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# Utilities for Generation

This page lists all the utility functions used by [`~generation_utils.GenerationMixin.generate`],
[`~generation_utils.GenerationMixin.greedy_search`],
[`~generation_utils.GenerationMixin.sample`],
[`~generation_utils.GenerationMixin.beam_search`],
[`~generation_utils.GenerationMixin.beam_sample`],
[`~generation_utils.GenerationMixin.group_beam_search`], and
[`~generation_utils.GenerationMixin.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 [`~generation_utils.GenerationMixin.generate`] is an instance of a subclass of
[`~file_utils.ModelOutput`]. This output is a data structure containing all the information returned
by [`~generation_utils.GenerationMixin.generate`], but that can also be used as tuple or dictionary.

Here's an example:

```python
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 [`~generation_utils.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

```python
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

[[autodoc]] generation_utils.GreedySearchDecoderOnlyOutput

[[autodoc]] generation_utils.GreedySearchEncoderDecoderOutput

[[autodoc]] generation_flax_utils.FlaxGreedySearchOutput

### SampleOutput

[[autodoc]] generation_utils.SampleDecoderOnlyOutput

[[autodoc]] generation_utils.SampleEncoderDecoderOutput

[[autodoc]] generation_flax_utils.FlaxSampleOutput

### BeamSearchOutput

[[autodoc]] generation_utils.BeamSearchDecoderOnlyOutput

[[autodoc]] generation_utils.BeamSearchEncoderDecoderOutput

### BeamSampleOutput

[[autodoc]] generation_utils.BeamSampleDecoderOnlyOutput

[[autodoc]] generation_utils.BeamSampleEncoderDecoderOutput

## LogitsProcessor

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

[[autodoc]] LogitsProcessor
    - __call__

[[autodoc]] LogitsProcessorList
    - __call__

[[autodoc]] LogitsWarper
    - __call__

[[autodoc]] MinLengthLogitsProcessor
    - __call__

[[autodoc]] TemperatureLogitsWarper
    - __call__

[[autodoc]] RepetitionPenaltyLogitsProcessor
    - __call__

[[autodoc]] TopPLogitsWarper
    - __call__

[[autodoc]] TopKLogitsWarper
    - __call__

[[autodoc]] NoRepeatNGramLogitsProcessor
    - __call__

[[autodoc]] NoBadWordsLogitsProcessor
    - __call__

[[autodoc]] PrefixConstrainedLogitsProcessor
    - __call__

[[autodoc]] HammingDiversityLogitsProcessor
    - __call__

[[autodoc]] ForcedBOSTokenLogitsProcessor
    - __call__

[[autodoc]] ForcedEOSTokenLogitsProcessor
    - __call__

[[autodoc]] InfNanRemoveLogitsProcessor
    - __call__

[[autodoc]] TFLogitsProcessor
    - __call__

[[autodoc]] TFLogitsProcessorList
    - __call__

[[autodoc]] TFLogitsWarper
    - __call__

[[autodoc]] TFTemperatureLogitsWarper
    - __call__

[[autodoc]] TFTopPLogitsWarper
    - __call__

[[autodoc]] TFTopKLogitsWarper
    - __call__

[[autodoc]] TFMinLengthLogitsProcessor
    - __call__

[[autodoc]] TFNoBadWordsLogitsProcessor
    - __call__

[[autodoc]] TFNoRepeatNGramLogitsProcessor
    - __call__

[[autodoc]] TFRepetitionPenaltyLogitsProcessor
    - __call__

[[autodoc]] FlaxLogitsProcessor
    - __call__

[[autodoc]] FlaxLogitsProcessorList
    - __call__

[[autodoc]] FlaxLogitsWarper
    - __call__

[[autodoc]] FlaxTemperatureLogitsWarper
    - __call__

[[autodoc]] FlaxTopPLogitsWarper
    - __call__

[[autodoc]] FlaxTopKLogitsWarper
    - __call__

[[autodoc]] FlaxForcedBOSTokenLogitsProcessor
    - __call__

[[autodoc]] FlaxForcedEOSTokenLogitsProcessor
    - __call__

[[autodoc]] FlaxMinLengthLogitsProcessor
    - __call__

## StoppingCriteria

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

[[autodoc]] StoppingCriteria
    - __call__

[[autodoc]] StoppingCriteriaList
    - __call__

[[autodoc]] MaxLengthCriteria
    - __call__

[[autodoc]] MaxTimeCriteria
    - __call__

## Constraints

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

[[autodoc]] Constraint

[[autodoc]] PhrasalConstraint

[[autodoc]] DisjunctiveConstraint

[[autodoc]] ConstraintListState

## BeamSearch

[[autodoc]] BeamScorer
    - process
    - finalize

[[autodoc]] BeamSearchScorer
    - process
    - finalize

[[autodoc]] ConstrainedBeamSearchScorer
    - process
    - finalize

## Utilities

[[autodoc]] top_k_top_p_filtering

[[autodoc]] tf_top_k_top_p_filtering
