mirror of
https://github.com/huggingface/transformers.git
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246 lines
15 KiB
Python
246 lines
15 KiB
Python
# 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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import torch
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import torch.nn as nn
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from torch.nn import CrossEntropyLoss, MSELoss
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from torch.nn.parameter import Parameter
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from .modeling_bert import BertConfig, BertModel
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from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel
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from .modeling_gpt2 import GPT2Config, GPT2Model
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from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel
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from .modeling_xlnet import XLNetConfig, XLNetModel
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from .modeling_xlm import XLMConfig, XLMModel
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from .modeling_roberta import RobertaConfig, RobertaModel
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from .modeling_utils import PreTrainedModel, SequenceSummary
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logger = logging.getLogger(__name__)
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class AutoConfig(object):
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r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class
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that will be instantiated as one of the configuration classes of the library
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when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method take 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 `bert`: BertConfig (Bert model)
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- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
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- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
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- contains `xlnet`: XLNetConfig (XLNet model)
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- contains `xlm`: XLMConfig (XLM model)
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- contains `roberta`: RobertaConfig (RoBERTa model)
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This class cannot be instantiated using `__init__()` (throw an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoConfig is designed to be instantiated "
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"using the `AutoConfig.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, **kwargs):
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r""" Instantiate a one of the configuration classes of the library
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from a pre-trained model configuration.
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The configuration 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 `bert`: BertConfig (Bert model)
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- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
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- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
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- contains `xlnet`: XLNetConfig (XLNet model)
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- contains `xlm`: XLMConfig (XLM model)
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- contains `roberta`: RobertaConfig (RoBERTa model)
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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 configuration to load from cache
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or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
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- a path to a `directory` containing a configuration file saved
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using the `save_pretrained(save_directory)` method.
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- a path or url to a saved configuration `file`.
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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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**return_unused_kwargs**: (`optional`) bool:
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- If False, then this function returns just the final configuration object.
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- If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs`
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is a dictionary consisting of the key/value pairs whose keys are not configuration attributes:
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ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
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**kwargs**: (`optional`) dict:
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Dictionary of key/value pairs with which to update the configuration object after loading.
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- The values in kwargs of any keys which are configuration attributes will be used
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to override the loaded values.
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- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
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by the `return_unused_kwargs` keyword parameter.
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Examples::
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config = AutoConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
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config = AutoConfig.from_pretrained('./test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
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config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
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config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
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assert config.output_attention == True
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config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
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foo=False, return_unused_kwargs=True)
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assert config.output_attention == True
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assert unused_kwargs == {'foo': False}
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"""
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if 'bert' in pretrained_model_name_or_path:
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return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'gpt2' in pretrained_model_name_or_path:
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return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'transfo-xl' in pretrained_model_name_or_path:
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return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'roberta' in pretrained_model_name_or_path:
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return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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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'".format(pretrained_model_name_or_path))
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class AutoModel(object):
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r"""
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:class:`~pytorch_transformers.AutoModel` 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 `AutoModel.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method take 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 `bert`: BertModel (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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- contains `roberta`: RobertaModel (RoBERTa model)
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This class cannot be instantiated using `__init__()` (throw an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoModel is designed to be instantiated "
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"using the `AutoModel.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""" Instantiate a one of the base model classes of the library
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from a pre-trained model configuration.
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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 `bert`: BertModel (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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- contains `roberta`: RobertaModel (RoBERTa 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
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or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
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- a path to a `directory` containing a configuration file saved
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using the `save_pretrained(save_directory)` method.
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- a path or url to a tensorflow index checkpoint `file` (e.g. `./tf_model/model.ckpt.index`).
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In this case, ``from_tf`` should be set to True and a configuration object should be
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provided as `config` argument. This loading option is slower than converting the TensorFlow
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checkpoint in a PyTorch model using the provided conversion scripts and loading
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the PyTorch model afterwards.
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**model_args**: (`optional`) Sequence:
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All remaning positional arguments will be passed to the underlying model's __init__ function
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**config**: an optional configuration for the model to use instead of an automatically loaded configuation.
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Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with a `shortcut name` of a pre-trained model), or
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- the model was saved using the `save_pretrained(save_directory)` (loaded by suppling the save directory).
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**state_dict**: an optional state dictionnary for the model to use instead of a state dictionary loaded
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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 `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
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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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**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`) dict:
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Dictionary of key, values to update the configuration object after loading.
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Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
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- If a configuration is provided with `config`, **kwargs will be directly passed
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to the underlying model's __init__ method.
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- If a configuration is not provided, **kwargs will be first passed to the pretrained
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model configuration class loading function (`PretrainedConfig.from_pretrained`).
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Each key of **kwargs that corresponds to a configuration attribute
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will be used to override said attribute with the supplied **kwargs value.
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Remaining keys that do not correspond to any configuration attribute will
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be passed to the underlying model's __init__ function.
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Examples::
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model = AutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
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model = AutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = AutoModel.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 = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'bert' in pretrained_model_name_or_path:
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return BertModel.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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return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'gpt2' in pretrained_model_name_or_path:
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return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'transfo-xl' in pretrained_model_name_or_path:
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return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'roberta' in pretrained_model_name_or_path:
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return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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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'".format(pretrained_model_name_or_path))
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