BART

BART (Bidirectional and Auto-Regressive Transformers)는 요약 및 번역과 같은 시퀀스-투-시퀀스 작업을 위해 설계된 트랜스포머 인코더-디코더 모델입니다. 텍스트 생성 작업의 성능을 향상시키기 위해 양방향(BERT와 같은) 및 자기회귀적(GPT와 같은) 사전 훈련을 결합합니다. RBLN NPU는 Optimum RBLN을 사용하여 BART 모델 추론을 가속화할 수 있습니다.

주요 클래스

• RBLNBartForConditionalGeneration: RBLN NPU에서 조건부 텍스트 생성을 위한 BART 모델 구현
• RBLNBartForConditionalGenerationConfig: BART 조건부 생성 모델의 설정 클래스

API 참조

Classes

RBLNBartForConditionalGeneration

Bases: RBLNModelForSeq2SeqLM

BART model for conditional text generation optimized for RBLN NPU.

Functions

from_pretrained(model_id, export=False, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	bool	A boolean flag to indicate whether the model should be compiled.	False
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Dict[str, Any]	Additional keyword arguments. Arguments with the prefix 'rbln_' are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
Self	A RBLN model instance ready for inference on RBLN NPU devices.

from_model(model, *, rbln_config=None, **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Dict[str, Any]	Additional keyword arguments. Arguments with the prefix 'rbln_' are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
Self	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, PathLike]	The directory to save the model and its configuration files. Will be created if it doesn't exist.	required

Classes

RBLNBartForConditionalGenerationConfig

Bases: RBLNModelForSeq2SeqLMConfig

Configuration class for BART conditional generation model. Inherits from RBLNModelForSeq2SeqLMConfig with no additional parameters.

Functions

__init__(batch_size=None, enc_max_seq_len=None, dec_max_seq_len=None, use_attention_mask=None, pad_token_id=None, **kwargs)

Parameters:

Name	Type	Description	Default
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
enc_max_seq_len	Optional[int]	Maximum sequence length for the encoder.	None
dec_max_seq_len	Optional[int]	Maximum sequence length for the decoder.	None
use_attention_mask	Optional[bool]	Whether to use attention masks during inference. This is automatically set to True for RBLN-CA02 devices.	None
pad_token_id	Optional[int]	The ID of the padding token in the vocabulary.	None
**kwargs	Dict[str, Any]	Additional arguments passed to the parent RBLNModelConfig.	{}