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# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import copy
from typing import Dict, Any, Optional
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from .configuration_intern_vit import InternVisionConfig
logger = logging.get_logger(__name__)
class InternVLChatConfig(PretrainedConfig):
model_type = 'internvl_chat'
is_composition = True
def __init__(
self,
vision_config: Optional[Dict[str, Any]] = None,
llm_config: Optional[Dict[str, Any]] = 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,
ps_version="v1",
min_dynamic_patch=1,
max_dynamic_patch=6,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {'architectures': ['InternVisionModel']}
logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
if llm_config is None:
llm_config = {'architectures': ['Qwen2ForCausalLM']}
logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
assert 'architectures' in llm_config, "Should specify architecture in llm_config"
if isinstance(vision_config, dict):
self.vision_config = InternVisionConfig(**vision_config)
else:
self.vision_config = vision_config
if isinstance(llm_config, dict):
architecture: str = llm_config['architectures'][0]
if architecture == 'LlamaForCausalLM':
from transformers import LlamaConfig
self.llm_config = LlamaConfig(**llm_config)
elif architecture == 'Qwen2ForCausalLM':
from transformers import Qwen2Config
self.llm_config = Qwen2Config(**llm_config)
elif architecture == 'Qwen3MoeForCausalLM':
from transformers import Qwen3MoeConfig
self.llm_config = Qwen3MoeConfig(**llm_config)
elif architecture == 'Qwen3ForCausalLM':
from transformers import Qwen3Config
self.llm_config = Qwen3Config(**llm_config)
else:
raise ValueError('Unsupported architecture: {}'.format(architecture))
else:
self.llm_config = llm_config
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.ps_version = ps_version # pixel shuffle version
self.min_dynamic_patch = min_dynamic_patch
self.max_dynamic_patch = max_dynamic_patch
self.tie_word_embeddings = self.llm_config.tie_word_embeddings
logger.info(f'vision_select_layer: {self.select_layer}')
logger.info(f'ps_version: {self.ps_version}')
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['ps_version'] = self.ps_version
output['min_dynamic_patch'] = self.min_dynamic_patch
output['max_dynamic_patch'] = self.max_dynamic_patch
return output
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