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| import copy |
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| from transformers.models.llama.configuration_llama import LlamaConfig |
| from transformers.models.qwen2.configuration_qwen2 import Qwen2Config |
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
| from transformers.models.siglip.configuration_siglip import SiglipVisionConfig |
| logger = logging.get_logger(__name__) |
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|
| class Eagle2_5_VLConfig(PretrainedConfig): |
| model_type = 'eagle_2_5_vl' |
| is_composition = True |
| sub_configs = {"vision_config": SiglipVisionConfig, "text_config": Qwen2Config} |
| def __init__( |
| self, |
| vision_config=None, |
| text_config=None, |
| use_backbone_lora=0, |
| use_llm_lora=0, |
| pad2square=False, |
| select_layer=-4, |
| force_image_size=None, |
| downsample_ratio=0.5, |
| template=None, |
| dynamic_image_size=False, |
| use_thumbnail=False, |
| loss_version='v1', |
| min_dynamic_tiles=1, |
| max_dynamic_tiles=6, |
| mlp_checkpoint=False, |
| initializer_range=0.02, |
| _attn_implementation='flash_attention_2', |
| _attn_implementation_autoset=False, |
| llm_config=None, |
| image_token_index=None, |
| **kwargs): |
| super().__init__(**kwargs) |
|
|
| if vision_config is None: |
| vision_config = {'model_type': 'siglip_vision_model'} |
| logger.info('vision_config is None. Initializing the InternVisionConfig with default values.') |
|
|
| if text_config is None: |
| text_config = {'architectures': ['Qwen2ForCausalLM']} |
| logger.info('text_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).') |
|
|
| if vision_config['model_type'] == 'siglip_vision_model': |
| self.vision_config = SiglipVisionConfig(**vision_config) |
| else: |
| raise ValueError('Unsupported model_type: {}'.format(vision_config['model_type'])) |
|
|
|
|
| if text_config['architectures'][0] == 'LlamaForCausalLM': |
| self.text_config = LlamaConfig(**text_config) |
| elif text_config['architectures'][0] == 'Qwen2ForCausalLM': |
| self.text_config = Qwen2Config(**text_config) |
| else: |
| raise ValueError('Unsupported architecture: {}'.format(text_config['architectures'][0])) |
| self.use_backbone_lora = use_backbone_lora |
| self.use_llm_lora = use_llm_lora |
| self.mlp_checkpoint = mlp_checkpoint |
| self.pad2square = pad2square |
| 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.loss_version = loss_version |
| self.initializer_range = initializer_range |
| self.min_dynamic_tiles = min_dynamic_tiles |
| self.max_dynamic_tiles = max_dynamic_tiles |
| self.tie_word_embeddings = self.text_config.tie_word_embeddings |
| self._attn_implementation = _attn_implementation |
| self._attn_implementation_autoset = _attn_implementation_autoset |
| self.image_token_index = image_token_index |
| logger.info(f'min_dynamic_tiles: {self.min_dynamic_tiles}') |
| logger.info(f'max_dynamic_tiles: {self.max_dynamic_tiles}') |
|
|
| 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['text_config'] = self.text_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['pad2square'] = self.pad2square |
| 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_tiles'] = self.min_dynamic_tiles |
| output['max_dynamic_tiles'] = self.max_dynamic_tiles |
| output['tie_word_embeddings'] = self.tie_word_embeddings |
| output['_attn_implementation'] = self._attn_implementation |
| output['_attn_implementation_autoset'] = self._attn_implementation_autoset |
|
|
| return output |
|
|
