From ac42049c0877104632cff44d52ddb0a06d927ee0 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 26 Jul 2019 17:08:59 +0200 Subject: [PATCH] add auto models and auto tokenizer --- pytorch_transformers/modeling_auto.py | 230 ++++++++++++++++++++++ pytorch_transformers/tokenization_auto.py | 100 ++++++++++ 2 files changed, 330 insertions(+) create mode 100644 pytorch_transformers/modeling_auto.py create mode 100644 pytorch_transformers/tokenization_auto.py diff --git a/pytorch_transformers/modeling_auto.py b/pytorch_transformers/modeling_auto.py new file mode 100644 index 0000000000..68eb85cbd8 --- /dev/null +++ b/pytorch_transformers/modeling_auto.py @@ -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)) + diff --git a/pytorch_transformers/tokenization_auto.py b/pytorch_transformers/tokenization_auto.py new file mode 100644 index 0000000000..66d0ce51ba --- /dev/null +++ b/pytorch_transformers/tokenization_auto.py @@ -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))