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Idefics3

Idefics3 모델은 이미지와 텍스트 입력의 임의의 시퀀스를 받아 텍스트 출력을 생성하는 오픈 멀티모달 모델입니다. 이 모델은 이미지에 대한 질문에 답하거나, 시각적 콘텐츠를 설명하고, 여러 이미지를 기반으로 이야기를 만들거나, 시각적 입력 없이 순수한 언어 모델로 작동할 수 있습니다. RBLN NPU는 Optimum RBLN을 사용하여 Idefics3 모델 추론을 가속화할 수 있습니다.

API Reference

Classes

RBLNIdefics3VisionTransformer

Bases: RBLNModel

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

RBLNIdefics3ForConditionalGeneration

Bases: RBLNModel

RBLNIdefics3ForConditionalGeneration is a multi-modal model that integrates 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 RBLNIdefics3ForConditionalGenerationConfig class for details.

Examples:

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from optimum.rbln import RBLNIdefics3ForConditionalGeneration

model = RBLNIdefics3ForConditionalGeneration.from_pretrained(
    "HuggingFaceM4/idefics3-8b",
    export=True,
    rbln_config={
        "vision_model": {
            "device": 0,
        },
        "text_model": {
            "batch_size": 1,
            "max_seq_len": 131_072,
            "tensor_parallel_size": 8,
            "use_inputs_embeds": True,
            "attn_impl": "flash_attn",
            "kvcache_partition_len": 16_384,
            "device": [0, 1, 2, 3, 4, 5, 6, 7],
        },
    },
)

model.save_pretrained("compiled-idefics3-8b")

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

Classes

RBLNIdefics3VisionTransformerConfig

Bases: RBLNModelConfig

Functions

__init__(cls_name=None, create_runtimes=None, optimize_host_memory=None, device=None, device_map=None, activate_profiler=None, npu=None, tensor_parallel_size=None, timeout=None, optimum_rbln_version=None, _torch_dtype=None, _compile_cfgs=[], **kwargs)

Initialize a RBLN model configuration with runtime options and compile configurations.

Parameters:

Name Type Description Default
cls_name Optional[str]

The class name of the configuration. Defaults to the current class name.

 None
create_runtimes Optional[bool]

Whether to create RBLN runtimes. Defaults to True.

 None
optimize_host_memory Optional[bool]

Whether to optimize host memory usage. Defaults to True.

 None
device Optional[Union[int, List[int]]]

The device(s) to load the model onto. Can be a single device ID or a list.

 None
device_map Optional[Dict[str, Union[int, List[int]]]]

Mapping from compiled model names to device IDs.

 None
activate_profiler Optional[bool]

Whether to activate the profiler for performance analysis.

 None
npu Optional[str]

The NPU device name to use for compilation.

 None
tensor_parallel_size Optional[int]

Size for tensor parallelism to distribute the model across devices.

 None
timeout Optional[int]

The timeout for the runtime in seconds. If it isn't provided, it will be set to 60 by default.

 None
optimum_rbln_version Optional[str]

The optimum-rbln version used for this configuration.

 None
_torch_dtype Optional[str]

The data type to use for the model.

 None
_compile_cfgs List[RBLNCompileConfig]

List of compilation configurations for the model.

 []
kwargs Any

Additional keyword arguments.

 {}

Raises:

Type Description
ValueError

If unexpected keyword arguments are provided.
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.

RBLNIdefics3ForConditionalGenerationConfig

Bases: RBLNModelConfig

Configuration class for RBLNIdefics3ForConditionalGeneration models.

This class extends RBLNModelConfig to include settings specific to the Idefics3 vision-language model optimized for RBLN devices. It allows configuration of the batch size and separate configurations for the vision and text submodules.

Attributes:

Name Type Description
submodules List[str]

List of submodules included in the model. Defaults to ["vision_model", "text_model"].

Functions

__init__(batch_size=None, vision_model=None, text_model=None, **kwargs)

Parameters:

Name Type Description Default
batch_size Optional[int]

The batch size for inference. Defaults to 1.

 None
vision_model Optional[RBLNModelConfig]

Configuration for the vision transformer 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
text_model Optional[RBLNModelConfig]

Configuration for the text 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.