# Generation

每个框架都在它们各自的 `GenerationMixin` 类中实现了文本生成的 `generate` 方法：

- PyTorch [generate()](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationMixin.generate) 在 [GenerationMixin](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationMixin) 中实现。

无论您选择哪个框架，都可以使用 [GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig) 类实例对 generate 方法进行参数化。有关生成方法的控制参数的完整列表，请参阅此类。

要了解如何检查模型的生成配置、默认值是什么、如何临时更改参数以及如何创建和保存自定义生成配置，请参阅 [文本生成策略指南](../generation_strategies)。该指南还解释了如何使用相关功能，如token流。

## GenerationConfig[[transformers.GenerationConfig]]

#### transformers.GenerationConfig[[transformers.GenerationConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/generation/configuration_utils.py#L83)

Class that holds a configuration for a generation task. A `generate` call supports the following generation methods
for text-decoder, text-to-text, speech-to-text, and vision-to-text models:

- *greedy decoding* if `num_beams=1` and `do_sample=False`
- *multinomial sampling* if `num_beams=1` and `do_sample=True`
- *beam-search decoding* if `num_beams>1` and `do_sample=False`
- *beam-search multinomial sampling* if `num_beams>1` and `do_sample=True`
- *assisted decoding* if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`

To learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).

A large number of these flags control the logits or the stopping criteria of the generation. Make sure you check
the [generate-related classes](https://huggingface.co/docs/transformers/internal/generation_utils) for a full
description of the possible manipulations, as well as examples of their usage.

Note: the configuration fields that are still `None` will be overridden by `GenerationConfig._get_default_generation_params()`
during the generation loop. If you want to use different values for these fields, make sure to explicitly set them in the
generation config.

from_pretrainedtransformers.GenerationConfig.from_pretrainedhttps://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/generation/configuration_utils.py#L827[{"name": "pretrained_model_name", "val": ": str | os.PathLike"}, {"name": "config_file_name", "val": ": str | os.PathLike | None = None"}, {"name": "cache_dir", "val": ": str | os.PathLike | None = None"}, {"name": "force_download", "val": ": bool = False"}, {"name": "local_files_only", "val": ": bool = False"}, {"name": "token", "val": ": str | bool | None = None"}, {"name": "revision", "val": ": str = 'main'"}, {"name": "**kwargs", "val": ""}]- **pretrained_model_name** (`str` or `os.PathLike`) --
  This can be either:

  - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
    huggingface.co.
  - a path to a *directory* containing a configuration file saved using the
    [save_pretrained()](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig.save_pretrained) method, e.g., `./my_model_directory/`.
- **config_file_name** (`str` or `os.PathLike`, *optional*, defaults to `"generation_config.json"`) --
  Name of the generation configuration JSON file to be loaded from `pretrained_model_name`.
- **cache_dir** (`str` or `os.PathLike`, *optional*) --
  Path to a directory in which a downloaded pretrained model configuration should be cached if the
  standard cache should not be used.
- **force_download** (`bool`, *optional*, defaults to `False`) --
  Whether or not to force to (re-)download the configuration files and override the cached versions if
  they exist.
- **proxies** (`dict[str, str]`, *optional*) --
  A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
  'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- **token** (`str` or `bool`, *optional*) --
  The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
  the token generated when running `hf auth login` (stored in `~/.huggingface`).
- **revision** (`str`, *optional*, defaults to `"main"`) --
  The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
  git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
  identifier allowed by git.

  

  To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`.

  

- **return_unused_kwargs** (`bool`, *optional*, defaults to `False`) --
  If `False`, then this function returns just the final configuration object.

  If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
  dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
  part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- **subfolder** (`str`, *optional*, defaults to `""`) --
  In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
  specify the folder name here.
- **kwargs** (`dict[str, Any]`, *optional*) --
  The values in kwargs of any keys which are configuration attributes will be used to override the loaded
  values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
  by the `return_unused_kwargs` keyword parameter.0[GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig)The configuration object instantiated from this pretrained model.

Instantiate a [GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig) from a generation configuration file.

Examples:

```python
>>> from transformers import GenerationConfig

>>> # Download configuration from huggingface.co and cache.
>>> generation_config = GenerationConfig.from_pretrained("openai-community/gpt2")

>>> # E.g. config was saved using *save_pretrained('./test/saved_model/')*
>>> generation_config.save_pretrained("./test/saved_model/")
>>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/")

>>> # You can also specify configuration names to your generation configuration file
>>> generation_config.save_pretrained("./test/saved_model/", config_file_name="my_configuration.json")
>>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/", "my_configuration.json")

>>> # If you'd like to try a minor variation to an existing configuration, you can also pass generation
>>> # arguments to `.from_pretrained()`. Be mindful that typos and unused arguments will be ignored
>>> generation_config, unused_kwargs = GenerationConfig.from_pretrained(
...     "openai-community/gpt2", top_k=1, foo=False, do_sample=True, return_unused_kwargs=True
... )
>>> generation_config.top_k
1

>>> unused_kwargs
{'foo': False}
```

**Returns:**

`[GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig)`

The configuration object instantiated from this pretrained model.
#### from_model_config[[transformers.GenerationConfig.from_model_config]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/generation/configuration_utils.py#L1159)

Instantiates a [GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig) from a [PreTrainedConfig](/docs/transformers/v5.6.1/zh/main_classes/configuration#transformers.PreTrainedConfig). This function is useful to convert legacy
[PreTrainedConfig](/docs/transformers/v5.6.1/zh/main_classes/configuration#transformers.PreTrainedConfig) objects, which may contain generation parameters, into a stand-alone [GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig).

**Parameters:**

model_config (`PreTrainedConfig | dict`) : The model config that will be used to instantiate the generation config.

**Returns:**

`[GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig)`

The configuration object instantiated from those parameters.
#### save_pretrained[[transformers.GenerationConfig.save_pretrained]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/generation/configuration_utils.py#L768)

Save a generation configuration object to the directory `save_directory`, so that it can be re-loaded using the
[from_pretrained()](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig.from_pretrained) class method.

**Parameters:**

save_directory (`str` or `os.PathLike`) : Directory where the configuration JSON file will be saved (will be created if it does not exist).

config_file_name (`str` or `os.PathLike`, *optional*, defaults to `"generation_config.json"`) : Name of the generation configuration JSON file to be saved in `save_directory`.

push_to_hub (`bool`, *optional*, defaults to `False`) : Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).

kwargs (`dict[str, Any]`, *optional*) : Additional key word arguments passed along to the [push_to_hub()](/docs/transformers/v5.6.1/zh/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub) method.

## GenerationMixin[[transformers.GenerationMixin]]

#### transformers.GenerationMixin[[transformers.GenerationMixin]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/generation/utils.py#L338)

A class containing all functions for auto-regressive text generation, to be used as a mixin in model classes.
Inheriting from this class causes the model to have special generation-related behavior, such as loading a
`GenerationConfig` at initialization time or ensuring `generate`-related tests are run in `transformers` CI.

A model class should inherit from `GenerationMixin` to enable calling methods like `generate`, or when it
has defined a custom `generate` method that relies on `GenerationMixin`, directly or indirectly, which
approximately shares the same interface to public methods like `generate`. Three examples:
- `LlamaForCausalLM` should inherit from `GenerationMixin` to enable calling `generate` and other public
  methods in the mixin;
- `BlipForQuestionAnswering` has a custom `generate` method that approximately shares the same interface as
  `GenerationMixin.generate` (it has a few extra arguments, and the same output). That function also calls
  `GenerationMixin.generate` indirectly, through an inner model. As such, `BlipForQuestionAnswering` should
  inherit from `GenerationMixin` to benefit from all generation-related automation in our codebase;
- `BarkModel` has a custom `generate` method and one of its inner models calls `GenerationMixin.generate`.
  However, its `generate` does not share the same interface as `GenerationMixin.generate`. In this case,
  `BarkModel` should NOT inherit from `GenerationMixin`, as it breaks the `generate` interface.

The class exposes [generate()](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationMixin.generate), which can be used for:
- *greedy decoding* if `num_beams=1` and `do_sample=False`
- *multinomial sampling* if `num_beams=1` and `do_sample=True`
- *beam-search decoding* if `num_beams>1` and `do_sample=False`
- *beam-search multinomial sampling* if `num_beams>1` and `do_sample=True`
- *assisted decoding* if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`

To learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).

generatetransformers.GenerationMixin.generatehttps://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/generation/utils.py#L2130[{"name": "inputs", "val": ": torch.Tensor | None = None"}, {"name": "generation_config", "val": ": transformers.generation.configuration_utils.GenerationConfig | None = None"}, {"name": "logits_processor", "val": ": transformers.generation.logits_process.LogitsProcessorList | None = None"}, {"name": "stopping_criteria", "val": ": transformers.generation.stopping_criteria.StoppingCriteriaList | None = None"}, {"name": "prefix_allowed_tokens_fn", "val": ": collections.abc.Callable[[int, torch.Tensor], list[int]] | None = None"}, {"name": "synced_gpus", "val": ": bool | None = None"}, {"name": "assistant_model", "val": ": typing.Optional[ForwardRef('PreTrainedModel')] = None"}, {"name": "streamer", "val": ": typing.Optional[ForwardRef('BaseStreamer')] = None"}, {"name": "negative_prompt_ids", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "custom_generate", "val": ": str | collections.abc.Callable | None = None"}, {"name": "**kwargs", "val": ""}]- **inputs** (`torch.Tensor` of varying shape depending on the modality, *optional*) --
  The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
  method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
  should be in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
  `input_ids`, `input_values`, `input_features`, or `pixel_values`.
- **generation_config** ([GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig), *optional*) --
  The generation configuration to be used as base parametrization for the generation call. `**kwargs`
  passed to generate matching the attributes of `generation_config` will override them. If
  `generation_config` is not provided, the default will be used, which has the following loading
  priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
  configuration. Please note that unspecified parameters will inherit [GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig)'s
  default values, whose documentation should be checked to parameterize generation.
- **logits_processor** (`LogitsProcessorList`, *optional*) --
  Custom logits processors that complement the default logits processors built from arguments and
  generation config. If a logit processor is passed that is already created with the arguments or a
  generation config an error is thrown. This feature is intended for advanced users.
- **stopping_criteria** (`StoppingCriteriaList`, *optional*) --
  Custom stopping criteria that complements the default stopping criteria built from arguments and a
  generation config. If a stopping criteria is passed that is already created with the arguments or a
  generation config an error is thrown. If your stopping criteria depends on the `scores` input, make
  sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is
  intended for advanced users.
- **prefix_allowed_tokens_fn** (`Callable[[int, torch.Tensor], list[int]]`, *optional*) --
  If provided, this function constraints the beam search to allowed tokens only at each step. If not
  provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
  `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
  on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
  for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
  Retrieval](https://huggingface.co/papers/2010.00904).
- **synced_gpus** (`bool`, *optional*) --
  Whether to continue running the while loop until max_length. Unless overridden, this flag will be set
  to `True` if using `FullyShardedDataParallel` or DeepSpeed ZeRO Stage 3 with multiple GPUs to avoid
  deadlocking if one GPU finishes generating before other GPUs. Otherwise, defaults to `False`.
- **assistant_model** (`PreTrainedModel`, *optional*) --
  An assistant model that can be used to accelerate generation. The assistant model must have the exact
  same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistant model
  is much faster than running generation with the model you're calling generate from. As such, the
  assistant model should be much smaller.
- **streamer** (`BaseStreamer`, *optional*) --
  Streamer object that will be used to stream the generated sequences. Generated tokens are passed
  through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
- **negative_prompt_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  The negative prompt needed for some processors such as CFG. The batch size must match the input batch
  size. This is an experimental feature, subject to breaking API changes in future versions.
- **negative_prompt_attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Attention_mask for `negative_prompt_ids`.
- **custom_generate** (`str` or `Callable`, *optional*) --
  One of the following:
  - `str` (Hugging Face Hub repository name): runs the custom `generate` function defined at
    `custom_generate/generate.py` in that repository instead of the standard `generate` method. The
    repository fully replaces the generation logic, and the return type may differ.
  - `str` (local repository path): same as above but from a local path, `trust_remote_code` not required.
  - `Callable`: `generate` will perform the usual input preparation steps, then call the provided callable to
    run the decoding loop.
  For more information, see [the docs](../../generation_strategies#custom-generation-methods).
- **kwargs** (`dict[str, Any]`, *optional*) --
  Ad hoc parametrization of `generation_config` and/or additional model-specific kwargs that will be
  forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
  specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.0[ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) or `torch.LongTensor`A [ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) (if `return_dict_in_generate=True`
or when `config.return_dict_in_generate=True`) or a `torch.LongTensor`.

If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) types are:

- [GenerateDecoderOnlyOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateDecoderOnlyOutput),
- [GenerateBeamDecoderOnlyOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateBeamDecoderOnlyOutput)

If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) types are:

- [GenerateEncoderDecoderOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateEncoderDecoderOutput),
- [GenerateBeamEncoderDecoderOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateBeamEncoderDecoderOutput)

Generates sequences of token ids for models with a language modeling head.

Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
model's default generation configuration. You can override any `generation_config` by passing the corresponding
parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.

For an overview of generation strategies and code examples, check out the [following
guide](../generation_strategies).

**Parameters:**

inputs (`torch.Tensor` of varying shape depending on the modality, *optional*) : The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs` should be in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of `input_ids`, `input_values`, `input_features`, or `pixel_values`.

generation_config ([GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig), *optional*) : The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which has the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit [GenerationConfig](/docs/transformers/v5.6.1/zh/main_classes/text_generation#transformers.GenerationConfig)'s default values, whose documentation should be checked to parameterize generation.

logits_processor (`LogitsProcessorList`, *optional*) : Custom logits processors that complement the default logits processors built from arguments and generation config. If a logit processor is passed that is already created with the arguments or a generation config an error is thrown. This feature is intended for advanced users.

stopping_criteria (`StoppingCriteriaList`, *optional*) : Custom stopping criteria that complements the default stopping criteria built from arguments and a generation config. If a stopping criteria is passed that is already created with the arguments or a generation config an error is thrown. If your stopping criteria depends on the `scores` input, make sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is intended for advanced users.

prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*) : If provided, this function constraints the beam search to allowed tokens only at each step. If not provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful for constrained generation conditioned on the prefix, as described in [Autoregressive Entity Retrieval](https://huggingface.co/papers/2010.00904).

synced_gpus (`bool`, *optional*) : Whether to continue running the while loop until max_length. Unless overridden, this flag will be set to `True` if using `FullyShardedDataParallel` or DeepSpeed ZeRO Stage 3 with multiple GPUs to avoid deadlocking if one GPU finishes generating before other GPUs. Otherwise, defaults to `False`.

assistant_model (`PreTrainedModel`, *optional*) : An assistant model that can be used to accelerate generation. The assistant model must have the exact same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistant model is much faster than running generation with the model you're calling generate from. As such, the assistant model should be much smaller.

streamer (`BaseStreamer`, *optional*) : Streamer object that will be used to stream the generated sequences. Generated tokens are passed through `streamer.put(token_ids)` and the streamer is responsible for any further processing.

negative_prompt_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : The negative prompt needed for some processors such as CFG. The batch size must match the input batch size. This is an experimental feature, subject to breaking API changes in future versions.

negative_prompt_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : Attention_mask for `negative_prompt_ids`.

custom_generate (`str` or `Callable`, *optional*) : One of the following: - `str` (Hugging Face Hub repository name): runs the custom `generate` function defined at `custom_generate/generate.py` in that repository instead of the standard `generate` method. The repository fully replaces the generation logic, and the return type may differ. - `str` (local repository path): same as above but from a local path, `trust_remote_code` not required. - `Callable`: `generate` will perform the usual input preparation steps, then call the provided callable to run the decoding loop. For more information, see [the docs](../../generation_strategies#custom-generation-methods).

kwargs (`dict[str, Any]`, *optional*) : Ad hoc parametrization of `generation_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.

**Returns:**

`[ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) or `torch.LongTensor``

A [ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) (if `return_dict_in_generate=True`
or when `config.return_dict_in_generate=True`) or a `torch.LongTensor`.

If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) types are:

- [GenerateDecoderOnlyOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateDecoderOnlyOutput),
- [GenerateBeamDecoderOnlyOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateBeamDecoderOnlyOutput)

If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[ModelOutput](/docs/transformers/v5.6.1/zh/main_classes/output#transformers.utils.ModelOutput) types are:

- [GenerateEncoderDecoderOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateEncoderDecoderOutput),
- [GenerateBeamEncoderDecoderOutput](/docs/transformers/v5.6.1/zh/internal/generation_utils#transformers.generation.GenerateBeamEncoderDecoderOutput)
#### compute_transition_scores[[transformers.GenerationMixin.compute_transition_scores]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.1/src/transformers/generation/utils.py#L1335)

Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was
used). This is a convenient method to quickly obtain the scores of the selected tokens at generation time.

Examples:

```python
>>> from transformers import GPT2Tokenizer, AutoModelForCausalLM
>>> import numpy as np

>>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer.pad_token_id = tokenizer.eos_token_id
>>> inputs = tokenizer(["Today is"], return_tensors="pt")

>>> # Example 1: Print the scores for each token generated with Greedy Search
>>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
>>> transition_scores = model.compute_transition_scores(
...     outputs.sequences, outputs.scores, normalize_logits=True
... )
>>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
>>> # encoder-decoder models, like BART or T5.
>>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
>>> generated_tokens = outputs.sequences[:, input_length:]
>>> for tok, score in zip(generated_tokens[0], transition_scores[0]):
...     # | token | token string | log probability | probability
...     print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
|   262 |  the     | -1.414 | 24.33%
|  1110 |  day     | -2.609 | 7.36%
|   618 |  when    | -2.010 | 13.40%
|   356 |  we      | -1.859 | 15.58%
|   460 |  can     | -2.508 | 8.14%

>>> # Example 2: Reconstruct the sequence scores from Beam Search
>>> outputs = model.generate(
...     **inputs,
...     max_new_tokens=5,
...     num_beams=4,
...     num_return_sequences=4,
...     return_dict_in_generate=True,
...     output_scores=True,
... )
>>> transition_scores = model.compute_transition_scores(
...     outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
... )
>>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
>>> # Tip 1: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
>>> # use case, you might want to recompute it with `normalize_logits=True`.
>>> # Tip 2: the output length does NOT include the input length
>>> output_length = np.sum(transition_scores.numpy() >> length_penalty = model.generation_config.length_penalty
>>> reconstructed_scores = transition_scores.sum(axis=1) / (output_length**length_penalty)
>>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
True
```

**Parameters:**

sequences (`torch.LongTensor`) : 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)`) : 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*) : Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at generate-time.

normalize_logits (`bool`, *optional*, defaults to `False`) : Whether to normalize the logits (which, for legacy reasons, may be unnormalized).

**Returns:**

``torch.Tensor``

A `torch.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing
the transition scores (logits)