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ColPali

ColPali 모델은 새로운 아키텍처와 학습 전략을 사용해 문서를 시각적 특징으로 효율적으로 색인하는 시각 언어 모델(Vision Language Model, VLM)입니다. RBLN Optimum을 사용하면 RBLN NPU에서 ColPali 모델 추론을 가속할 수 있습니다.

API 참조

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:

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from optimum.rbln import RBLNColPaliForRetrieval

# Simple usage using rbln_* arguments
model = RBLNColPaliForRetrieval.from_pretrained(
    "vidore/colpali-v1.3-hf",
    export=True,
    rbln_config={
        "vlm": {
            "language_model": {
                "prefill_chunk_size": 8192, # same as model's max_position_embeddings (max_seq_len)
            }
        }
    }
)

# Using a config dictionary
rbln_config = {
    "vlm": {
        "language_model": {
            "prefill_chunk_size": 8192, # same as model's max_position_embeddings (max_seq_len)
        }
    }
}
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(
    vlm={
        "language_model": {"prefill_chunk_size": 8192},
    },
    output_hidden_states=False,
    num_devices=4
)
model = RBLNColPaliForRetrieval.from_pretrained(
    "vidore/colpali-v1.3-hf",
    export=True,
    rbln_config=config
)

forward(input_ids=None, pixel_values=None, attention_mask=None, output_hidden_states=None, return_dict=None, **kwargs)

Forward pass for the RBLN-optimized ColPaliForRetrieval model.

Parameters:

Name Type Description Default
input_ids torch.LongTensor of shape (batch_size, sequence_length)

Indices of input sequence tokens in the vocabulary.

 None
pixel_values torch.Tensor of shape (batch_size, num_channels, image_size, image_size)

The tensors corresponding to the input images.

 None
attention_mask torch.Tensor of shape (batch_size, sequence_length)

Mask to avoid performing attention on padding token indices.

 None
output_hidden_states bool

Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

 None
return_dict bool

Whether or not to return a ModelOutput instead of a plain tuple.

 None

Returns:

Type Description
tuple | ColPaliForRetrievalOutput

ColPaliForRetrievalOutput or tuple(torch.FloatTensor)

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 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 | None

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 dict | RBLNModelConfig | None

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.

get_hf_class() classmethod

Lazily load and cache the HuggingFace class this model wraps.

The class is looked up in hf_library_name under the name without the RBLN prefix (e.g. RBLNLlamaForCausalLM -> LlamaForCausalLM).

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 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

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 | ModelMixin

The PyTorch model to be compiled. The object must be a HuggingFace transformers PreTrainedModel or a Diffusers component based on diffusers.ModelMixin, such as a VAE, UNet or ControlNet.

 required
config PretrainedConfig | None

The configuration object associated with the model.

 None
rbln_config RBLNModelConfig | dict | None

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.

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 
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from optimum.rbln import RBLNColPaliForRetrieval, RBLNColPaliForRetrievalConfig

# Create a configuration object
config = RBLNColPaliForRetrievalConfig(
    output_hidden_states=False,
    num_devices=4,
)

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

__init__(batch_size=None, vlm=None, output_hidden_states=None, **kwargs)

Parameters:

Name Type Description Default
batch_size int | None

The batch size for the model.

 None
vlm RBLNModelConfig | None

Configuration for the VLM component.

 None
output_hidden_states bool | None

Whether to output the hidden states of the decoder. Defaults to False.

 None
kwargs Any

Additional arguments passed to the parent RBLNModelConfig.

 {}

Raises: ValueError: If batch_size is not a positive integer.

from_pretrained(path, rbln_config=None, return_unused_kwargs=False, **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
rbln_config dict[str, Any] | None

Additional configuration to override.

 None
return_unused_kwargs bool

Whether to return unused kwargs.

 False
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 Self | tuple[Self, dict[str, Any]]

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.

Examples:

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config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")

initialize_submodule_config(submodule_config=None, force_kwargs=False, **kwargs)

Build the config of a submodule from the parent's runtime options and kwargs.

Returns the submodule config instance when its class is known (an instance was passed, or cls_name was given), otherwise the merged kwargs dict for the model class to resolve later.

get_load_overrides()

Extract the options that must cross the load boundary, for passing to from_pretrained.

Returns the explicitly-set runtime options (device, device_map, create_runtimes, activate_profiler, timeout) and the subclass-specific non-save attributes (load-behavior flags such as _load_visual_runtime, which are never serialized so they can only travel with the caller), recursively including submodules. Compile-time attributes are not included: when loading a compiled model they come from the artifact's rbln_config.json, and this dict is merged on top of it.

Returns:

Type Description
dict[str, Any]

Dictionary of load overrides, keyed like the rbln_config dict accepted by from_pretrained.