# CodeGen

## Overview

CodeGen モデルは、[A Conversational Paradigm for Program Synthesis](https://huggingface.co/papers/2203.13474) で Erik Nijkamp、Bo Pang、林宏明、Lifu Tu、Huan Wang、Yingbo Zhou、Silvio Savarese、Caiming Xiong およびカイミン・ションさん。

CodeGen は、[The Pile](https://pile.eleuther.ai/)、BigQuery、BigPython で順次トレーニングされたプログラム合成用の自己回帰言語モデルです。

論文の要約は次のとおりです。

*プログラム合成は、与えられた問題仕様の解決策としてコンピューター プログラムを生成することを目的としています。我々は、大規模な言語モデルを介した会話型プログラム合成アプローチを提案します。これは、従来のアプローチで直面した広大なプログラム空間とユーザーの意図の仕様を検索するという課題に対処します。私たちの新しいアプローチでは、仕様とプログラムを作成するプロセスを、ユーザーとシステムの間の複数回の対話として捉えます。これはプログラム合成をシーケンス予測問題として扱い、仕様が自然言語で表現され、目的のプログラムが条件付きでサンプリングされます。私たちは、自然言語とプログラミング言語のデータに基づいて、CodeGen と呼ばれる大規模な言語モデルのファミリーをトレーニングします。データの監視が弱く、データ サイズとモデル サイズが拡大すると、単純な自己回帰言語モデリングから会話能力が生まれます。会話型プログラム合成におけるモデルの動作を研究するために、マルチターン プログラミング ベンチマーク (MTPB) を開発します。このベンチマークでは、各問題を解決するには、ユーザーとモデル間のマルチターン会話を介したマルチステップ合成が必要です。私たちの調査結果は、会話機能の出現と、提案されている会話プログラム合成パラダイムの有効性を示しています。さらに、私たちのモデル CodeGen (TPU-v4 でトレーニングされた最大 16B パラメーターを含む) は、HumanEval ベンチマークで OpenAI の Codex を上回ります。私たちはチェックポイントを含むトレーニング ライブラリ JaxFormer をオープン ソースのコントリビューションとして利用できるようにしています: [この https URL](https://github.com/salesforce/codegen)*。

このモデルは [林 宏明](https://huggingface.co/rooa) によって寄稿されました。
元のコードは [ここ](https://github.com/salesforce/codegen) にあります。

## Checkpoint Naming

* CodeGen モデル [チェックポイント](https://huggingface.co/models?other=codegen) は、可変サイズのさまざまな事前トレーニング データで利用できます。
* 形式は「Salesforce/codegen-{size}-{data}」です。ここで、
  * `size`: `350M`、`2B`、`6B`、`16B`
  * `data`:
    * `nl`: パイルで事前トレーニング済み
    * `multi`: `nl` で初期化され、複数のプログラミング言語データでさらに事前トレーニングされます。
    * `mono`: `multi` で初期化され、Python データでさらに事前トレーニングされます。
* たとえば、`Salesforce/codegen-350M-mono` は、Pile、複数のプログラミング言語、および Python で順次事前トレーニングされた 3 億 5,000 万のパラメーターのチェックポイントを提供します。

## Usage example

```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer

>>> checkpoint = "Salesforce/codegen-350M-mono"
>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)

>>> text = "def hello_world():"

>>> completion = model.generate(**tokenizer(text, return_tensors="pt"))

>>> print(tokenizer.decode(completion[0]))
def hello_world():
    print("Hello World")

hello_world()
```

## Resources

- [因果言語モデリング タスク ガイド](../tasks/language_modeling)

## CodeGenConfig[[transformers.CodeGenConfig]]

#### transformers.CodeGenConfig[[transformers.CodeGenConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/configuration_codegen.py#L24)

This is the configuration class to store the configuration of a CodeGenModel. It is used to instantiate a Codegen
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.5.4/ja/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.5.4/ja/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import CodeGenConfig, CodeGenModel

>>> # Initializing a CodeGen 6B configuration
>>> configuration = CodeGenConfig()

>>> # Initializing a model (with random weights) from the configuration
>>> model = CodeGenModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

vocab_size (`int`, *optional*, defaults to `50400`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.

n_positions (`int`, *optional*, defaults to `2048`) : The maximum sequence length that this model might ever be used with.

n_ctx (`int`, *optional*, defaults to 2048) : This attribute is used in `CodeGenModel.__init__` without any real effect. The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

n_embd (`int`, *optional*, defaults to `4096`) : Dimensionality of the embeddings and hidden states.

n_layer (`int`, *optional*, defaults to `28`) : Number of hidden layers in the Transformer decoder.

n_head (`int`, *optional*, defaults to `16`) : Number of attention heads for each attention layer in the Transformer decoder.

rotary_dim (`int`, *optional*, defaults to 64) : Number of dimensions in the embedding that Rotary Position Embedding is applied to.

n_inner (`int`, *optional*) : Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd

activation_function (`str`, *optional*, defaults to `gelu_new`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

resid_pdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

embd_pdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the embeddings.

attn_pdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities.

layer_norm_epsilon (`float`, *optional*, defaults to `1e-05`) : The epsilon used by the layer normalization layers.

initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

use_cache (`bool`, *optional*, defaults to `True`) : Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True` or when the model is a decoder-only generative model.

bos_token_id (`int`, *optional*, defaults to `50256`) : Token id used for beginning-of-stream in the vocabulary.

eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `50256`) : Token id used for end-of-stream in the vocabulary.

tie_word_embeddings (`bool`, *optional*, defaults to `False`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.

## CodeGenTokenizer[[transformers.CodeGenTokenizer]]

#### transformers.CodeGenTokenizer[[transformers.CodeGenTokenizer]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/tokenization_codegen.py#L37)

Construct a CodeGen tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.

This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will

be encoded differently whether it is at the beginning of the sentence (without space) or not:

```python
>>> from transformers import CodeGenTokenizer

>>> tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
>>> tokenizer("Hello world")["input_ids"]
[15496, 995]

>>> tokenizer(" Hello world")["input_ids"]
[18435, 995]
```

You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
the model was not pretrained this way, it might yield a decrease in performance.

When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.

This tokenizer inherits from [TokenizersBackend](/docs/transformers/v5.5.4/ja/main_classes/tokenizer#transformers.TokenizersBackend) which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.

save_vocabularytransformers.CodeGenTokenizer.save_vocabularyhttps://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/tokenization_utils_tokenizers.py#L509[{"name": "save_directory", "val": ": str"}, {"name": "filename_prefix", "val": ": str | None = None"}]

**Parameters:**

vocab (`str` or `dict[str, int]`, *optional*) : Custom vocabulary dictionary. If not provided, vocabulary is loaded from `vocab_file`.

merges (`str` or `list[str]`, *optional*) : Custom merges list. If not provided, merges are loaded from `merges_file`.

unk_token (`str`, *optional*, defaults to `""`) : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

bos_token (`str`, *optional*, defaults to `""`) : The beginning of sequence token.

eos_token (`str`, *optional*, defaults to `""`) : The end of sequence token.

pad_token (`str`, *optional*) : The token used for padding, for example when batching sequences of different lengths.

add_prefix_space (`bool`, *optional*, defaults to `False`) : Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (CodeGen tokenizer detect beginning of words by the preceding space).

return_token_type_ids (`bool`, *optional*, defaults to `False`) : Whether to return token type IDs.

## CodeGenTokenizerFast[[transformers.CodeGenTokenizer]]

#### transformers.CodeGenTokenizer[[transformers.CodeGenTokenizer]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/tokenization_codegen.py#L37)

Construct a CodeGen tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.

This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will

be encoded differently whether it is at the beginning of the sentence (without space) or not:

```python
>>> from transformers import CodeGenTokenizer

>>> tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
>>> tokenizer("Hello world")["input_ids"]
[15496, 995]

>>> tokenizer(" Hello world")["input_ids"]
[18435, 995]
```

You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
the model was not pretrained this way, it might yield a decrease in performance.

When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.

This tokenizer inherits from [TokenizersBackend](/docs/transformers/v5.5.4/ja/main_classes/tokenizer#transformers.TokenizersBackend) which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.

decodetransformers.CodeGenTokenizer.decodehttps://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/tokenization_codegen.py#L141[{"name": "token_ids", "val": ": typing.Union[int, list[int], numpy.ndarray, ForwardRef('torch.Tensor')]"}, {"name": "skip_special_tokens", "val": ": bool = False"}, {"name": "clean_up_tokenization_spaces", "val": ": bool | None = None"}, {"name": "truncate_before_pattern", "val": ": list[str] | None = None"}, {"name": "**kwargs", "val": ""}]- **token_ids** (`Union[int, List[int], np.ndarray, torch.Tensor]`) --
  List of tokenized input ids. Can be obtained using the `__call__` method.
- **skip_special_tokens** (`bool`, *optional*, defaults to `False`) --
  Whether or not to remove special tokens in the decoding.
- **clean_up_tokenization_spaces** (`bool`, *optional*) --
  Whether or not to clean up the tokenization spaces. If `None`, will default to
  `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
- **truncate_before_pattern** (`List[str]`, *optional*, defaults to `None`) --
  A list of regular expression strings that will be used to truncate the returned string. This can be
  used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning
  of a new line). An example pattern could be `["^#", re.escape(""), "^'''", "0`str`The decoded sentence.

Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.

Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.

"]`.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.

**Parameters:**

vocab (`str` or `dict[str, int]`, *optional*) : Custom vocabulary dictionary. If not provided, vocabulary is loaded from `vocab_file`.

merges (`str` or `list[str]`, *optional*) : Custom merges list. If not provided, merges are loaded from `merges_file`.

unk_token (`str`, *optional*, defaults to `""`) : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

bos_token (`str`, *optional*, defaults to `""`) : The beginning of sequence token.

eos_token (`str`, *optional*, defaults to `""`) : The end of sequence token.

pad_token (`str`, *optional*) : The token used for padding, for example when batching sequences of different lengths.

add_prefix_space (`bool`, *optional*, defaults to `False`) : Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (CodeGen tokenizer detect beginning of words by the preceding space).

return_token_type_ids (`bool`, *optional*, defaults to `False`) : Whether to return token type IDs.

**Returns:**

``str``

The decoded sentence.

## CodeGenModel[[transformers.CodeGenModel]]

#### transformers.CodeGenModel[[transformers.CodeGenModel]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/modeling_codegen.py#L276)

The bare Codegen Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v5.5.4/ja/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.CodeGenModel.forwardhttps://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/modeling_codegen.py#L299[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "token_type_ids", "val": ": torch.LongTensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.5.4/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.5.4/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.5.4/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only `Cache` instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, `DynamicCache` will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
  of shape `(batch_size, sequence_length)`.
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
  model's internal embedding lookup matrix.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[BaseModelOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CodeGenConfig](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenConfig)) and inputs.
The [CodeGenModel](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenModel) forward method, overrides the `__call__` special method.

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

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a `Cache` instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

**Parameters:**

config ([CodeGenModel](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenModel)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.5.4/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[BaseModelOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CodeGenConfig](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenConfig)) and inputs.

## CodeGenForCausalLM[[transformers.CodeGenForCausalLM]]

#### transformers.CodeGenForCausalLM[[transformers.CodeGenForCausalLM]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/modeling_codegen.py#L411)

The CodeGen Model transformer with a language modeling head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v5.5.4/ja/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.CodeGenForCausalLM.forwardhttps://github.com/huggingface/transformers/blob/v5.5.4/src/transformers/models/codegen/modeling_codegen.py#L422[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "token_type_ids", "val": ": torch.LongTensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "logits_to_keep", "val": ": int | torch.Tensor = 0"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.5.4/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.5.4/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.5.4/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only `Cache` instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, `DynamicCache` will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
  of shape `(batch_size, sequence_length)`.
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
  model's internal embedding lookup matrix.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
  `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
  are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.
- **logits_to_keep** (`Union[int, torch.Tensor]`, *optional*, defaults to `0`) --
  If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
  `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
  token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
  If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
  This is useful when using packed tensor format (single dimension for batch and sequence length).0[CausalLMOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CodeGenConfig](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenConfig)) and inputs.
The [CodeGenForCausalLM](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenForCausalLM) forward method, overrides the `__call__` special method.

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

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a `Cache` instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Example:

```python
```

**Parameters:**

config ([CodeGenForCausalLM](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenForCausalLM)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.5.4/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[CausalLMOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)``

A [CausalLMOutputWithPast](/docs/transformers/v5.5.4/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CodeGenConfig](/docs/transformers/v5.5.4/ja/model_doc/codegen#transformers.CodeGenConfig)) and inputs.