| 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 | |