configuration_nvlm_d.py

# --------------------------------------------------------
# Adapted from https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B under MIT License
#     LICENSE is in incl_licenses directory.
# --------------------------------------------------------

import copy

from transformers import AutoConfig, Qwen2Config
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

from .configuration_intern_vit import InternVisionConfig

logger = logging.get_logger(__name__)


class NVLM_D_Config(PretrainedConfig):
    model_type = 'NVLM_D'
    is_composition = True

    def __init__(
        self,
        vision_config=None,
        llm_config=None,
        use_backbone_lora=0,
        use_llm_lora=0,
        select_layer=-1,
        force_image_size=None,
        downsample_ratio=0.5,
        template=None,
        dynamic_image_size=False,
        use_thumbnail=False,
        ps_version='v1',
        min_dynamic_patch=1,
        max_dynamic_patch=6,
        **kwargs
    ):
        super().__init__(**kwargs)

        # Handle vision_config initialization
        if vision_config is None:
            vision_config = {}
            logger.info('vision_config is None. Initializing InternVisionConfig with default values.')

        # Handle llm_config initialization
        if llm_config is None:
            llm_config = {}
            logger.info('llm_config is None. Initializing LLM Config with default values.')

        self.vision_config = InternVisionConfig(**vision_config)

        # Check for supported architecture
        if llm_config.get('architectures', [None])[0] == 'Qwen2ForCausalLM':
            self.llm_config = Qwen2Config(**llm_config)
        else:
            raise ValueError(f"Unsupported architecture: {llm_config.get('architectures', [None])[0]}")

        # Assign configuration values
        self.use_backbone_lora = use_backbone_lora
        self.use_llm_lora = use_llm_lora
        self.select_layer = select_layer
        self.force_image_size = force_image_size
        self.downsample_ratio = downsample_ratio
        self.template = template
        self.dynamic_image_size = dynamic_image_size
        self.use_thumbnail = use_thumbnail
        self.ps_version = ps_version  # Pixel shuffle version
        self.min_dynamic_patch = min_dynamic_patch
        self.max_dynamic_patch = max_dynamic_patch

        # Log important parameters
        logger.info(f'vision_select_layer: {self.select_layer}')
        logger.info(f'ps_version: {self.ps_version}')
        logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
        logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')

    def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Overrides the default `PretrainedConfig.to_dict`.

        Returns:
            Dict[str, Any]: Dictionary of all the attributes that make up this configuration instance.
        """
        output = copy.deepcopy(self.__dict__)
        output['vision_config'] = self.vision_config.to_dict()
        output['llm_config'] = self.llm_config.to_dict()
        output['model_type'] = self.model_type
        output['use_backbone_lora'] = self.use_backbone_lora
        output['use_llm_lora'] = self.use_llm_lora
        output['select_layer'] = self.select_layer
        output['force_image_size'] = self.force_image_size
        output['downsample_ratio'] = self.downsample_ratio
        output['template'] = self.template
        output['dynamic_image_size'] = self.dynamic_image_size
        output['use_thumbnail'] = self.use_thumbnail
        output['ps_version'] = self.ps_version
        output['min_dynamic_patch'] = self.min_dynamic_patch
        output['max_dynamic_patch'] = self.max_dynamic_patch

        return output