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|
| | import copy |
| |
|
| | 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 |
| |
|
