2018 The HuggingFace Inc. team.
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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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""" Auto Model class. """
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from __future__ import absolute_import, division, print_function, unicode_literals
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import logging
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from .modeling_tf_bert import TFBertModel, TFBertForMaskedLM, TFBertForSequenceClassification, TFBertForQuestionAnswering
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from .file_utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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class TFAutoModel(object):
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r"""
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:class:`~pytorch_transformers.TFAutoModel` is a generic model class
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that will be instantiated as one of the base model classes of the library
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when created with the `TFAutoModel.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The base model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `distilbert`: DistilBertModel (DistilBERT model)
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- contains `roberta`: RobertaModel (RoBERTa model)
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- contains `bert`: TFBertModel (Bert model)
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- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
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- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
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- contains `xlnet`: XLNetModel (XLNet model)
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- contains `xlm`: XLMModel (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("TFAutoModel is designed to be instantiated "
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"using the `TFAutoModel.from_pretrained(pretrained_model_name_or_path)` method.")
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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r""" Instantiates one of the base model classes of the library
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from a pre-trained model configuration.
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The model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `distilbert`: DistilBertModel (DistilBERT model)
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- contains `roberta`: RobertaModel (RoBERTa model)
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- contains `bert`: TFBertModel (Bert model)
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- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
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- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
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- contains `xlnet`: XLNetModel (XLNet model)
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- contains `xlm`: XLMModel (XLM model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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Params:
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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 to load from cache or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
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- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
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model_args: (`optional`) Sequence of positional arguments:
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All remaning positional arguments will be passed to the underlying model's ``__init__`` method
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config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
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Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
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- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
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- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
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state_dict: (`optional`) dict:
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an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
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This option can be used if you want to create a model from a pretrained configuration but load your own weights.
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In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
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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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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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output_loading_info: (`optional`) boolean:
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Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
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kwargs: (`optional`) Remaining dictionary of keyword arguments:
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Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
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- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
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- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
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Examples::
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model = TFAutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
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model = TFAutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = TFAutoModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
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assert model.config.output_attention == True
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# Loading from a TF checkpoint file instead of a PyTorch model (slower)
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config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
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model = TFAutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'distilbert' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'roberta' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'bert' in pretrained_model_name_or_path:
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return TFBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'gpt2' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'transfo-xl' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'xlnet' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'xlm' in pretrained_model_name_or_path:
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raise NotImplementedError
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raise ValueError("Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm', 'roberta'".format(pretrained_model_name_or_path))
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class TFAutoModelWithLMHead(object):
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r"""
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:class:`~pytorch_transformers.TFAutoModelWithLMHead` is a generic model class
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that will be instantiated as one of the language modeling model classes of the library
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when created with the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
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- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
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- contains `bert`: TFBertForMaskedLM (Bert model)
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- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
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- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
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- contains `xlnet`: XLNetLMHeadModel (XLNet model)
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- contains `xlm`: XLMWithLMHeadModel (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("TFAutoModelWithLMHead is designed to be instantiated "
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"using the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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r""" Instantiates one of the language modeling model classes of the library
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from a pre-trained model configuration.
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
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- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
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- contains `bert`: TFBertForMaskedLM (Bert model)
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- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
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- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
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- contains `xlnet`: XLNetLMHeadModel (XLNet model)
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- contains `xlm`: XLMWithLMHeadModel (XLM model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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Params:
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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 to load from cache or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
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- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
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model_args: (`optional`) Sequence of positional arguments:
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All remaning positional arguments will be passed to the underlying model's ``__init__`` method
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config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
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Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
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- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
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- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
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state_dict: (`optional`) dict:
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an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
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This option can be used if you want to create a model from a pretrained configuration but load your own weights.
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In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
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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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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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output_loading_info: (`optional`) boolean:
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Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
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kwargs: (`optional`) Remaining dictionary of keyword arguments:
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Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
|
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|
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- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
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- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
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Examples::
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model = TFAutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
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model = TFAutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = TFAutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
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assert model.config.output_attention == True
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# Loading from a TF checkpoint file instead of a PyTorch model (slower)
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config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
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model = TFAutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'distilbert' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'roberta' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'bert' in pretrained_model_name_or_path:
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return TFBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'gpt2' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'transfo-xl' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'xlnet' in pretrained_model_name_or_path:
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raise NotImplementedError
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elif 'xlm' in pretrained_model_name_or_path:
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raise NotImplementedError
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raise ValueError("Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm', 'roberta'".format(pretrained_model_name_or_path))
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class TFAutoModelForSequenceClassification(object):
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r"""
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:class:`~pytorch_transformers.TFAutoModelForSequenceClassification` is a generic model class
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that will be instantiated as one of the sequence classification model classes of the library
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when created with the `TFAutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
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class method.
|
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|
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The `from_pretrained()` method takes care of returning the correct model class instance
|
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using pattern matching on the `pretrained_model_name_or_path` string.
|
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|
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The model class to instantiate is selected as the first pattern matching
|
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in the `pretrained_model_name_or_path` string (in the following order):
|
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- contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
|
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- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
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- contains `bert`: TFBertForSequenceClassification (Bert model)
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- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
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- contains `xlm`: XLMForSequenceClassification (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("TFAutoModelWithLMHead is designed to be instantiated "
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"using the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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r""" Instantiates one of the sequence classification model classes of the library
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from a pre-trained model configuration.
|
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|
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The `from_pretrained()` method takes care of returning the correct model class instance
|
||||
using pattern matching on the `pretrained_model_name_or_path` string.
|
||||
|
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The 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`: DistilBertForSequenceClassification (DistilBERT model)
|
||||
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
|
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- contains `bert`: TFBertForSequenceClassification (Bert model)
|
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- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
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- contains `xlm`: XLMForSequenceClassification (XLM model)
|
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|
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
|
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To train the model, you should first set it back in training mode with `model.train()`
|
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|
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Params:
|
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pretrained_model_name_or_path: either:
|
||||
|
||||
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
|
||||
- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
|
||||
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
|
||||
|
||||
model_args: (`optional`) Sequence of positional arguments:
|
||||
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
|
||||
|
||||
config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
|
||||
Configuration for the model to use instead of an automatically loaded configuation. 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:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
|
||||
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
|
||||
|
||||
state_dict: (`optional`) dict:
|
||||
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
|
||||
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
|
||||
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
|
||||
|
||||
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.
|
||||
|
||||
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 dictionnary containing missing keys, unexpected keys and error messages.
|
||||
|
||||
kwargs: (`optional`) Remaining dictionary of keyword arguments:
|
||||
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
|
||||
|
||||
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
|
||||
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
|
||||
|
||||
Examples::
|
||||
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
|
||||
assert model.config.output_attention == True
|
||||
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
|
||||
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
||||
|
||||
"""
|
||||
if 'distilbert' in pretrained_model_name_or_path:
|
||||
raise NotImplementedError
|
||||
elif 'roberta' in pretrained_model_name_or_path:
|
||||
raise NotImplementedError
|
||||
elif 'bert' in pretrained_model_name_or_path:
|
||||
return TFBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'xlnet' in pretrained_model_name_or_path:
|
||||
raise NotImplementedError
|
||||
elif 'xlm' in pretrained_model_name_or_path:
|
||||
raise NotImplementedError
|
||||
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path))
|
||||
|
||||
|
||||
class TFAutoModelForQuestionAnswering(object):
|
||||
r"""
|
||||
:class:`~pytorch_transformers.TFAutoModelForQuestionAnswering` is a generic model class
|
||||
that will be instantiated as one of the question answering model classes of the library
|
||||
when created with the `TFAutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
|
||||
class method.
|
||||
|
||||
The `from_pretrained()` method takes care of returning the correct model class instance
|
||||
using pattern matching on the `pretrained_model_name_or_path` string.
|
||||
|
||||
The 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`: DistilBertForQuestionAnswering (DistilBERT model)
|
||||
- contains `bert`: TFBertForQuestionAnswering (Bert model)
|
||||
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
||||
- contains `xlm`: XLMForQuestionAnswering (XLM model)
|
||||
|
||||
This class cannot be instantiated using `__init__()` (throws an error).
|
||||
"""
|
||||
def __init__(self):
|
||||
raise EnvironmentError("TFAutoModelWithLMHead is designed to be instantiated "
|
||||
"using the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
r""" Instantiates one of the question answering model classes of the library
|
||||
from a pre-trained model configuration.
|
||||
|
||||
The `from_pretrained()` method takes care of returning the correct model class instance
|
||||
using pattern matching on the `pretrained_model_name_or_path` string.
|
||||
|
||||
The 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`: DistilBertForQuestionAnswering (DistilBERT model)
|
||||
- contains `bert`: TFBertForQuestionAnswering (Bert model)
|
||||
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
||||
- contains `xlm`: XLMForQuestionAnswering (XLM 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()`
|
||||
|
||||
Params:
|
||||
pretrained_model_name_or_path: either:
|
||||
|
||||
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
|
||||
- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
|
||||
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
|
||||
|
||||
model_args: (`optional`) Sequence of positional arguments:
|
||||
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
|
||||
|
||||
config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
|
||||
Configuration for the model to use instead of an automatically loaded configuation. 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:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
|
||||
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
|
||||
|
||||
state_dict: (`optional`) dict:
|
||||
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
|
||||
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
|
||||
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
|
||||
|
||||
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.
|
||||
|
||||
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 dictionnary containing missing keys, unexpected keys and error messages.
|
||||
|
||||
kwargs: (`optional`) Remaining dictionary of keyword arguments:
|
||||
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
|
||||
|
||||
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
|
||||
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
|
||||
|
||||
Examples::
|
||||
|
||||
model = TFAutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
|
||||
model = TFAutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
||||
model = TFAutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
|
||||
assert model.config.output_attention == True
|
||||
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
|
||||
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
|
||||
model = TFAutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
||||
|
||||
"""
|
||||
if 'distilbert' in pretrained_model_name_or_path:
|
||||
raise NotImplementedError
|
||||
elif 'bert' in pretrained_model_name_or_path:
|
||||
return TFBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'xlnet' in pretrained_model_name_or_path:
|
||||
raise NotImplementedError
|
||||
elif 'xlm' in pretrained_model_name_or_path:
|
||||
raise NotImplementedError
|
||||
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path))
|
||||
@ -170,9 +170,6 @@ class TFPreTrainedModel(tf.keras.Model):
|
||||
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 dictionnary containing missing keys, unexpected keys and error messages.
|
||||
|
||||
kwargs: (`optional`) Remaining dictionary of keyword arguments:
|
||||
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
|
||||
|
||||
@ -195,7 +192,6 @@ class TFPreTrainedModel(tf.keras.Model):
|
||||
from_pt = kwargs.pop('from_pt', False)
|
||||
force_download = kwargs.pop('force_download', False)
|
||||
proxies = kwargs.pop('proxies', None)
|
||||
output_loading_info = kwargs.pop('output_loading_info', False)
|
||||
|
||||
# Load config
|
||||
if config is None:
|
||||
@ -258,11 +254,4 @@ class TFPreTrainedModel(tf.keras.Model):
|
||||
|
||||
ret = model(inputs, training=False) # Make sure restore ops are run
|
||||
|
||||
# if hasattr(model, 'tie_weights'):
|
||||
# model.tie_weights() # TODO make sure word embedding weights are still tied
|
||||
|
||||
if output_loading_info:
|
||||
loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs}
|
||||
return model, loading_info
|
||||
|
||||
return model
|
||||
|
||||
85
pytorch_transformers/tests/modeling_tf_auto_test.py
Normal file
85
pytorch_transformers/tests/modeling_tf_auto_test.py
Normal file
@ -0,0 +1,85 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Google AI Language Team Authors.
|
||||
#
|
||||
# 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.
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import unittest
|
||||
import shutil
|
||||
import pytest
|
||||
import logging
|
||||
|
||||
try:
|
||||
from pytorch_transformers import (AutoConfig, BertConfig,
|
||||
TFAutoModel, TFBertModel,
|
||||
TFAutoModelWithLMHead, TFBertForMaskedLM,
|
||||
TFAutoModelForSequenceClassification, TFBertForSequenceClassification,
|
||||
TFAutoModelForQuestionAnswering, TFBertForQuestionAnswering)
|
||||
from pytorch_transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
|
||||
from .modeling_common_test import (CommonTestCases, ids_tensor)
|
||||
from .configuration_common_test import ConfigTester
|
||||
except ImportError:
|
||||
pytestmark = pytest.mark.skip("Require TensorFlow")
|
||||
|
||||
|
||||
class TFAutoModelTest(unittest.TestCase):
|
||||
def test_model_from_pretrained(self):
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
config = AutoConfig.from_pretrained(model_name)
|
||||
self.assertIsNotNone(config)
|
||||
self.assertIsInstance(config, BertConfig)
|
||||
|
||||
model = TFAutoModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertIsInstance(model, TFBertModel)
|
||||
|
||||
def test_lmhead_model_from_pretrained(self):
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
config = AutoConfig.from_pretrained(model_name)
|
||||
self.assertIsNotNone(config)
|
||||
self.assertIsInstance(config, BertConfig)
|
||||
|
||||
model = TFAutoModelWithLMHead.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertIsInstance(model, TFBertForMaskedLM)
|
||||
|
||||
def test_sequence_classification_model_from_pretrained(self):
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
config = AutoConfig.from_pretrained(model_name)
|
||||
self.assertIsNotNone(config)
|
||||
self.assertIsInstance(config, BertConfig)
|
||||
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertIsInstance(model, TFBertForSequenceClassification)
|
||||
|
||||
def test_question_answering_model_from_pretrained(self):
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
config = AutoConfig.from_pretrained(model_name)
|
||||
self.assertIsNotNone(config)
|
||||
self.assertIsInstance(config, BertConfig)
|
||||
|
||||
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertIsInstance(model, TFBertForQuestionAnswering)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in New Issue
Block a user