#
Utilities for Generation

This page lists all the utility functions used by generate(), greedy_search(), contrastive_search(), sample(), beam_search(), beam_sample(), group_beam_search(), and constrained_beam_search().

Most of those are only useful if you are studying the code of the generate methods in the library.

##
Generate Outputs

The output of generate() is an instance of a subclass of ModelOutput. This output is a data structure containing all the information returned by generate(), but that can also be used as tuple or dictionary.

Here’s an example:

    from transformers import GPT2Tokenizer, GPT2LMHeadModel

    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
    model = GPT2LMHeadModel.from_pretrained("gpt2")

    inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
    generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)

The `generation_output` object is a GreedySearchDecoderOnlyOutput, as we can see in the documentation of that class below, it means it has the following attributes:

- `sequences`: the generated sequences of tokens
- `scores` (optional): the prediction scores of the language modelling head, for each generation step
- `hidden_states` (optional): the hidden states of the model, for each generation step
- `attentions` (optional): the attention weights of the model, for each generation step

Here we have the `scores` since we passed along `output_scores=True`, but we don’t have `hidden_states` and `attentions` because we didn’t pass `output_hidden_states=True` or `output_attentions=True`.

You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get `None`. Here for instance `generation_output.scores` are all the generated prediction scores of the language modeling head, and `generation_output.attentions` is `None`.

When using our `generation_output` object as a tuple, it only keeps the attributes that don’t have `None` values. Here, for instance, it has two elements, `loss` then `logits`, so

    generation_output[:2]

will return the tuple `(generation_output.sequences, generation_output.scores)` for instance.

When using our `generation_output` object as a dictionary, it only keeps the attributes that don’t have `None` values. Here, for instance, it has two keys that are `sequences` and `scores`.

We document here all output types.

###
GreedySearchOutput

###
class transformers.generation.GreedySearchDecoderOnlyOutput

< source >

( sequences: LongTensor = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
- attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
- hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.

Base class for outputs of decoder-only generation models using greedy search.

###
class transformers.generation.GreedySearchEncoderDecoderOutput

< source >

( sequences: LongTensor = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None cross_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None decoder_hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
- encoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.
- 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)`.
- decoder_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
- cross_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
- decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.

Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)

###
class transformers.generation.FlaxGreedySearchOutput

< source >

( sequences: ndarray = None )

Parameters

- sequences (`jnp.ndarray` of shape `(batch_size, max_length)`) — The generated sequences.

Flax Base class for outputs of decoder-only generation models using greedy search.

####
replace

< source >

( **updates )

“Returns a new object replacing the specified fields with new values.

###
SampleOutput

###
class transformers.generation.SampleDecoderOnlyOutput

< source >

( sequences: LongTensor = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
- attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length, sequence_length)`.
- hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(num_return_sequences*batch_size, generated_length, hidden_size)`.

Base class for outputs of decoder-only generation models using sampling.

###
class transformers.generation.SampleEncoderDecoderOutput

< source >

( sequences: LongTensor = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None cross_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None decoder_hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
- encoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size*num_return_sequences, num_heads, sequence_length, sequence_length)`.
- 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*num_return_sequences, sequence_length, hidden_size)`.
- decoder_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_return_sequences, num_heads, generated_length, sequence_length)`.
- cross_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
- decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_return_sequences, generated_length, hidden_size)`.

Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)

###
class transformers.generation.FlaxSampleOutput

< source >

( sequences: ndarray = None )

Parameters

- sequences (`jnp.ndarray` of shape `(batch_size, max_length)`) — The generated sequences.

Flax Base class for outputs of decoder-only generation models using sampling.

####
replace

< source >

( **updates )

“Returns a new object replacing the specified fields with new values.

###
BeamSearchOutput

###
class transformers.generation.BeamSearchDecoderOnlyOutput

< source >

( sequences: LongTensor = None sequences_scores: typing.Optional[torch.FloatTensor] = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None beam_indices: typing.Optional[torch.LongTensor] = None attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Final beam scores of the generated `sequences`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
- beam_indices (`torch.LongTensor`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`.
- attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
- hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.

Base class for outputs of decoder-only generation models using beam search.

###
class transformers.generation.BeamSearchEncoderDecoderOutput

< source >

( sequences: LongTensor = None sequences_scores: typing.Optional[torch.FloatTensor] = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None beam_indices: typing.Optional[torch.LongTensor] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None cross_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None decoder_hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Final beam scores of the generated `sequences`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
- beam_indices (`torch.LongTensor`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`.
- encoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.
- 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*num_beams*num_return_sequences, sequence_length, hidden_size)`.
- decoder_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length, sequence_length)`.
- cross_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
- decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.

Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)

###
BeamSampleOutput

###
class transformers.generation.BeamSampleDecoderOnlyOutput

< source >

( sequences: LongTensor = None sequences_scores: typing.Optional[torch.FloatTensor] = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None beam_indices: typing.Optional[torch.LongTensor] = None attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- sequences_scores (`torch.FloatTensor` of shape `(batch_size * num_return_sequence)`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Final beam scores of the generated `sequences`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
- beam_indices (`torch.LongTensor`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`.
- attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
- hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.

Base class for outputs of decoder-only generation models using beam sample.

###
class transformers.generation.BeamSampleEncoderDecoderOutput

< source >

( sequences: LongTensor = None sequences_scores: typing.Optional[torch.FloatTensor] = None scores: typing.Optional[typing.Tuple[torch.FloatTensor]] = None beam_indices: typing.Optional[torch.LongTensor] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None cross_attentions: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None decoder_hidden_states: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None )

Parameters

- sequences (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- sequences_scores (`torch.FloatTensor` of shape `(batch_size * num_return_sequence)`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Final beam scores of the generated `sequences`.
- scores (`tuple(torch.FloatTensor)` optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`).
- beam_indices (`torch.LongTensor`, optional, returned when `output_scores=True` is passed or when `config.output_scores=True`) — Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`.
- encoder_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.
- 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*num_beams, sequence_length, hidden_size)`.
- decoder_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
- cross_attentions (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_attentions=True` is passed or `config.output_attentions=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
- decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.

Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)

##
LogitsProcessor

A LogitsProcessor can be used to modify the prediction scores of a language model head for generation.

###
class transformers.LogitsProcessor

< source >

( )

Abstract base class for all logit processors that can be applied during generation.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor ) → `torch.FloatTensor` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search kwargs — Additional logits processor specific kwargs.

Returns

`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

Torch method for processing logits.

###
class transformers.LogitsProcessorList

< source >

( iterable = () )

This class can be used to create a list of LogitsProcessor or LogitsWarper to subsequently process a `scores` input tensor. This class inherits from list and adds a specific call method to apply each LogitsProcessor or LogitsWarper to the inputs.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor **kwargs ) → `torch.FloatTensor` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search kwargs — Additional logits processor specific kwargs.

Returns

`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

###
class transformers.LogitsWarper

< source >

( )

Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor ) → `torch.FloatTensor` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search kwargs — Additional logits processor specific kwargs.

Returns

`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

Torch method for warping logits.

###
class transformers.MinLengthLogitsProcessor

< source >

( min_length: int eos_token_id: typing.Union[int, typing.List[int]] )

Parameters

- min_length (`int`) — The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
- eos_token_id (`Union[int, List[int]]`) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.

LogitsProcessor enforcing a min-length by setting EOS probability to 0.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.MinNewTokensLengthLogitsProcessor

< source >

( prompt_length_to_skip: int min_new_tokens: int eos_token_id: typing.Union[int, typing.List[int]] )

Parameters

- prompt_length_to_skip (`int`) — The input tokens length.
- min_new_tokens (`int`) — The minimum new tokens length below which the score of `eos_token_id` is set to `-float("Inf")`.
- eos_token_id (`Union[int, List[int]]`) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.

LogitsProcessor enforcing a min-length of new tokens by setting EOS (End-Of-Sequence) token probability to 0.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.TemperatureLogitsWarper

< source >

( temperature: float )

Parameters

- temperature (`float`) — The value used to module the logits distribution.

LogitsWarper for temperature (exponential scaling output probability distribution).

####
__call__

< source >

( input_ids: Tensor scores: Tensor )

###
class transformers.RepetitionPenaltyLogitsProcessor

< source >

( penalty: float )

Parameters

- repetition_penalty (`float`) — The parameter for repetition penalty. 1.0 means no penalty. See this paper for more details.

LogitsProcessor enforcing an exponential penalty on repeated sequences.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.TopPLogitsWarper

< source >

( top_p: float filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- top_p (`float`) — If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.
- filter_value (`float`, optional, defaults to `-float("Inf")`) — All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimum number of tokens that cannot be filtered.

LogitsWarper that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.TopKLogitsWarper

< source >

( top_k: int filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- top_k (`int`) — The number of highest probability vocabulary tokens to keep for top-k-filtering.
- filter_value (`float`, optional, defaults to `-float("Inf")`) — All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimum number of tokens that cannot be filtered.

LogitsWarper that performs top-k, i.e. restricting to the k highest probability elements.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.TypicalLogitsWarper

< source >

( mass: float = 0.9 filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- mass (`float`) — Value of typical_p between 0 and 1 inclusive, defaults to 0.9.
- filter_value (`float`, optional, defaults to `-float("Inf")`) — All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimum number of tokens that cannot be filtered.

LogitsWarper that performs typical decoding. See Typical Decoding for Natural Language Generation for more information.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.NoRepeatNGramLogitsProcessor

< source >

( ngram_size: int )

Parameters

- ngram_size (`int`) — All ngrams of size `ngram_size` can only occur once.

LogitsProcessor that enforces no repetition of n-grams. See Fairseq.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.NoBadWordsLogitsProcessor

< source >

( bad_words_ids: typing.List[typing.List[int]] eos_token_id: typing.Union[int, typing.List[int]] )

Parameters

- bad_words_ids (`List[List[int]]`) — List of list of token ids that are not allowed to be generated. In order to get the token ids of the words that should not appear in the generated text, use `tokenizer(bad_words, add_prefix_space=True, add_special_tokens=False).input_ids`.
- eos_token_id (`Union[int, List[int]]`) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.

LogitsProcessor that enforces that specified sequences will never be sampled.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.PrefixConstrainedLogitsProcessor

< source >

( prefix_allowed_tokens_fn: typing.Callable[[int, torch.Tensor], typing.List[int]] num_beams: int )

Parameters

- prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`) — This function constraints the beam search to allowed tokens only at each step. This function takes 2 arguments `inputs_ids` and the batch ID `batch_id`. It has to return a list with the allowed tokens for the next generation step conditioned on the previously generated tokens `inputs_ids` and the batch ID `batch_id`.

LogitsProcessor that enforces constrained generation and is useful for prefix-conditioned constrained generation. See Autoregressive Entity Retrieval for more information.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.HammingDiversityLogitsProcessor

< source >

( diversity_penalty: float num_beams: int num_beam_groups: int )

Parameters

- diversity_penalty (`float`) — This value is subtracted from a beam’s score if it generates a token same as any beam from other group at a particular time. Note that `diversity_penalty` is only effective if `group beam search` is enabled.
- num_beams (`int`) — Number of beams used for group beam search. See this paper for more details.
- num_beam_groups (`int`) — Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams. See this paper for more details.

LogitsProcessor that enforces diverse beam search. Note that this logits processor is only effective for PreTrainedModel.group_beam_search(). See Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models for more details.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor current_tokens: LongTensor beam_group_idx: int )

###
class transformers.ForcedBOSTokenLogitsProcessor

< source >

( bos_token_id: int )

Parameters

- bos_token_id (`int`) — The id of the token to force as the first generated token.

LogitsProcessor that enforces the specified token as the first generated token.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.ForcedEOSTokenLogitsProcessor

< source >

( max_length: int eos_token_id: typing.Union[int, typing.List[int]] )

Parameters

- max_length (`int`) — The maximum length of the sequence to be generated.
- eos_token_id (`Union[int, List[int]]`) — The id of the token to force as the last generated token when `max_length` is reached. Optionally, use a list to set multiple end-of-sequence tokens.

LogitsProcessor that enforces the specified token as the last generated token when `max_length` is reached.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.InfNanRemoveLogitsProcessor

< source >

( )

LogitsProcessor that removes all `nan` and `inf` values to avoid the generation method to fail. Note that using the logits processor should only be used if necessary since it can slow down the generation method.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor )

###
class transformers.TFLogitsProcessor

< source >

( )

Abstract base class for all logit processors that can be applied during generation.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int ) → `tf.Tensor` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`tf.Tensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search.
- cur_len (`int`) — The current length of valid input sequence tokens. In the TF implementation, the input_ids’ sequence length is the maximum length generate can produce, and we need to know which of its tokens are valid. kwargs — Additional logits processor specific kwargs.

Returns

`tf.Tensor` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

TF method for processing logits.

###
class transformers.TFLogitsProcessorList

< source >

( iterable = () )

This class can be used to create a list of TFLogitsProcessor to subsequently process a `scores` input tensor. This class inherits from list and adds a specific call method to apply each TFLogitsProcessor to the inputs.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int **kwargs ) → `tf.Tensor` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`tf.Tensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search.
- cur_len (`int`) — The current length of valid input sequence tokens. In the TF implementation, the input_ids’ sequence length is the maximum length generate can produce, and we need to know which of its tokens are valid. kwargs — Additional logits processor specific kwargs.

Returns

`tf.Tensor` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

###
class transformers.TFLogitsWarper

< source >

( )

Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int ) → `tf.Tensor` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`tf.Tensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search.
- cur_len (`int`) — The current length of valid input sequence tokens. In the TF implementation, the input_ids’ sequence length is the maximum length generate can produce, and we need to know which of its tokens are valid. kwargs — Additional logits processor specific kwargs.

Returns

`tf.Tensor` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

TF method for warping logits.

###
class transformers.TFTemperatureLogitsWarper

< source >

( temperature: float )

Parameters

- temperature (`float`) — The value used to module the logits distribution.

TFLogitsWarper for temperature (exponential scaling output probability distribution).

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFTopPLogitsWarper

< source >

( top_p: float filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- top_p (`float`) — If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.
- filter_value (`float`, optional, defaults to `-float("Inf")`) — All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimum number of tokens that cannot be filtered.

TFLogitsWarper that performs top-p, i.e. restricting to top tokens summing to <= prob_cut_off.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFTopKLogitsWarper

< source >

( top_k: int filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- top_k (`int`) — The number of highest probability vocabulary tokens to keep for top-k-filtering.
- filter_value (`float`, optional, defaults to `-float("Inf")`) — All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimum number of tokens that cannot be filtered.

TFLogitsWarper that performs top-k, i.e. restricting to the k highest probability elements.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFMinLengthLogitsProcessor

< source >

( min_length: int eos_token_id: int )

Parameters

- min_length (`int`) — The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
- eos_token_id (`int`) — The id of the end-of-sequence token.

TFLogitsProcessor enforcing a min-length by setting EOS probability to 0.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFNoBadWordsLogitsProcessor

< source >

( bad_words_ids: typing.List[typing.List[int]] eos_token_id: int )

Parameters

- bad_words_ids (`List[List[int]]`) — List of list of token ids that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use `tokenizer(bad_word, add_prefix_space=True).input_ids`.
- eos_token_id (`int`) — The id of the end-of-sequence token.

TFLogitsProcessor that enforces that specified sequences will never be sampled.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFNoRepeatNGramLogitsProcessor

< source >

( ngram_size: int )

Parameters

- ngram_size (`int`) — All ngrams of size `ngram_size` can only occur once.

TFLogitsProcessor that enforces no repetition of n-grams. See Fairseq.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFRepetitionPenaltyLogitsProcessor

< source >

( penalty: float )

Parameters

- repetition_penalty (`float`) — The parameter for repetition penalty. 1.0 means no penalty. See this paper for more details.

TFLogitsProcessor enforcing an exponential penalty on repeated sequences.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFForcedBOSTokenLogitsProcessor

< source >

( bos_token_id: int )

Parameters

- bos_token_id (`int`) — The id of the token to force as the first generated token.

TFLogitsProcessor that enforces the specified token as the first generated token.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.TFForcedEOSTokenLogitsProcessor

< source >

( max_length: int eos_token_id: int )

Parameters

- max_length (`int`) — The maximum length of the sequence to be generated.
- eos_token_id (`int`) — The id of the token to force as the last generated token when `max_length` is reached.

TFLogitsProcessor that enforces the specified token as the last generated token when `max_length` is reached.

####
__call__

< source >

( input_ids: Tensor scores: Tensor cur_len: int )

###
class transformers.FlaxLogitsProcessor

< source >

( )

Abstract base class for all logit processors that can be applied during generation.

####
__call__

< source >

( input_ids: ndarray scores: ndarray ) → `jnp.ndarray` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search kwargs — Additional logits processor specific kwargs.

Returns

`jnp.ndarray` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

Flax method for processing logits.

###
class transformers.FlaxLogitsProcessorList

< source >

( iterable = () )

This class can be used to create a list of FlaxLogitsProcessor or FlaxLogitsWarper to subsequently process a `scores` input tensor. This class inherits from list and adds a specific call method to apply each FlaxLogitsProcessor or FlaxLogitsWarper to the inputs.

####
__call__

< source >

( input_ids: ndarray scores: ndarray cur_len: int **kwargs ) → `jnp.ndarray` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search kwargs — Additional logits processor specific kwargs.

Returns

`jnp.ndarray` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

###
class transformers.FlaxLogitsWarper

< source >

( )

Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.

####
__call__

< source >

( input_ids: ndarray scores: ndarray ) → `jnp.ndarray` of shape `(batch_size, config.vocab_size)`

Parameters

- input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam search or log softmax for each vocabulary token when using beam search kwargs — Additional logits processor specific kwargs.

Returns

`jnp.ndarray` of shape `(batch_size, config.vocab_size)`

The processed prediction scores.

Flax method for warping logits.

###
class transformers.FlaxTemperatureLogitsWarper

< source >

( temperature: float )

Parameters

- temperature (`float`) — The value used to module the logits distribution.

FlaxLogitsWarper for temperature (exponential scaling output probability distribution).

####
__call__

< source >

( input_ids: ndarray scores: ndarray cur_len: int )

###
class transformers.FlaxTopPLogitsWarper

< source >

( top_p: float filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- top_p (`float`) — If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.
- filter_value (`float`, optional, defaults to `-float("Inf")`) — All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimum number of tokens that cannot be filtered.

FlaxLogitsWarper that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.

####
__call__

< source >

( input_ids: ndarray scores: ndarray cur_len: int )

###
class transformers.FlaxTopKLogitsWarper

< source >

( top_k: int filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- top_k (`int`) — The number of highest probability vocabulary tokens to keep for top-k-filtering.
- filter_value (`float`, optional, defaults to `-float("Inf")`) — All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimum number of tokens that cannot be filtered.

FlaxLogitsWarper that performs top-k, i.e. restricting to the k highest probability elements.

####
__call__

< source >

( input_ids: ndarray scores: ndarray cur_len: int )

###
class transformers.FlaxForcedBOSTokenLogitsProcessor

< source >

( bos_token_id: int )

Parameters

- bos_token_id (`int`) — The id of the token to force as the first generated token.

FlaxLogitsProcessor that enforces the specified token as the first generated token.

####
__call__

< source >

( input_ids: ndarray scores: ndarray cur_len: int )

###
class transformers.FlaxForcedEOSTokenLogitsProcessor

< source >

( max_length: int eos_token_id: int )

Parameters

- max_length (`int`) — The maximum length of the sequence to be generated.
- eos_token_id (`int`) — The id of the token to force as the last generated token when `max_length` is reached.

FlaxLogitsProcessor that enforces the specified token as the last generated token when `max_length` is reached.

####
__call__

< source >

( input_ids: ndarray scores: ndarray cur_len: int )

###
class transformers.FlaxMinLengthLogitsProcessor

< source >

( min_length: int eos_token_id: int )

Parameters

- min_length (`int`) — The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
- eos_token_id (`int`) — The id of the end-of-sequence token.

FlaxLogitsProcessor enforcing a min-length by setting EOS probability to 0.

####
__call__

< source >

( input_ids: ndarray scores: ndarray cur_len: int )

##
StoppingCriteria

A StoppingCriteria can be used to change when to stop generation (other than EOS token).

###
class transformers.StoppingCriteria

< source >

( )

Abstract base class for all stopping criteria that can be applied during generation.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor **kwargs )

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax or scores for each vocabulary token after SoftMax. kwargs — Additional stopping criteria specific kwargs.

###
class transformers.StoppingCriteriaList

< source >

( iterable = () )

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor **kwargs )

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax or scores for each vocabulary token after SoftMax. kwargs — Additional stopping criteria specific kwargs.

###
class transformers.MaxLengthCriteria

< source >

( max_length: int )

Parameters

- max_length (`int`) — The maximum length that the output sequence can have in number of tokens.

This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. Keep in mind for decoder-only type of transformers, this will include the initial prompted tokens.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor **kwargs )

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax or scores for each vocabulary token after SoftMax. kwargs — Additional stopping criteria specific kwargs.

###
class transformers.MaxTimeCriteria

< source >

( max_time: float initial_timestamp: typing.Optional[float] = None )

Parameters

- max_time (`float`) — The maximum allowed time in seconds for the generation.
- initial_time (`float`, optional, defaults to `time.time()`) — The start of the generation allowed time.

This class can be used to stop generation whenever the full generation exceeds some amount of time. By default, the time will start being counted when you initialize this function. You can override this by passing an `initial_time`.

####
__call__

< source >

( input_ids: LongTensor scores: FloatTensor **kwargs )

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`) — Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax or scores for each vocabulary token after SoftMax. kwargs — Additional stopping criteria specific kwargs.

##
Constraints

A Constraint can be used to force the generation to include specific tokens or sequences in the output.

###
class transformers.Constraint

< source >

( )

Abstract base class for all constraints that can be applied during generation. It must define how the constraint can be satisfied.

All classes that inherit Constraint must follow the requirement that

    completed = False
    while not completed:
        _, completed = constraint.update(constraint.advance())

will always terminate (halt).

####
advance

< source >

( ) → token_ids(`torch.tensor`)

Returns

token_ids(`torch.tensor`)

Must be a tensor of a list of indexable tokens, not some integer.

When called, returns the token that would take this constraint one step closer to being fulfilled.

####
copy

< source >

( stateful = False ) → constraint(`Constraint`)

Returns

constraint(`Constraint`)

The same constraint as the one being called from.

Creates a new instance of this constraint.

####
does_advance

< source >

( token_id: int )

Reads in a token and returns whether it creates progress.

####
remaining

< source >

( )

Returns the number of remaining steps of `advance()` in order to complete this constraint.

####
reset

< source >

( )

Resets the state of this constraint to its initialization. We would call this in cases where the fulfillment of a constraint is abrupted by an unwanted token.

####
test

< source >

( )

Tests whether this constraint has been properly defined.

####
update

< source >

( token_id: int ) → stepped(`bool`)

Returns

stepped(`bool`)

Whether this constraint has become one step closer to being fulfuilled. completed(`bool`): Whether this constraint has been completely fulfilled by this token being generated. reset (`bool`): Whether this constraint has reset its progress by this token being generated.

Reads in a token and returns booleans that indicate the progress made by it. This function will update the state of this object unlikes `does_advance(self, token_id: int)`.

This isn’t to test whether a certain token will advance the progress; it’s to update its state as if it has been generated. This becomes important if token_id != desired token (refer to else statement in PhrasalConstraint)

###
class transformers.PhrasalConstraint

< source >

( token_ids: typing.List[int] )

Parameters

- token_ids (`List[int]`) — The id of the token that must be generated by the output.

Constraint enforcing that an ordered sequence of tokens is included in the output.

###
class transformers.DisjunctiveConstraint

< source >

( nested_token_ids: typing.List[typing.List[int]] )

Parameters

- nested_token_ids (`List[List[int]]`) — a list of words, where each word is a list of ids. This constraint
- is fulfilled by generating just one from the list of words. —

A special Constraint that is fulfilled by fulfilling just one of several constraints.

###
class transformers.ConstraintListState

< source >

( constraints: typing.List[transformers.generation.beam_constraints.Constraint] )

Parameters

- constraints (`List[Constraint]`) — A list of Constraint objects that must be fulfilled by the beam scorer.

A class for beam scorers to track its progress through a list of constraints.

####
advance

< source >

( )

The list of tokens to generate such that we can make progress. By “list” we don’t mean the list of token that will fully fulfill a constraint.

Given constraints `c_i = {t_ij | j == # of tokens}`, If we’re not in the middle of progressing through a specific constraint `c_i`, we return:

`[t_k1 for k in indices of unfulfilled constraints]`

If we are in the middle of a constraint, then we return: `[t_ij]`, where `i` is the index of the inprogress constraint, `j` is the next step for the constraint.

Though we don’t care which constraint is fulfilled first, if we are in the progress of fulfilling a constraint, that’s the only one we’ll return.

####
reset

< source >

( token_ids: typing.Optional[typing.List[int]] )

token_ids: the tokens generated thus far to reset the state of the progress through constraints.

##
BeamSearch

###
class transformers.BeamScorer

< source >

( )

Abstract base class for all beam scorers that are used for beam_search() and beam_sample().

####
process

< source >

( input_ids: LongTensor next_scores: FloatTensor next_tokens: LongTensor next_indices: LongTensor **kwargs ) → `UserDict`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size * num_beams, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using any class inheriting from PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- next_scores (`torch.FloatTensor` of shape `(batch_size, 2 * num_beams)`) — Current scores of the top `2 * num_beams` non-finished beam hypotheses.
- next_tokens (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`) — `input_ids` of the tokens corresponding to the top `2 * num_beams` non-finished beam hypotheses.
- next_indices (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`) — Beam indices indicating to which beam hypothesis the `next_tokens` correspond.
- pad_token_id (`int`, optional) — The id of the padding token.
- eos_token_id (`Union[int, List[int]]`, optional) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.

Returns

`UserDict`

A dictionary composed of the fields as defined above:

- next_beam_scores (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — Updated scores of all non-finished beams.
- next_beam_tokens (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — Next tokens to be added to the non-finished beam_hypotheses.
- next_beam_indices (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — Beam indices indicating to which beam the next tokens shall be added.

####
finalize

< source >

( input_ids: LongTensor next_scores: FloatTensor next_tokens: LongTensor next_indices: LongTensor max_length: int **kwargs ) → `torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size * num_beams, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using any class inheriting from PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- final_beam_scores (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — The final scores of all non-finished beams.
- final_beam_tokens (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — The last tokens to be added to the non-finished beam_hypotheses.
- final_beam_indices (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — The beam indices indicating to which beam the `final_beam_tokens` shall be added.
- pad_token_id (`int`, optional) — The id of the padding token.
- eos_token_id (`Union[int, List[int]]`, optional) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.

Returns

`torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`

The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.

###
class transformers.BeamSearchScorer

< source >

( batch_size: int num_beams: int device: device length_penalty: typing.Optional[float] = 1.0 do_early_stopping: typing.Union[bool, str, NoneType] = False num_beam_hyps_to_keep: typing.Optional[int] = 1 num_beam_groups: typing.Optional[int] = 1 max_length: typing.Optional[int] = None )

Parameters

- batch_size (`int`) — Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
- num_beams (`int`) — Number of beams for beam search.
- device (`torch.device`) — Defines the device type (e.g., `"cpu"` or `"cuda"`) on which this instance of `BeamSearchScorer` will be allocated.
- length_penalty (`float`, optional, defaults to 1.0) — Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while `length_penalty` < 0.0 encourages shorter sequences.
- do_early_stopping (`bool` or `str`, optional, defaults to `False`) — Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values: `True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an heuristic is applied and the generation stops when is it very unlikely to find better candidates; `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical beam search algorithm).
- num_beam_hyps_to_keep (`int`, optional, defaults to 1) — The number of beam hypotheses that shall be returned upon calling `~transformer.BeamSearchScorer.finalize`.
- num_beam_groups (`int`) — Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams. See this paper for more details.
- max_length (`int`, optional) — The maximum length of the sequence to be generated.

BeamScorer implementing standard beam search decoding.

Adapted in part from Facebook’s XLM beam search code.

Reference for the diverse beam search algorithm and implementation Ashwin Kalyan’s DBS implementation

####
process

< source >

( input_ids: LongTensor next_scores: FloatTensor next_tokens: LongTensor next_indices: LongTensor pad_token_id: typing.Optional[int] = None eos_token_id: typing.Union[int, typing.List[int], NoneType] = None beam_indices: typing.Optional[torch.LongTensor] = None )

####
finalize

< source >

( input_ids: LongTensor final_beam_scores: FloatTensor final_beam_tokens: LongTensor final_beam_indices: LongTensor max_length: int pad_token_id: typing.Optional[int] = None eos_token_id: typing.Union[int, typing.List[int], NoneType] = None beam_indices: typing.Optional[torch.LongTensor] = None )

###
class transformers.ConstrainedBeamSearchScorer

< source >

( batch_size: int num_beams: int constraints: typing.List[transformers.generation.beam_constraints.Constraint] device: device length_penalty: typing.Optional[float] = 1.0 do_early_stopping: typing.Union[bool, str, NoneType] = False num_beam_hyps_to_keep: typing.Optional[int] = 1 num_beam_groups: typing.Optional[int] = 1 max_length: typing.Optional[int] = None )

Parameters

- batch_size (`int`) — Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
- num_beams (`int`) — Number of beams for beam search.
- constraints (`List[Constraint]`) — A list of positive constraints represented as `Constraint` objects that must be fulfilled in the generation output. For more information, the documentation of Constraint should be read.
- device (`torch.device`) — Defines the device type (e.g., `"cpu"` or `"cuda"`) on which this instance of `BeamSearchScorer` will be allocated.
- length_penalty (`float`, optional, defaults to 1.0) — Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while `length_penalty` < 0.0 encourages shorter sequences.
- do_early_stopping (`bool` or `str`, optional, defaults to `False`) — Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values: `True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an heuristic is applied and the generation stops when is it very unlikely to find better candidates; `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical beam search algorithm).
- num_beam_hyps_to_keep (`int`, optional, defaults to 1) — The number of beam hypotheses that shall be returned upon calling `~transformer.BeamSearchScorer.finalize`.
- num_beam_groups (`int`) — Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams. See this paper for more details.
- max_length (`int`, optional) — The maximum length of the sequence to be generated.

BeamScorer implementing constrained beam search decoding.

####
process

< source >

( input_ids: LongTensor next_scores: FloatTensor next_tokens: LongTensor next_indices: LongTensor scores_for_all_vocab: FloatTensor pad_token_id: typing.Optional[int] = None eos_token_id: typing.Union[int, typing.List[int], NoneType] = None ) → `UserDict`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size * num_beams, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using any class inheriting from PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- next_scores (`torch.FloatTensor` of shape `(batch_size, 2 * num_beams)`) — Current scores of the top `2 * num_beams` non-finished beam hypotheses.
- next_tokens (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`) — `input_ids` of the tokens corresponding to the top `2 * num_beams` non-finished beam hypotheses.
- next_indices (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`) — Beam indices indicating to which beam hypothesis the `next_tokens` correspond.
- scores_for_all_vocab (`torch.FloatTensor` of shape `(batch_size * num_beams, sequence_length)`) — The scores of all tokens in the vocabulary for each of the beam hypotheses.
- pad_token_id (`int`, optional) — The id of the padding token.
- eos_token_id (`Union[int, List[int]]`, optional) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.

Returns

`UserDict`

A dictionary composed of the fields as defined above:

- next_beam_scores (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — Updated scores of all non-finished beams.

- next_beam_tokens (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — Next tokens to be added to the non-finished beam_hypotheses.

- next_beam_indices (`torch.FloatTensor` of shape `(batch_size * num_beams)`) — Beam indices indicating to which beam the next tokens shall be added.

####
finalize

< source >

( input_ids: LongTensor final_beam_scores: FloatTensor final_beam_tokens: LongTensor final_beam_indices: LongTensor max_length: int pad_token_id: typing.Optional[int] = None eos_token_id: typing.Union[int, typing.List[int], NoneType] = None )

##
Utilities

####
transformers.top_k_top_p_filtering

< source >

( logits: FloatTensor top_k: int = 0 top_p: float = 1.0 filter_value: float = -inf min_tokens_to_keep: int = 1 )

Parameters

- top_k (`int`, optional, defaults to 0) — If > 0, only keep the top k tokens with highest probability (top-k filtering)
- top_p (`float`, optional, defaults to 1.0) — If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimumber of tokens we keep per batch example in the output.

Filter a distribution of logits using top-k and/or nucleus (top-p) filtering

From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317

####
transformers.tf_top_k_top_p_filtering

< source >

( logits top_k = 0 top_p = 1.0 filter_value = -inf min_tokens_to_keep = 1 )

Parameters

- top_k (`int`, optional, defaults to 0) — If > 0, only keep the top k tokens with highest probability (top-k filtering)
- top_p (`float`, optional, defaults to 1.0) — If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
- min_tokens_to_keep (`int`, optional, defaults to 1) — Minimumber of tokens we keep per batch example in the output.

Filter a distribution of logits using top-k and/or nucleus (top-p) filtering

From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317

##
Streamers

###
class transformers.TextStreamer

< source >

( tokenizer: AutoTokenizer skip_prompt: bool = False **decode_kwargs )

Parameters

- tokenizer (`AutoTokenizer`) — The tokenized used to decode the tokens.
- skip_prompt (`bool`, optional, defaults to `False`) — Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
- decode_kwargs (`dict`, optional) — Additional keyword arguments to pass to the tokenizer’s `decode` method.

Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.

The API for the streamer classes is still under development and may change in the future.

Examples:

    >>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

    >>> tok = AutoTokenizer.from_pretrained("gpt2")
    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
    >>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
    >>> streamer = TextStreamer(tok)

    >>> # Despite returning the usual output, the streamer will also print the generated text to stdout.
    >>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20)
    An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,

####
end

< source >

( )

Flushes any remaining cache and prints a newline to stdout.

####
on_finalized_text

< source >

( text: str stream_end: bool = False )

Prints the new text to stdout. If the stream is ending, also prints a newline.

####
put

< source >

( value )

Recives tokens, decodes them, and prints them to stdout as soon as they form entire words.

###
class transformers.TextIteratorStreamer

< source >

( tokenizer: AutoTokenizer skip_prompt: bool = False timeout: typing.Optional[float] = None **decode_kwargs )

Parameters

- tokenizer (`AutoTokenizer`) — The tokenized used to decode the tokens.
- skip_prompt (`bool`, optional, defaults to `False`) — Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
- timeout (`float`, optional) — The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions in `.generate()`, when it is called in a separate thread.
- decode_kwargs (`dict`, optional) — Additional keyword arguments to pass to the tokenizer’s `decode` method.

Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive Gradio demo).

The API for the streamer classes is still under development and may change in the future.

Examples:

    >>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
    >>> from threading import Thread

    >>> tok = AutoTokenizer.from_pretrained("gpt2")
    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
    >>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
    >>> streamer = TextIteratorStreamer(tok)

    >>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
    >>> generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
    >>> thread = Thread(target=model.generate, kwargs=generation_kwargs)
    >>> thread.start()
    >>> generated_text = ""
    >>> for new_text in streamer:
    ...     generated_text += new_text
    >>> generated_text
    'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,'

####
on_finalized_text

< source >

( text: str stream_end: bool = False )

Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.