Encoder Decoder Models¶

The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any pre-trained autoencoding model as the encoder and any pre-trained autoregressive model as the decoder.

The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.

After such an EncoderDecoderModel has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).

An application of this architecture could be to leverage two pre-trained transformers.BertModel models as the encoder and decoder for a summarization model as was shown in: Text Summarization with Pretrained Encoders by Yang Liu and Mirella Lapata.

EncoderDecoderConfig¶

class transformers.EncoderDecoderConfig(**kwargs)[source]¶

EncoderDecoderConfig is the configuration class to store the configuration of a EncoderDecoderModel.

It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. See the documentation for PretrainedConfig for more information.

Parameters

kwargs (optional) –

Remaining dictionary of keyword arguments. Notably:
encoder (PretrainedConfig, optional, defaults to None):

An instance of a configuration object that defines the encoder config.

decoder (PretrainedConfig, optional, defaults to None):

An instance of a configuration object that defines the decoder config.

Example:

>>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel

>>> # Initializing a BERT bert-base-uncased style configuration
>>> config_encoder = BertConfig()
>>> config_decoder = BertConfig()

>>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

>>> # Initializing a Bert2Bert model from the bert-base-uncased style configurations
>>> model = EncoderDecoderModel(config=config)

>>> # Accessing the model configuration
>>> config_encoder = model.config.encoder
>>> config_decoder  = model.config.decoder
>>> # set decoder config to causal lm
>>> config_decoder.is_decoder = True
>>> config_decoder.add_cross_attention = True

>>> # Saving the model, including its configuration
>>> model.save_pretrained('my-model')

>>> # loading model and config from pretrained folder
>>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained('my-model')
>>> model = EncoderDecoderModel.from_pretrained('my-model', config=encoder_decoder_config)
classmethod from_encoder_decoder_configs(encoder_config: transformers.configuration_utils.PretrainedConfig, decoder_config: transformers.configuration_utils.PretrainedConfig, **kwargs) → transformers.configuration_utils.PretrainedConfig[source]¶

Instantiate a EncoderDecoderConfig (or a derived class) from a pre-trained encoder model configuration and decoder model configuration.

Returns

An instance of a configuration object

Return type

EncoderDecoderConfig

to_dict()[source]¶

Serializes this instance to a Python dictionary. Override the default to_dict() from PretrainedConfig.

Returns

Dictionary of all the attributes that make up this configuration instance,

Return type

Dict[str, any]

EncoderDecoderModel¶

class transformers.EncoderDecoderModel(config: Optional[transformers.configuration_utils.PretrainedConfig] = None, encoder: Optional[transformers.modeling_utils.PreTrainedModel] = None, decoder: Optional[transformers.modeling_utils.PreTrainedModel] = None)[source]¶

This class can be used to inialize a sequence-to-sequnece model with any pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via from_pretrained() function and the decoder is loaded via from_pretrained() function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, i.e. summarization.

The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.

After such an Encoder Decoder model has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (T5Config) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

EncoderDecoder is a generic model class that will be instantiated as a transformer architecture with one of the base model classes of the library as encoder and another one as decoder when created with the AutoModel.from_pretrained(pretrained_model_name_or_path) class method for the encoder and AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path) class method for the decoder.

config_class¶

alias of transformers.configuration_encoder_decoder.EncoderDecoderConfig

forward(input_ids=None, inputs_embeds=None, attention_mask=None, encoder_outputs=None, decoder_input_ids=None, decoder_attention_mask=None, decoder_inputs_embeds=None, labels=None, return_dict=None, **kwargs)[source]¶

The EncoderDecoderModel forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) – Indices of input sequence tokens in the vocabulary for the encoder. Indices can be obtained using PretrainedTokenizer. See encode() and convert_tokens_to_ids() for details.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional, defaults to None) – Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional, defaults to None) – Mask to avoid performing attention on padding token indices for the encoder. Mask values selected in [0, 1]: 1 for tokens that are NOT MASKED, 0 for MASKED tokens.

  • encoder_outputs (tuple(torch.FloatTensor), optional, defaults to None) – This tuple must consist of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) is a tensor of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

  • decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional, defaults to None) – Provide for sequence to sequence training to the decoder. Indices can be obtained using transformers.PretrainedTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.convert_tokens_to_ids() for details.

  • decoder_attention_mask (torch.BoolTensor of shape (batch_size, tgt_seq_len), optional, defaults to None) – Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.

  • decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional, defaults to None) – Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional, defaults to None) – Labels for computing the masked language modeling loss for the decoder. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]

  • return_dict (bool, optional, defaults to None) – If set to True, the model will return a Seq2SeqLMOutput instead of a plain tuple.

  • kwargs – (optional) Remaining dictionary of keyword arguments. Keyword arguments come in two flavors: - Without a prefix which will be input as **encoder_kwargs for the encoder forward function. - With a decoder_ prefix which will be input as **decoder_kwargs for the decoder forward function.

Returns

A Seq2SeqLMOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (EncoderDecoderConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Languaged modeling loss.

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) – Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).

    Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

  • decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.

  • decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

  • encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

  • encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

  • encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Examples:

>>> from transformers import EncoderDecoderModel, BertTokenizer
>>> import torch

>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert from pre-trained checkpoints

>>> # forward
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
>>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)

>>> # training
>>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=input_ids, return_dict=True)
>>> loss, logits = outputs.loss, outputs.logits

>>> # save and load from pretrained
>>> model.save_pretrained("bert2bert")
>>> model = EncoderDecoderModel.from_pretrained("bert2bert")

>>> # generation
>>> generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)

Return type

Seq2SeqLMOutput or tuple(torch.FloatTensor)

classmethod from_encoder_decoder_pretrained(encoder_pretrained_model_name_or_path: str = None, decoder_pretrained_model_name_or_path: str = None, *model_args, **kwargs) → transformers.modeling_utils.PreTrainedModel[source]¶

Instantiates an encoder and a decoder from one or two base classes of the library from pre-trained model checkpoints.

The model is set in evaluation mode by default using model.eval() (Dropout modules are deactivated). To train the model, you need to first set it back in training mode with model.train().

Params:
encoder_pretrained_model_name_or_path (:obj: str, optional, defaults to None):

information necessary to initiate the encoder. Either:

  • a string with the shortcut name of a pre-trained model to load from cache or download, e.g.: bert-base-uncased.

  • a string with the identifier name of a pre-trained model that was user-uploaded to our S3, e.g.: dbmdz/bert-base-german-cased.

  • a path to a directory containing model weights saved using save_pretrained(), e.g.: ./my_model_directory/encoder.

  • 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 path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

decoder_pretrained_model_name_or_path (:obj: str, optional, defaults to None):

information necessary to initiate the decoder. Either:

  • a string with the shortcut name of a pre-trained model to load from cache or download, e.g.: bert-base-uncased.

  • a string with the identifier name of a pre-trained model that was user-uploaded to our S3, e.g.: dbmdz/bert-base-german-cased.

  • a path to a directory containing model weights saved using save_pretrained(), e.g.: ./my_model_directory/decoder.

  • 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 path 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 of positional arguments:

All remaning positional arguments will be passed to the underlying model’s __init__ method

kwargs: (optional) Remaining dictionary of keyword arguments.

Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. output_attention=True). - To update the encoder configuration, use the prefix encoder_ for each configuration parameter - To update the decoder configuration, use the prefix decoder_ for each configuration parameter - To update the parent model configuration, do not use a prefix for each configuration parameter Behave differently depending on whether a config is provided or automatically loaded.

Examples:

>>> from transformers import EncoderDecoderModel
>>> # initialize a bert2bert from two pretrained BERT models. Note that the cross-attention layers will be randomly initialized
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased')
>>> # saving model after fine-tuning
>>> model.save_pretrained("./bert2bert")
>>> # load fine-tuned model
>>> model = EncoderDecoderModel.from_pretrained("./bert2bert")
get_input_embeddings()[source]¶

Returns the model’s input embeddings.

Returns

A torch module mapping vocabulary to hidden states.

Return type

nn.Module

get_output_embeddings()[source]¶

Returns the model’s output embeddings.

Returns

A torch module mapping hidden states to vocabulary.

Return type

nn.Module

prepare_inputs_for_generation(input_ids, past, attention_mask, encoder_outputs, **kwargs)[source]¶

Implement in subclasses of PreTrainedModel for custom behavior to prepare inputs in the generate method.

tie_weights()[source]¶

Tie the weights between the input embeddings and the output embeddings.

If the torchscript flag is set in the configuration, can’t handle parameter sharing so we are cloning the weights instead.