ColPali

ColPali 모델은 시각적 특징을 효율적으로 색인화하기 위해, 새로운 아키텍처와 학습 전략을 사용하는 시각 언어 모델(Vision Language Model, VLM)입니다. RBLN NPU는 Optimum RBLN을 사용하여 ColPali 모델 추론을 가속화할 수 있습니다.

API Reference

Classes

RBLNColPaliForRetrieval

Bases: RBLNModel

The ColPali Model transformer for document retrieval using vision-language models. This model inherits from [RBLNModel]. Check the superclass documentation for the generic methods the library implements for all its models.

A class to convert and run pre-trained transformers based ColPaliForRetrieval model on RBLN devices. It implements the methods to convert a pre-trained transformers ColPaliForRetrieval model into a RBLN transformer model by:

• transferring the checkpoint weights of the original into an optimized RBLN graph,
• compiling the resulting graph using the RBLN compiler.

Configuration: This model uses [RBLNColPaliForRetrievalConfig] for configuration. When calling methods like from_pretrained or from_model, the rbln_config parameter should be an instance of [RBLNColPaliForRetrievalConfig] or a dictionary conforming to its structure.

See the [RBLNColPaliForRetrievalConfig] class for all available configuration options.

Examples:

from optimum.rbln import RBLNColPaliForRetrieval

# Simple usage using rbln_* arguments
# `max_seq_lens` is automatically inferred from the model config
model = RBLNColPaliForRetrieval.from_pretrained(
    "vidore/colpali-v1.3-hf",
    export=True,
    rbln_max_seq_lens=1152,
)

# Using a config dictionary
rbln_config = {
    "max_seq_lens": 1152,
    "output_hidden_states": False,
}
model = RBLNColPaliForRetrieval.from_pretrained(
    "vidore/colpali-v1.3-hf",
    export=True,
    rbln_config=rbln_config
)

# Using a RBLNColPaliForRetrievalConfig instance (recommended for type checking)
from optimum.rbln import RBLNColPaliForRetrievalConfig

config = RBLNColPaliForRetrievalConfig(
    max_seq_lens=1152,
    output_hidden_states=False,
    tensor_parallel_size=4
)
model = RBLNColPaliForRetrieval.from_pretrained(
    "vidore/colpali-v1.3-hf",
    export=True,
    rbln_config=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

Classes

RBLNColPaliForRetrievalConfig

Bases: RBLNModelConfig

Configuration class for RBLN ColPali models for document retrieval.

This class extends RBLNModelConfig with specific configurations for ColPali models, including vision tower settings and multi-sequence length support.

Example usage:

from optimum.rbln import RBLNColPaliForRetrieval, RBLNColPaliForRetrievalConfig

# Create a configuration object
config = RBLNColPaliForRetrievalConfig(
    max_seq_lens=1152,
    output_hidden_states=False,
    tensor_parallel_size=4
)

# Use the configuration with from_pretrained
model = RBLNColPaliForRetrieval.from_pretrained(
    "vidore/colpali-v1.3-hf",
    export=True,
    rbln_config=config
)

Functions

__init__(max_seq_lens=None, output_hidden_states=None, vision_tower=None, **kwargs)

Parameters:

Name	Type	Description	Default
max_seq_lens	Union[int, List[int]]	The maximum sequence lengths for the language model. This can be multiple values, and the model will be compiled for each max_seq_len, allowing selection of the most appropriate max_seq_len at inference time.	None
output_hidden_states	Optional[bool]	Whether to output the hidden states of the language model.	None
vision_tower	Optional[RBLNModelConfig]	Configuration for the vision encoder component.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises: 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.