remove derived classes for now
This commit is contained in:
thomwolf
parent
13936a9621
commit
0b524b0848
|
|
@ -3,12 +3,9 @@ AutoModels
|
|||
|
||||
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.
|
||||
|
||||
AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary.
|
||||
AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary:
|
||||
|
||||
There are two types of AutoClasses:
|
||||
|
||||
- ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer``: instantiating these ones will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``)
|
||||
- All the others (``AutoModelWithLMHead``, ``AutoModelForSequenceClassification``...) are standardized Auto classes for finetuning. Instantiating these will create instance of the same class (``AutoModelWithLMHead``, ``AutoModelForSequenceClassification``...) comprising (i) the relevant base model class (as mentioned just above) and (ii) a standard fine-tuning head on top, convenient for the task.
|
||||
Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``).
|
||||
|
||||
|
||||
``AutoConfig``
|
||||
|
|
@ -25,20 +22,6 @@ There are two types of AutoClasses:
|
|||
:members:
|
||||
|
||||
|
||||
``AutoModelWithLMHead``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_transformers.AutoModelWithLMHead
|
||||
:members:
|
||||
|
||||
|
||||
``AutoModelForSequenceClassification``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_transformers.AutoModelForSequenceClassification
|
||||
:members:
|
||||
|
||||
|
||||
``AutoTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
|
|||
from .tokenization_xlm import XLMTokenizer
|
||||
from .tokenization_utils import (PreTrainedTokenizer)
|
||||
|
||||
from .modeling_auto import (AutoConfig, AutoModel, AutoModelForSequenceClassification, AutoModelWithLMHead)
|
||||
from .modeling_auto import (AutoConfig, AutoModel)
|
||||
|
||||
from .modeling_bert import (BertConfig, BertModel, BertForPreTraining,
|
||||
BertForMaskedLM, BertForNextSentencePrediction,
|
||||
|
|
|
|||
|
|
@ -234,269 +234,3 @@ class AutoModel(object):
|
|||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
|
||||
"'xlm'".format(pretrained_model_name_or_path))
|
||||
|
||||
|
||||
class DerivedAutoModel(PreTrainedModel):
|
||||
r"""
|
||||
:class:`~pytorch_transformers.DerivedAutoModel` is a base class for building
|
||||
standardized derived models on top of :class:`~pytorch_transformers.AutoModel` by adding heads
|
||||
|
||||
The `from_pretrained()` method take care of using the correct base 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 `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)
|
||||
|
||||
This class should usually not be instantiated using `__init__()` but `from_pretrained()`.
|
||||
"""
|
||||
config_class = None
|
||||
pretrained_model_archive_map = {}
|
||||
load_tf_weights = lambda model, config, path: None
|
||||
base_model_prefix = "transformer"
|
||||
|
||||
def __init__(self, base_model):
|
||||
super(DerivedAutoModel, self).__init__(base_model.config)
|
||||
self.transformer = base_model
|
||||
|
||||
def init_weights(self, module):
|
||||
""" Initialize the weights. Use the base model initialization function.
|
||||
"""
|
||||
self.transformer.init_weights(module)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
r""" Instantiate a :class:`~pytorch_transformers.DerivedAutoModel` with one of the base model classes of the library
|
||||
from a pre-trained model configuration.
|
||||
|
||||
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 `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)
|
||||
|
||||
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 and cache if not already stored in cache (e.g. 'bert-base-uncased').
|
||||
- a path to a `directory` containing a configuration file saved
|
||||
using the `save_pretrained(save_directory)` method.
|
||||
- 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 option 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:
|
||||
All remaning positional arguments will be passed to the underlying model's __init__ function
|
||||
**config**: an optional 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 a `shortcut name` of a pre-trained model), or
|
||||
- the model was saved using the `save_pretrained(save_directory)` (loaded by suppling the save directory).
|
||||
**state_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 `save_pretrained(dir)` and `from_pretrained(save_directory)` 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.
|
||||
**output_loading_info**: (`optional`) boolean:
|
||||
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
|
||||
**kwargs**: (`optional`) dict:
|
||||
Dictionary of key, values to update the configuration object after loading.
|
||||
Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
|
||||
|
||||
- If a configuration is provided with `config`, **kwargs will be directly passed
|
||||
to the underlying model's __init__ method.
|
||||
- If a configuration is not provided, **kwargs will be first passed to the pretrained
|
||||
model configuration class loading function (`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 = AutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
|
||||
model = AutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
||||
model = AutoModel.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 = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
||||
|
||||
"""
|
||||
if 'bert' in pretrained_model_name_or_path:
|
||||
base_model_class = BertModel
|
||||
elif 'openai-gpt' in pretrained_model_name_or_path:
|
||||
base_model_class = OpenAIGPTModel
|
||||
elif 'gpt2' in pretrained_model_name_or_path:
|
||||
base_model_class = GPT2Model
|
||||
elif 'transfo-xl' in pretrained_model_name_or_path:
|
||||
base_model_class = TransfoXLModel
|
||||
elif 'xlnet' in pretrained_model_name_or_path:
|
||||
base_model_class = XLNetModel
|
||||
elif 'xlm' in pretrained_model_name_or_path:
|
||||
base_model_class = XLMModel
|
||||
else:
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
|
||||
"'xlm'".format(pretrained_model_name_or_path))
|
||||
|
||||
# Get a pretrained base_model
|
||||
base_model = base_model_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
|
||||
# Create our derived model
|
||||
model = cls(base_model)
|
||||
|
||||
# Setup class attribute from the base model class
|
||||
model.config_class = base_model.config_class
|
||||
model.pretrained_model_archive_map = base_model.pretrained_model_archive_map
|
||||
model.load_tf_weights = base_model.load_tf_weights
|
||||
|
||||
return model
|
||||
|
||||
|
||||
class AutoModelWithLMHead(DerivedAutoModel):
|
||||
r"""
|
||||
:class:`~pytorch_transformers.AutoModelWithLMHead` is a base class for language modeling
|
||||
that contains
|
||||
|
||||
- a base model instantiated as one of the base model classes of the library when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)` class method, and .
|
||||
- a language modeling head on top of the base model.
|
||||
|
||||
The `from_pretrained()` method take care of using the correct base 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 `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)
|
||||
|
||||
This class should usually not be instantiated using `__init__()` but `from_pretrained()`.
|
||||
"""
|
||||
|
||||
def __init__(self, base_model):
|
||||
super(AutoModelWithLMHead, self).__init__(base_model)
|
||||
config = base_model.config
|
||||
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
self.apply(self.init_weights)
|
||||
self.tie_weights()
|
||||
|
||||
def tie_weights(self):
|
||||
""" Make sure we are sharing the input and output embeddings.
|
||||
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
|
||||
"""
|
||||
# get input embeddings - whatever the model is
|
||||
input_embeddings = self.transformer.resize_token_embeddings(new_num_tokens=None)
|
||||
|
||||
# tie of clone (torchscript) embeddings
|
||||
self._tie_or_clone_weights(self.lm_head, input_embeddings)
|
||||
|
||||
def forward(self, input_ids, **kwargs):
|
||||
labels = kwargs.pop('labels', None) # Python 2 compatibility...
|
||||
|
||||
transformer_outputs = self.transformer(input_ids, **kwargs)
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
lm_logits = self.lm_head(hidden_states)
|
||||
|
||||
outputs = (lm_logits,) + transformer_outputs[1:]
|
||||
if labels is not None:
|
||||
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
||||
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)),
|
||||
labels.view(-1))
|
||||
outputs = (loss,) + outputs
|
||||
|
||||
return outputs # (loss), lm_logits, presents, (all hidden_states), (attentions)
|
||||
|
||||
|
||||
AUTO_MODEL_SEQUENCE_SUMMARY_DEFAULTS = {
|
||||
'num_labels': 2,
|
||||
'summary_type': 'first',
|
||||
'summary_use_proj': True,
|
||||
'summary_activation': None,
|
||||
'summary_proj_to_labels': True,
|
||||
'summary_first_dropout': 0.1
|
||||
}
|
||||
|
||||
|
||||
|
||||
class AutoModelForSequenceClassification(DerivedAutoModel):
|
||||
r"""
|
||||
:class:`~pytorch_transformers.AutoModelForSequenceClassification` is a class for sequence classification
|
||||
that contains
|
||||
|
||||
- a base model instantiated as one of the base model classes of the library when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)` class method, and .
|
||||
- a classification head on top of the base model.
|
||||
|
||||
The `from_pretrained()` method take care of using the correct base 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 `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)
|
||||
|
||||
This class should usually not be instantiated using `__init__()` but `from_pretrained()`.
|
||||
"""
|
||||
|
||||
def __init__(self, base_model):
|
||||
super(AutoModelForSequenceClassification, self).__init__(base_model)
|
||||
# Complete configuration with defaults if necessary
|
||||
config = base_model.config
|
||||
for key, value in AUTO_MODEL_SEQUENCE_SUMMARY_DEFAULTS.items():
|
||||
if not hasattr(config, key):
|
||||
setattr(config, key, value)
|
||||
|
||||
# Update base model and derived model config
|
||||
self.transformer.config = config
|
||||
self.config = config
|
||||
|
||||
self.num_labels = config.num_labels
|
||||
self.sequence_summary = SequenceSummary(config)
|
||||
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, cls_index, **kwargs):
|
||||
labels = kwargs.pop('labels', None) # Python 2 compatibility...
|
||||
|
||||
transformer_outputs = self.transformer(input_ids, **kwargs)
|
||||
|
||||
output = transformer_outputs[0]
|
||||
logits = self.sequence_summary(output, cls_index=cls_index)
|
||||
|
||||
outputs = (logits,) + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
|
||||
|
||||
if labels is not None:
|
||||
if self.num_labels == 1:
|
||||
# We are doing regression
|
||||
loss_fct = MSELoss()
|
||||
loss = loss_fct(logits.view(-1), labels.view(-1))
|
||||
else:
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
||||
outputs = (loss,) + outputs
|
||||
|
||||
return outputs
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ import shutil
|
|||
import pytest
|
||||
import logging
|
||||
|
||||
from pytorch_transformers import AutoConfig, BertConfig, AutoModel, BertModel, AutoModelForSequenceClassification, AutoModelWithLMHead
|
||||
from pytorch_transformers import AutoConfig, BertConfig, AutoModel, BertModel
|
||||
from pytorch_transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
|
||||
from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor)
|
||||
|
|
@ -42,14 +42,6 @@ class AutoModelTest(unittest.TestCase):
|
|||
for value in loading_info.values():
|
||||
self.assertEqual(len(value), 0)
|
||||
|
||||
model = AutoModelForSequenceClassification.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertIsInstance(getattr(model, model.base_model_prefix), BertModel)
|
||||
|
||||
model = AutoModelWithLMHead.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertIsInstance(getattr(model, model.base_model_prefix), BertModel)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
|
|
|||
Loading…
Reference in New Issue