diff --git a/pytorch_transformers/__init__.py b/pytorch_transformers/__init__.py index 62e3b8c47b8..424e22eb523 100644 --- a/pytorch_transformers/__init__.py +++ b/pytorch_transformers/__init__.py @@ -10,7 +10,8 @@ from .tokenization_roberta import RobertaTokenizer from .tokenization_utils import (PreTrainedTokenizer) -from .modeling_auto import (AutoConfig, AutoModel) +from .modeling_auto import (AutoConfig, AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering, + AutoModelWithLMHead) from .modeling_bert import (BertConfig, BertPreTrainedModel, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, diff --git a/pytorch_transformers/modeling_auto.py b/pytorch_transformers/modeling_auto.py index 516107c40bb..9171438f97c 100644 --- a/pytorch_transformers/modeling_auto.py +++ b/pytorch_transformers/modeling_auto.py @@ -23,13 +23,13 @@ import torch.nn as nn from torch.nn import CrossEntropyLoss, MSELoss from torch.nn.parameter import Parameter -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 -from .modeling_roberta import RobertaConfig, RobertaModel +from .modeling_bert import BertConfig, BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering +from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel, OpenAIGPTLMHeadModel +from .modeling_gpt2 import GPT2Config, GPT2Model, GPT2LMHeadModel +from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel +from .modeling_xlnet import XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering +from .modeling_xlm import XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering +from .modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification from .modeling_utils import PreTrainedModel, SequenceSummary @@ -137,20 +137,20 @@ class AutoModel(object): 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 + 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 base model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): + - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: GPT2Model (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLModel (Transformer-XL model) - contains `xlnet`: XLNetModel (XLNet model) - contains `xlm`: XLMModel (XLM model) - - contains `roberta`: RobertaModel (RoBERTa model) - This class cannot be instantiated using `__init__()` (throw an error). + This class cannot be instantiated using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError("AutoModel is designed to be instantiated " @@ -158,18 +158,18 @@ class AutoModel(object): @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 + r""" Instantiates 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 `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: GPT2Model (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLModel (Transformer-XL model) - contains `xlnet`: XLNetModel (XLNet model) - contains `xlm`: XLMModel (XLM model) - - contains `roberta`: RobertaModel (RoBERTa 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()` @@ -186,12 +186,12 @@ class AutoModel(object): 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. + All remaining 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 configuration. 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 + - the model was saved using the `save_pretrained(save_directory)` (loaded by supplying the save directory). + **state_dict**: an optional state dictionary 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 @@ -200,7 +200,7 @@ class AutoModel(object): 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. + Set to ``True`` to also return a dictionary 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``. @@ -243,3 +243,328 @@ class AutoModel(object): raise ValueError("Unrecognized model identifier in {}. Should contains one of " "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " "'xlm', 'roberta'".format(pretrained_model_name_or_path)) + + + +class AutoModelWithLMHead(object): + r""" + :class:`~pytorch_transformers.AutoModelWithLMHead` is a generic model class + that will be instantiated as one of the language modeling model classes of the library + when created with the `AutoModelWithLMHead.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 `roberta`: RobertaForMaskedLM (RoBERTa model) + - contains `bert`: BertForMaskedLM (Bert model) + - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) + - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model) + - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model) + - contains `xlnet`: XLNetLMHeadModel (XLNet model) + - contains `xlm`: XLMWithLMHeadModel (XLM model) + + This class cannot be instantiated using `__init__()` (throws an error). + """ + def __init__(self): + raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated " + "using the `AutoModelWithLMHead.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 language modeling 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 `roberta`: RobertaForMaskedLM (RoBERTa model) + - contains `bert`: BertForMaskedLM (Bert model) + - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) + - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model) + - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model) + - contains `xlnet`: XLNetLMHeadModel (XLNet model) + - contains `xlm`: XLMWithLMHeadModel (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 remaining 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 configuration. + 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 supplying the save directory). + **state_dict**: an optional state dictionary 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 dictionary 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 = AutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. + model = AutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` + model = AutoModelWithLMHead.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 = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) + + """ + if 'roberta' in pretrained_model_name_or_path: + return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'bert' in pretrained_model_name_or_path: + return BertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'openai-gpt' in pretrained_model_name_or_path: + return OpenAIGPTLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'gpt2' in pretrained_model_name_or_path: + return GPT2LMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'transfo-xl' in pretrained_model_name_or_path: + return TransfoXLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'xlnet' in pretrained_model_name_or_path: + return XLNetLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'xlm' in pretrained_model_name_or_path: + return XLMWithLMHeadModel.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', 'roberta'".format(pretrained_model_name_or_path)) + + +class AutoModelForSequenceClassification(object): + r""" + :class:`~pytorch_transformers.AutoModelForSequenceClassification` is a generic model class + that will be instantiated as one of the sequence classification model classes of the library + when created with the `AutoModelForSequenceClassification.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 `roberta`: RobertaForSequenceClassification (RoBERTa model) + - contains `bert`: BertForSequenceClassification (Bert model) + - contains `xlnet`: XLNetForSequenceClassification (XLNet model) + - contains `xlm`: XLMForSequenceClassification (XLM model) + + This class cannot be instantiated using `__init__()` (throws an error). + """ + def __init__(self): + raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated " + "using the `AutoModelWithLMHead.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 sequence classification 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 `roberta`: RobertaForSequenceClassification (RoBERTa model) + - contains `bert`: BertForSequenceClassification (Bert model) + - contains `xlnet`: XLNetForSequenceClassification (XLNet model) + - contains `xlm`: XLMForSequenceClassification (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 remaining 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 configuration. + 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 supplying the save directory). + **state_dict**: an optional state dictionary 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 dictionary 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 = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. + model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` + model = AutoModelForSequenceClassification.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 = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) + + """ + if 'roberta' in pretrained_model_name_or_path: + return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'bert' in pretrained_model_name_or_path: + return BertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'xlnet' in pretrained_model_name_or_path: + return XLNetForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'xlm' in pretrained_model_name_or_path: + return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + + raise ValueError("Unrecognized model identifier in {}. Should contains one of " + "'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path)) + + +class AutoModelForQuestionAnswering(object): + r""" + :class:`~pytorch_transformers.AutoModelForQuestionAnswering` is a generic model class + that will be instantiated as one of the question answering model classes of the library + when created with the `AutoModelForQuestionAnswering.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 `bert`: BertForQuestionAnswering (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("AutoModelWithLMHead is designed to be instantiated " + "using the `AutoModelWithLMHead.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 `bert`: BertForQuestionAnswering (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 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 remaining 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 configuration. + 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 supplying the save directory). + **state_dict**: an optional state dictionary 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 dictionary 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 = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. + model = AutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` + model = AutoModelForQuestionAnswering.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 = AutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) + + """ + if 'bert' in pretrained_model_name_or_path: + return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'xlnet' in pretrained_model_name_or_path: + return XLNetForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'xlm' in pretrained_model_name_or_path: + return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + + raise ValueError("Unrecognized model identifier in {}. Should contains one of " + "'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path))