mirror of
https://github.com/huggingface/transformers.git
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503 lines
28 KiB
Python
503 lines
28 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_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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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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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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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`: 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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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`: 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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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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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 DerivedAutoModel(PreTrainedModel):
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r"""
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:class:`~pytorch_transformers.DerivedAutoModel` is a base class for building
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standardized derived models on top of :class:`~pytorch_transformers.AutoModel` by adding heads
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The `from_pretrained()` method take care of using the correct base 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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This class should usually not be instantiated using `__init__()` but `from_pretrained()`.
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"""
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config_class = None
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pretrained_model_archive_map = {}
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load_tf_weights = lambda model, config, path: None
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base_model_prefix = "transformer"
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def __init__(self, base_model):
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super(DerivedAutoModel, self).__init__(base_model.config)
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self.transformer = base_model
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def init_weights(self, module):
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""" Initialize the weights. Use the base model initialization function.
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"""
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self.transformer.init_weights(module)
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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 :class:`~pytorch_transformers.DerivedAutoModel` with 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`: 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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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
|
|
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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|
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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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base_model_class = BertModel
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elif 'openai-gpt' in pretrained_model_name_or_path:
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base_model_class = OpenAIGPTModel
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elif 'gpt2' in pretrained_model_name_or_path:
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base_model_class = GPT2Model
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elif 'transfo-xl' in pretrained_model_name_or_path:
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base_model_class = TransfoXLModel
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elif 'xlnet' in pretrained_model_name_or_path:
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base_model_class = XLNetModel
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elif 'xlm' in pretrained_model_name_or_path:
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base_model_class = XLMModel
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else:
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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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# Get a pretrained base_model
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base_model = base_model_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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# Create our derived model
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model = cls(base_model)
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# Setup class attribute from the base model class
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model.config_class = base_model.config_class
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model.pretrained_model_archive_map = base_model.pretrained_model_archive_map
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model.load_tf_weights = base_model.load_tf_weights
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return model
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class AutoModelWithLMHead(DerivedAutoModel):
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r"""
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:class:`~pytorch_transformers.AutoModelWithLMHead` is a base class for language modeling
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that contains
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- 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 .
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- a language modeling head on top of the base model.
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The `from_pretrained()` method take care of using the correct base 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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This class should usually not be instantiated using `__init__()` but `from_pretrained()`.
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"""
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def __init__(self, base_model):
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super(AutoModelWithLMHead, self).__init__(base_model)
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config = base_model.config
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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self.apply(self.init_weights)
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self.tie_weights()
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def tie_weights(self):
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""" Make sure we are sharing the input and output embeddings.
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Export to TorchScript can't handle parameter sharing so we are cloning them instead.
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"""
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# get input embeddings - whatever the model is
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input_embeddings = self.transformer.resize_token_embeddings(new_num_tokens=None)
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# tie of clone (torchscript) embeddings
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self._tie_or_clone_weights(self.lm_head, input_embeddings)
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def forward(self, input_ids, **kwargs):
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labels = kwargs.pop('labels', None) # Python 2 compatibility...
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transformer_outputs = self.transformer(input_ids, **kwargs)
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hidden_states = transformer_outputs[0]
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lm_logits = self.lm_head(hidden_states)
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outputs = (lm_logits,) + transformer_outputs[1:]
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if labels is not None:
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loss_fct = CrossEntropyLoss(ignore_index=-1)
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loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)),
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labels.view(-1))
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outputs = (loss,) + outputs
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return outputs # (loss), lm_logits, presents, (all hidden_states), (attentions)
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AUTO_MODEL_SEQUENCE_SUMMARY_DEFAULTS = {
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'num_labels': 2,
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'summary_type': 'first',
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'summary_use_proj': True,
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'summary_activation': None,
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'summary_proj_to_labels': True,
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'summary_first_dropout': 0.1
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}
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class AutoModelForSequenceClassification(DerivedAutoModel):
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r"""
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:class:`~pytorch_transformers.AutoModelForSequenceClassification` is a class for sequence classification
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that contains
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- 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 .
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- a classification head on top of the base model.
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|
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The `from_pretrained()` method take care of using the correct base 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)
|
|
- 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()`.
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|
"""
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|
|
|
def __init__(self, base_model):
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super(AutoModelForSequenceClassification, self).__init__(base_model)
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# Complete configuration with defaults if necessary
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|
config = base_model.config
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for key, value in AUTO_MODEL_SEQUENCE_SUMMARY_DEFAULTS.items():
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if not hasattr(config, key):
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setattr(config, key, value)
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|
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# Update base model and derived model config
|
|
self.transformer.config = config
|
|
self.config = config
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|
|
|
self.num_labels = config.num_labels
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|
self.sequence_summary = SequenceSummary(config)
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|
|
|
self.apply(self.init_weights)
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|
|
|
def forward(self, input_ids, cls_index, **kwargs):
|
|
labels = kwargs.pop('labels', None) # Python 2 compatibility...
|
|
|
|
transformer_outputs = self.transformer(input_ids, **kwargs)
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|
|
|
output = transformer_outputs[0]
|
|
logits = self.sequence_summary(output, cls_index=cls_index)
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|
|
|
outputs = (logits,) + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
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|
|
|
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
|