|
|
|
|
|
|
|
|
|
|
|
|
|
import copy |
|
|
|
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__) |
|
|
|
|
|
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 |
|
|
