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| import copy |
|
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| from transformers import AutoConfig, LlamaConfig |
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
| from .configuration_siglip import SiglipVisionConfig |
| from .configuration_qwen2 import Qwen2Config |
| from .configuration_multi_backbone_channel_concatentation_model import MultiBackboneChannelConcatenationVisionModelConfig |
| logger = logging.get_logger(__name__) |
|
|
|
|
| class Eagle2ChatConfig(PretrainedConfig): |
| model_type = 'eagle_chat' |
| 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, |
| min_dynamic_patch=1, |
| max_dynamic_patch=6, |
| mlp_checkpoint=True, |
| pre_feature_reduction=False, |
| keep_aspect_ratio=False, |
| vocab_size=-1, |
| **kwargs): |
| super().__init__(**kwargs) |
|
|
| if vision_config is None: |
| vision_config = {} |
| logger.info('vision_config is None. Initializing Vision Encoders with default values.') |
|
|
| if llm_config is None: |
| llm_config = {} |
| logger.info('llm_config is None. Initializing the LLM config with default values') |
|
|
| if vision_config['model_type'] == 'siglip_vision_model': |
| self.vision_config = SiglipVisionConfig(**vision_config) |
| elif vision_config['model_type'].startswith("MOB"): |
| self.vision_config = MultiBackboneChannelConcatenationVisionModelConfig(**vision_config) |
| else: |
| raise ValueError('Unsupported model_type: {}'.format(vision_config['model_type'])) |
|
|
| if llm_config['architectures'][0] == 'LlamaForCausalLM': |
| self.llm_config = LlamaConfig(**llm_config) |
| elif llm_config['architectures'][0] == 'Qwen2ForCausalLM': |
| self.llm_config = Qwen2Config(**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.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.min_dynamic_patch = min_dynamic_patch |
| self.max_dynamic_patch = max_dynamic_patch |
| self.mlp_checkpoint = mlp_checkpoint |
| self.pre_feature_reduction = pre_feature_reduction |
| self.keep_aspect_ratio = keep_aspect_ratio |
| self.vocab_size = self.llm_config.vocab_size |
| logger.info(f'keep_aspect_ratio: {self.keep_aspect_ratio}') |
| logger.info(f'vision_select_layer: {self.select_layer}') |
| 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['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['min_dynamic_patch'] = self.min_dynamic_patch |
| output['max_dynamic_patch'] = self.max_dynamic_patch |
| output['keep_aspect_ratio'] = self.keep_aspect_ratio |
|
|
| return output |
|
|
