transformers/docs/source/zh/internal/generation_utils.md

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# 用于生成的工具

此页面列出了所有由 [`~generation.GenerationMixin.generate`]。

## 生成输出

[`~generation.GenerationMixin.generate`] 的输出是 [`~utils.ModelOutput`] 的一个子类的实例。这个输出是一种包含 [`~generation.GenerationMixin.generate`] 返回的所有信息数据结构，但也可以作为元组或字典使用。
这里是一个例子：


```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
model = GPT2LMHeadModel.from_pretrained("openai-community/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)
```

`generation_output` 的对象是 [`~generation.GenerateDecoderOnlyOutput`] 的一个实例，从该类的文档中我们可以看到，这意味着它具有以下属性：

- `sequences`: 生成的tokens序列
- `scores`（可选）: 每个生成步骤的语言建模头的预测分数
- `hidden_states`（可选）: 每个生成步骤模型的hidden states
- `attentions`（可选）: 每个生成步骤模型的注意力权重

在这里，由于我们传递了 `output_scores=True`，我们具有 `scores` 属性。但我们没有 `hidden_states` 和 `attentions`，因为没有传递 `output_hidden_states=True` 或 `output_attentions=True`。

您可以像通常一样访问每个属性，如果该属性未被模型返回，则将获得 `None`。例如，在这里 `generation_output.scores` 是语言建模头的所有生成预测分数，而 `generation_output.attentions` 为 `None`。

当我们将 `generation_output` 对象用作元组时，它只保留非 `None` 值的属性。例如，在这里它有两个元素，`loss` 然后是 `logits`，所以


```python
generation_output[:2]
```

将返回元组`(generation_output.sequences, generation_output.scores)`。

当我们将`generation_output`对象用作字典时，它只保留非`None`的属性。例如，它有两个键，分别是`sequences`和`scores`。

我们在此记录所有输出类型。


### PyTorch

[[autodoc]] generation.GenerateDecoderOnlyOutput

[[autodoc]] generation.GenerateEncoderDecoderOutput

[[autodoc]] generation.GenerateBeamDecoderOnlyOutput

[[autodoc]] generation.GenerateBeamEncoderDecoderOutput

### TensorFlow

[[autodoc]] generation.TFGreedySearchEncoderDecoderOutput

[[autodoc]] generation.TFGreedySearchDecoderOnlyOutput

[[autodoc]] generation.TFSampleEncoderDecoderOutput

[[autodoc]] generation.TFSampleDecoderOnlyOutput

[[autodoc]] generation.TFBeamSearchEncoderDecoderOutput

[[autodoc]] generation.TFBeamSearchDecoderOnlyOutput

[[autodoc]] generation.TFBeamSampleEncoderDecoderOutput

[[autodoc]] generation.TFBeamSampleDecoderOnlyOutput

[[autodoc]] generation.TFContrastiveSearchEncoderDecoderOutput

[[autodoc]] generation.TFContrastiveSearchDecoderOnlyOutput

### FLAX

[[autodoc]] generation.FlaxSampleOutput

[[autodoc]] generation.FlaxGreedySearchOutput

[[autodoc]] generation.FlaxBeamSearchOutput

## LogitsProcessor

[`LogitsProcessor`] 可以用于修改语言模型头的预测分数以进行生成


### PyTorch

[[autodoc]] AlternatingCodebooksLogitsProcessor
    - __call__

[[autodoc]] ClassifierFreeGuidanceLogitsProcessor
    - __call__

[[autodoc]] EncoderNoRepeatNGramLogitsProcessor
    - __call__

[[autodoc]] EncoderRepetitionPenaltyLogitsProcessor
    - __call__

[[autodoc]] EpsilonLogitsWarper
    - __call__

[[autodoc]] EtaLogitsWarper
    - __call__

[[autodoc]] ExponentialDecayLengthPenalty
    - __call__

[[autodoc]] ForcedBOSTokenLogitsProcessor
    - __call__

[[autodoc]] ForcedEOSTokenLogitsProcessor
    - __call__

[[autodoc]] ForceTokensLogitsProcessor
    - __call__

[[autodoc]] HammingDiversityLogitsProcessor
    - __call__

[[autodoc]] InfNanRemoveLogitsProcessor
    - __call__

[[autodoc]] LogitNormalization
    - __call__

[[autodoc]] LogitsProcessor
    - __call__

[[autodoc]] LogitsProcessorList
    - __call__

[[autodoc]] LogitsWarper
    - __call__

[[autodoc]] MinLengthLogitsProcessor
    - __call__

[[autodoc]] MinNewTokensLengthLogitsProcessor
    - __call__

[[autodoc]] NoBadWordsLogitsProcessor
    - __call__

[[autodoc]] NoRepeatNGramLogitsProcessor
    - __call__

[[autodoc]] PrefixConstrainedLogitsProcessor
    - __call__

[[autodoc]] RepetitionPenaltyLogitsProcessor
    - __call__

[[autodoc]] SequenceBiasLogitsProcessor
    - __call__

[[autodoc]] SuppressTokensAtBeginLogitsProcessor
    - __call__

[[autodoc]] SuppressTokensLogitsProcessor
    - __call__

[[autodoc]] TemperatureLogitsWarper
    - __call__

[[autodoc]] TopKLogitsWarper
    - __call__

[[autodoc]] TopPLogitsWarper
    - __call__

[[autodoc]] TypicalLogitsWarper
    - __call__

[[autodoc]] UnbatchedClassifierFreeGuidanceLogitsProcessor
    - __call__

[[autodoc]] WhisperTimeStampLogitsProcessor
    - __call__

### TensorFlow

[[autodoc]] TFForcedBOSTokenLogitsProcessor
    - __call__

[[autodoc]] TFForcedEOSTokenLogitsProcessor
    - __call__

[[autodoc]] TFForceTokensLogitsProcessor
    - __call__

[[autodoc]] TFLogitsProcessor
    - __call__

[[autodoc]] TFLogitsProcessorList
    - __call__

[[autodoc]] TFLogitsWarper
    - __call__

[[autodoc]] TFMinLengthLogitsProcessor
    - __call__

[[autodoc]] TFNoBadWordsLogitsProcessor
    - __call__

[[autodoc]] TFNoRepeatNGramLogitsProcessor
    - __call__

[[autodoc]] TFRepetitionPenaltyLogitsProcessor
    - __call__

[[autodoc]] TFSuppressTokensAtBeginLogitsProcessor
    - __call__

[[autodoc]] TFSuppressTokensLogitsProcessor
    - __call__

[[autodoc]] TFTemperatureLogitsWarper
    - __call__

[[autodoc]] TFTopKLogitsWarper
    - __call__

[[autodoc]] TFTopPLogitsWarper
    - __call__

### FLAX

[[autodoc]] FlaxForcedBOSTokenLogitsProcessor
    - __call__

[[autodoc]] FlaxForcedEOSTokenLogitsProcessor
    - __call__

[[autodoc]] FlaxForceTokensLogitsProcessor
    - __call__

[[autodoc]] FlaxLogitsProcessor
    - __call__

[[autodoc]] FlaxLogitsProcessorList
    - __call__

[[autodoc]] FlaxLogitsWarper
    - __call__

[[autodoc]] FlaxMinLengthLogitsProcessor
    - __call__

[[autodoc]] FlaxSuppressTokensAtBeginLogitsProcessor
    - __call__

[[autodoc]] FlaxSuppressTokensLogitsProcessor
    - __call__

[[autodoc]] FlaxTemperatureLogitsWarper
    - __call__

[[autodoc]] FlaxTopKLogitsWarper
    - __call__

[[autodoc]] FlaxTopPLogitsWarper
    - __call__

[[autodoc]] FlaxWhisperTimeStampLogitsProcessor
    - __call__

## StoppingCriteria

可以使用[`StoppingCriteria`]来更改停止生成的时间（除了EOS token以外的方法）。请注意，这仅适用于我们的PyTorch实现。


[[autodoc]] StoppingCriteria
    - __call__

[[autodoc]] StoppingCriteriaList
    - __call__

[[autodoc]] MaxLengthCriteria
    - __call__

[[autodoc]] MaxTimeCriteria
    - __call__

## Constraints

可以使用[`Constraint`]来强制生成结果包含输出中的特定tokens或序列。请注意，这仅适用于我们的PyTorch实现。

[[autodoc]] Constraint

[[autodoc]] PhrasalConstraint

[[autodoc]] DisjunctiveConstraint

[[autodoc]] ConstraintListState

## BeamSearch

[[autodoc]] BeamScorer
    - process
    - finalize

[[autodoc]] BeamSearchScorer
    - process
    - finalize

[[autodoc]] ConstrainedBeamSearchScorer
    - process
    - finalize

## Streamers

[[autodoc]] TextStreamer

[[autodoc]] TextIteratorStreamer