add auto models and auto tokenizer
This commit is contained in:
thomwolf
230
pytorch_transformers/modeling_auto.py
Normal file
230
pytorch_transformers/modeling_auto.py
Normal file
@@ -0,0 +1,230 @@
|
||||
# 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 .modeling_bert import BertConfig, BertModel
|
||||
from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel
|
||||
from .modeling_gpt2 import GPT2Config, GPT2Model
|
||||
from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel
|
||||
from .modeling_xlnet import XLNetConfig, XLNetModel
|
||||
from .modeling_xlm import XLMConfig, XLMModel
|
||||
|
||||
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 `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 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 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 `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)
|
||||
|
||||
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 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 saved configuration `file`.
|
||||
**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.
|
||||
**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.
|
||||
**kwargs**: (`optional`) dict:
|
||||
Dictionary of 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.
|
||||
|
||||
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 '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'".format(pretrained_model_name_or_path))
|
||||
|
||||
|
||||
class AutoModel(object):
|
||||
r"""
|
||||
:class:`~pytorch_transformers.AutoModel` is a generic model class
|
||||
that will be 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.
|
||||
|
||||
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 `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 cannot be instantiated using `__init__()` (throw an error).
|
||||
"""
|
||||
def __init__(self):
|
||||
raise EnvironmentError("AutoModel is designed to be instantiated "
|
||||
"using the `AutoModel.from_pretrained(pretrained_model_name_or_path)` method.")
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
r""" Instantiate a 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 desactivated)
|
||||
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 configuraton 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:
|
||||
return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'openai-gpt' in pretrained_model_name_or_path:
|
||||
return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'gpt2' in pretrained_model_name_or_path:
|
||||
return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'transfo-xl' in pretrained_model_name_or_path:
|
||||
return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'xlnet' in pretrained_model_name_or_path:
|
||||
return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'xlm' in pretrained_model_name_or_path:
|
||||
return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
|
||||
"'xlm'".format(pretrained_model_name_or_path))
|
||||
|
||||
100
pytorch_transformers/tokenization_auto.py
Normal file
100
pytorch_transformers/tokenization_auto.py
Normal file
@@ -0,0 +1,100 @@
|
||||
# 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 .tokenization_bert import BertTokenizer
|
||||
from .tokenization_openai import OpenAIGPTTokenizer
|
||||
from .tokenization_gpt2 import GPT2Tokenizer
|
||||
from .tokenization_transfo_xl import TransfoXLTokenizer
|
||||
from .tokenization_xlnet import XLNetTokenizer
|
||||
from .tokenization_xlm import XLMTokenizer
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class AutoTokenizer(object):
|
||||
r""":class:`~pytorch_transformers.AutoTokenizer` is a generic tokenizer class
|
||||
that will be instantiated as one of the tokenizer classes of the library
|
||||
when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)`
|
||||
class method.
|
||||
|
||||
The `from_pretrained()` method take care of returning the correct tokenizer class instance
|
||||
using pattern matching on the `pretrained_model_name_or_path` string.
|
||||
|
||||
The tokenizer class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `bert`: BertTokenizer (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
|
||||
- contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
|
||||
- contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetTokenizer (XLNet model)
|
||||
- contains `xlm`: XLMTokenizer (XLM model)
|
||||
|
||||
This class cannot be instantiated using `__init__()` (throw an error).
|
||||
"""
|
||||
def __init__(self):
|
||||
raise EnvironmentError("AutoTokenizer is designed to be instantiated "
|
||||
"using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method.")
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
|
||||
r""" Instantiate a one of the tokenizer classes of the library
|
||||
from a pre-trained model vocabulary.
|
||||
|
||||
The tokenizer class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `bert`: BertTokenizer (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
|
||||
- contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
|
||||
- contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetTokenizer (XLNet model)
|
||||
- contains `xlm`: XLMTokenizer (XLM 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 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 saved configuration `file`.
|
||||
**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.
|
||||
|
||||
Examples::
|
||||
|
||||
>>> config = AutoTokenizer.from_pretrained('bert-base-uncased') # Download vocabulary from S3 and cache.
|
||||
>>> config = AutoTokenizer.from_pretrained('./test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
|
||||
|
||||
"""
|
||||
if 'bert' in pretrained_model_name_or_path:
|
||||
return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
|
||||
elif 'openai-gpt' in pretrained_model_name_or_path:
|
||||
return OpenAIGPTTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
|
||||
elif 'gpt2' in pretrained_model_name_or_path:
|
||||
return GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
|
||||
elif 'transfo-xl' in pretrained_model_name_or_path:
|
||||
return TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
|
||||
elif 'xlnet' in pretrained_model_name_or_path:
|
||||
return XLNetTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
|
||||
elif 'xlm' in pretrained_model_name_or_path:
|
||||
return XLMTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
|
||||
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
|
||||
"'xlm'".format(pretrained_model_name_or_path))
|
||||
Reference in New Issue
Block a user