import copy from transformers import LlamaConfig from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from .configuration_intern_vit import InternVisionConfig from .configuration_internlm2 import InternLM2Config logger = logging.get_logger(__name__) class PhantomConfig(PretrainedConfig): model_type = 'phantom' is_composition = True def __init__( self, vision_config=None, llm_config=None, use_backbone_lora=0, use_llm_lora=0, force_image_size=None, downsample_ratio=0.5, template=None, dynamic_image_size=False, use_thumbnail=False, min_dynamic_patch=1, max_dynamic_patch=6, **kwargs): super().__init__(**kwargs) if vision_config is None: vision_config = {} logger.info('vision_config is None. Initializing the InternVisionConfig with default values.') if llm_config is None: llm_config = {} logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).') self.vision_config = InternVisionConfig(**vision_config) if llm_config['architectures'][0] == 'LlamaForCausalLM': self.llm_config = LlamaConfig(**llm_config) elif llm_config['architectures'][0] == 'InternLM2ForCausalLM': self.llm_config = InternLM2Config(**llm_config) else: raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0])) self.use_backbone_lora = use_backbone_lora self.use_llm_lora = use_llm_lora 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.min_dynamic_patch = min_dynamic_patch self.max_dynamic_patch = max_dynamic_patch 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. Override 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.__class__.model_type output['use_backbone_lora'] = self.use_backbone_lora output['use_llm_lora'] = self.use_llm_lora 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['min_dynamic_patch'] = self.min_dynamic_patch output['max_dynamic_patch'] = self.max_dynamic_patch return output