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# Eagle2
# Copyright (c) 2025 NVIDIA
# Licensed under The Apache License [see LICENSE for details]
# --------------------------------------------------------
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
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