104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""ColPali model configuration"""
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import logging
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from copy import deepcopy
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from ...configuration_utils import PretrainedConfig
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from ..auto import CONFIG_MAPPING, AutoConfig
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logger = logging.getLogger(__name__)
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class ColPaliConfig(PretrainedConfig):
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r"""
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Configuration class to store the configuration of a [`ColPaliForRetrieval`]. It is used to instantiate an instance
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of `ColPaliForRetrieval` according to the specified arguments, defining the model architecture following the methodology
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from the "ColPali: Efficient Document Retrieval with Vision Language Models" paper.
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Creating a configuration with the default settings will result in a configuration where the VLM backbone is set to the
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default PaliGemma configuration, i.e the one from [vidore/colpali-v1.2](https://huggingface.co/vidore/colpali-v1.2).
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Note that contrarily to what the class name suggests (actually the name refers to the ColPali **methodology**), you can
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use a different VLM backbone model than PaliGemma by passing the corresponding VLM configuration to the class constructor.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vlm_config (`PretrainedConfig`, *optional*):
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Configuration of the VLM backbone model.
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text_config (`PretrainedConfig`, *optional*):
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Configuration of the text backbone model. Overrides the `text_config` attribute of the `vlm_config` if provided.
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embedding_dim (`int`, *optional*, defaults to 128):
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Dimension of the multi-vector embeddings produced by the model.
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Example:
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```python
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from transformers.models.colpali import ColPaliConfig, ColPaliForRetrieval
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config = ColPaliConfig()
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model = ColPaliForRetrieval(config)
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```
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"""
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model_type = "colpali"
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sub_configs = {"vlm_config": PretrainedConfig, "text_config": AutoConfig}
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def __init__(
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self,
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vlm_config=None,
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text_config=None,
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embedding_dim: int = 128,
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**kwargs,
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):
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if vlm_config is None:
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vlm_config = CONFIG_MAPPING["paligemma"]()
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logger.info(
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"`vlm_config` is `None`. Initializing `vlm_config` with the `PaliGemmaConfig` with default values."
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)
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elif isinstance(vlm_config, dict):
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vlm_config = deepcopy(vlm_config)
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if "model_type" not in vlm_config:
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raise KeyError(
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"The `model_type` key is missing in the `vlm_config` dictionary. Please provide the model type."
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)
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elif vlm_config["model_type"] not in CONFIG_MAPPING:
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raise ValueError(
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f"The model type `{vlm_config['model_type']}` is not supported. Please provide a valid model type."
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)
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vlm_config = CONFIG_MAPPING[vlm_config["model_type"]](**vlm_config)
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elif isinstance(vlm_config, PretrainedConfig):
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vlm_config = vlm_config
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else:
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raise TypeError(
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f"Invalid type for `vlm_config`. Expected `PretrainedConfig`, `dict`, or `None`, but got {type(vlm_config)}."
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)
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self.vlm_config = vlm_config
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self.text_config = text_config if text_config is not None else vlm_config.text_config
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if isinstance(self.text_config, dict):
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text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "gemma"
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self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
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self.embedding_dim = embedding_dim
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super().__init__(**kwargs)
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__all__ = ["ColPaliConfig"]
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