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LLaVa

LLaVa 모델은 이미지와 텍스트 입력을 모두 처리할 수 있는 멀티모달 모델입니다. 주로 시각적 질의응답(Visual Question Answering)과 같은 작업에 사용됩니다. RBLN NPU는 Optimum RBLN을 사용하여 LLaVa 모델 추론을 가속화할 수 있습니다.

API Reference

Classes

RBLNLlavaForConditionalGeneration

Bases: RBLNModel

RBLNLlavaForConditionalGeneration is a multi-modal model that combines vision and language processing capabilities, optimized for RBLN NPUs. It is designed for conditional generation tasks that involve both image and text inputs. This model inherits from [RBLNModel]. Check the superclass documentation for the generic methods the library implements for all its models.

Important Note

This model includes a Large Language Model (LLM) as a submodule. For optimal performance, it is highly recommended to use tensor parallelism for the language model. This can be achieved by using the rbln_config parameter in the from_pretrained method. Refer to the from_pretrained documentation and the RBLNLlavaForConditionalGeneration class for details.

Examples:

from optimum.rbln import RBLNLlavaForConditionalGeneration
model = RBLNLlavaForConditionalGeneration.from_pretrained(
    "llava-hf/llava-1.5-7b-hf",
    export=True,
    rbln_config={
        "vision_tower": {"output_hidden_states": True},
        "language_model": {
            "tensor_parallel_size": 4,
            "use_inputs_embeds": True,  # In Llava, language model must use inputs_embeds as input.
        },
    },
)
model.save_pretrained("compiled-llava-1.5-7b-hf")

# Using a RBLNLlavaForConditionalGenerationConfig instance (recommended for type checking)
from optimum.rbln import RBLNLlavaForConditionalGenerationConfig
vision_config = RBLNCLIPVisionModelConfig(
    batch_size=1,
    output_hidden_states=True
)
language_model_config = RBLNLlamaForCausalLMConfig(
    batch_size=1,
    max_seq_len=4096,
    use_inputs_embeds=True,
    tensor_parallel_size=4
)
llava_config = RBLNLlavaForConditionalGenerationConfig(
    batch_size=1,
    vision_tower=vision_config,
    language_model=language_model_config
)
model = RBLNLlavaForConditionalGeneration.from_pretrained(
    "llava-hf/llava-1.5-7b-hf",
    export=True,
    rbln_config=llava_config
)

Functions

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **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
config Optional[PretrainedConfig]

The configuration object associated with the model.

None
rbln_config Optional[Union[RBLNModelConfig, Dict]]

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 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
RBLNModel

A RBLN model instance ready for inference on RBLN NPU devices.

from_pretrained(model_id, export=None, 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 Optional[bool]

A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.

None
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 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
RBLNModel

A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

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

Parameters:

Name Type Description Default
save_directory Union[str, Path]

Directory where to save the model file.

required
push_to_hub bool

Whether or not to push your model to the HuggingFace model hub after saving it.

False

Functions

Classes

RBLNLlavaForConditionalGenerationConfig

Bases: RBLNModelConfig

Configuration class for RBLNLlavaForConditionalGenerationConfig.

This configuration class stores the configuration parameters specific to RBLN-optimized LLaVA models for multimodal conditional generation tasks that combine vision and language processing capabilities.

Functions

__init__(batch_size=None, vision_tower=None, language_model=None, **kwargs)

Parameters:

Name Type Description Default
batch_size Optional[int]

The batch size for inference. Defaults to 1.

None
vision_tower Optional[RBLNModelConfig]

Configuration for the vision encoder component. This can include settings specific to the vision encoder, such as input resolution or other vision-related parameters. If not provided, default settings will be used.

None
language_model Optional[RBLNModelConfig]

Configuration for the language model component. This can include settings specific to the language model, such as tensor parallelism or other text-related parameters. If not provided, default settings will be used.

None
kwargs Any

Additional arguments passed to the parent RBLNModelConfig.

{}

Raises:

Type Description
ValueError

If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name Type Description Default
path str

Path to the RBLNModelConfig file or directory containing the config file.

required
kwargs Any

Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.

{}

Returns:

Name Type Description
RBLNModelConfig RBLNModelConfig

The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.