BertGeneration (original) (raw)

Overview

The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks usingEncoderDecoderModel as proposed in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.

The abstract from the paper is the following:

Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.

This model was contributed by patrickvonplaten. The original code can be found here.

Usage examples and tips

The model can be used in combination with the EncoderDecoderModel to leverage two pretrained BERT checkpoints for subsequent fine-tuning:

encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-large-uncased", bos_token_id=101, eos_token_id=102)

decoder = BertGenerationDecoder.from_pretrained( ... "google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102 ... ) bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)

tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")

input_ids = tokenizer( ... "This is a long article to summarize", add_special_tokens=False, return_tensors="pt" ... ).input_ids labels = tokenizer("This is a short summary", return_tensors="pt").input_ids

loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss loss.backward()

Pretrained EncoderDecoderModel are also directly available in the model hub, e.g.:

sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse") tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")

input_ids = tokenizer( ... "This is the first sentence. This is the second sentence.", add_special_tokens=False, return_tensors="pt" ... ).input_ids

outputs = sentence_fuser.generate(input_ids)

print(tokenizer.decode(outputs[0]))

Tips:

• BertGenerationEncoder and BertGenerationDecoder should be used in combination with EncoderDecoder.
• For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input. Therefore, no EOS token should be added to the end of the input.

BertGenerationConfig

class transformers.BertGenerationConfig

< source >

( vocab_size = 50358 hidden_size = 1024 num_hidden_layers = 24 num_attention_heads = 16 intermediate_size = 4096 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 initializer_range = 0.02 layer_norm_eps = 1e-12 pad_token_id = 0 bos_token_id = 2 eos_token_id = 1 position_embedding_type = 'absolute' use_cache = True **kwargs )

Parameters

• vocab_size (int, optional, defaults to 50358) — Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by theinputs_ids passed when calling BertGeneration.
• hidden_size (int, optional, defaults to 1024) — Dimensionality of the encoder layers and the pooler layer.
• num_hidden_layers (int, optional, defaults to 24) — Number of hidden layers in the Transformer encoder.
• num_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder.
• intermediate_size (int, optional, defaults to 4096) — Dimensionality of the “intermediate” (often called feed-forward) layer in the Transformer encoder.
• hidden_act (str or function, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu","relu", "silu" and "gelu_new" are supported.
• hidden_dropout_prob (float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
• attention_probs_dropout_prob (float, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.
• max_position_embeddings (int, optional, defaults to 512) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
• initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
• layer_norm_eps (float, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.
• pad_token_id (int, optional, defaults to 0) — Padding token id.
• bos_token_id (int, optional, defaults to 2) — Beginning of stream token id.
• eos_token_id (int, optional, defaults to 1) — End of stream token id.
• position_embedding_type (str, optional, defaults to "absolute") — Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For positional embeddings use "absolute". For more information on "relative_key", please refer toSelf-Attention with Relative Position Representations (Shaw et al.). For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.).
• use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.

This is the configuration class to store the configuration of a BertGenerationPreTrainedModel. It is used to instantiate a BertGeneration model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the BertGenerationgoogle/bert_for_seq_generation_L-24_bbc_encoderarchitecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Examples:

from transformers import BertGenerationConfig, BertGenerationEncoder

configuration = BertGenerationConfig()

model = BertGenerationEncoder(configuration)

configuration = model.config

BertGenerationTokenizer

class transformers.BertGenerationTokenizer

< source >

( vocab_file bos_token = '' eos_token = '' unk_token = '' pad_token = '' sep_token = '<::::>' sp_model_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None **kwargs )

Parameters

• vocab_file (str) —SentencePiece file (generally has a .spm extension) that contains the vocabulary necessary to instantiate a tokenizer.
• bos_token (str, optional, defaults to "<s>") — The begin of sequence token.
• eos_token (str, optional, defaults to "</s>") — The end of sequence token.
• unk_token (str, optional, defaults to "<unk>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
• pad_token (str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths.
• sep_token (str, optional, defaults to "< --:::>"): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
• sp_model_kwargs (dict, optional) — Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for SentencePiece can be used, among other things, to set:
  • enable_sampling: Enable subword regularization.
  • nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.
    * nbest_size = {0,1}: No sampling is performed.
    * nbest_size > 1: samples from the nbest_size results.
    * nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.
  • alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.

Construct a BertGeneration tokenizer. Based on SentencePiece.

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

save_vocabulary

< source >

( save_directory: str filename_prefix: typing.Optional[str] = None )

BertGenerationEncoder

class transformers.BertGenerationEncoder

< source >

( config )

Parameters

• config (BertGenerationEncoder) — 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 thefrom_pretrained() method to load the model weights.

The bare BertGeneration model transformer outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs ) → transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• position_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• head_mask (torch.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• inputs_embeds (torch.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — 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.
• encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.
• encoder_attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
• past_key_values (Tuple[Tuple[torch.FloatTensor]], optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_valuesreturned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.
  Two formats are allowed:
  • a Cache instance, see our kv cache guide;
  • Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)). This is also known as the legacy cache format.
    The model will output the same cache format that is fed as input. If no past_key_values are passed, the legacy cache format will be returned.
    If past_key_values are used, the user can optionally input only the last input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all input_idsof shape (batch_size, sequence_length).
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
• output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
• return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

A transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BertGenerationConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
  If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
• past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.
  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally ifconfig.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_valuesinput) to speed up sequential decoding.
• 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, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• 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 after the attention softmax, used to compute the weighted average in the self-attention heads.
• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=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’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

The BertGenerationEncoder forward method, overrides the __call__ special method.

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

BertGenerationDecoder

class transformers.BertGenerationDecoder

< source >

( config )

Parameters

• config (BertGenerationDecoder) — 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 thefrom_pretrained() method to load the model weights.

BertGeneration Model with a language modeling head on top for CLM fine-tuning.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs ) → transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• position_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• head_mask (torch.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• inputs_embeds (torch.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — 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.
• encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.
• encoder_attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
• labels (torch.Tensor of shape (batch_size, sequence_length), optional) — Labels for computing the left-to-right language modeling loss (next word prediction). 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]
• past_key_values (Tuple[Tuple[torch.FloatTensor]], optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_valuesreturned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.
  Two formats are allowed:
  • a Cache instance, see our kv cache guide;
  • Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)). This is also known as the legacy cache format.
    The model will output the same cache format that is fed as input. If no past_key_values are passed, the legacy cache format will be returned.
    If past_key_values are used, the user can optionally input only the last input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all input_idsof shape (batch_size, sequence_length).
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
• output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
• return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BertGenerationConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
• 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).
• 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, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• 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 after the attention softmax, used to compute the weighted average in the self-attention heads.
• cross_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).
  Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.
• past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.
  Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (seepast_key_values input) to speed up sequential decoding.

The BertGenerationDecoder forward method, overrides the __call__ special method.

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

Example:

from transformers import AutoTokenizer, BertGenerationDecoder, BertGenerationConfig import torch

tokenizer = AutoTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder") config = BertGenerationConfig.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder") config.is_decoder = True model = BertGenerationDecoder.from_pretrained( ... "google/bert_for_seq_generation_L-24_bbc_encoder", config=config ... )

inputs = tokenizer("Hello, my dog is cute", return_token_type_ids=False, return_tensors="pt") outputs = model(**inputs)

prediction_logits = outputs.logits

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