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import copy |
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from typing import Dict, Any, Optional |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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from .configuration_intern_vit import InternVisionConfig |
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logger = logging.get_logger(__name__) |
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class InternVLChatConfig(PretrainedConfig): |
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model_type = 'internvl_chat' |
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is_composition = True |
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def __init__( |
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self, |
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vision_config: Optional[Dict[str, Any]] = None, |
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llm_config: Optional[Dict[str, Any]] = None, |
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use_backbone_lora=0, |
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use_llm_lora=0, |
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select_layer=-1, |
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force_image_size=None, |
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downsample_ratio=0.5, |
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template=None, |
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dynamic_image_size=False, |
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use_thumbnail=False, |
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ps_version="v1", |
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min_dynamic_patch=1, |
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max_dynamic_patch=6, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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if vision_config is None: |
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vision_config = {'architectures': ['InternVisionModel']} |
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logger.info('vision_config is None. Initializing the InternVisionConfig with default values.') |
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if llm_config is None: |
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llm_config = {'architectures': ['Qwen2ForCausalLM']} |
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logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).') |
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assert 'architectures' in llm_config, "Should specify architecture in llm_config" |
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if isinstance(vision_config, dict): |
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self.vision_config = InternVisionConfig(**vision_config) |
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else: |
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self.vision_config = vision_config |
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if isinstance(llm_config, dict): |
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architecture: str = llm_config['architectures'][0] |
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if architecture == 'LlamaForCausalLM': |
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from transformers import LlamaConfig |
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self.llm_config = LlamaConfig(**llm_config) |
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elif architecture == 'Qwen2ForCausalLM': |
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from transformers import Qwen2Config |
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self.llm_config = Qwen2Config(**llm_config) |
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elif architecture == 'Qwen3MoeForCausalLM': |
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from transformers import Qwen3MoeConfig |
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self.llm_config = Qwen3MoeConfig(**llm_config) |
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elif architecture == 'Qwen3ForCausalLM': |
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from transformers import Qwen3Config |
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self.llm_config = Qwen3Config(**llm_config) |
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else: |
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raise ValueError('Unsupported architecture: {}'.format(architecture)) |
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else: |
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self.llm_config = llm_config |
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self.use_backbone_lora = use_backbone_lora |
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self.use_llm_lora = use_llm_lora |
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self.select_layer = select_layer |
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self.force_image_size = force_image_size |
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self.downsample_ratio = downsample_ratio |
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self.template = template |
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self.dynamic_image_size = dynamic_image_size |
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self.use_thumbnail = use_thumbnail |
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self.ps_version = ps_version |
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self.min_dynamic_patch = min_dynamic_patch |
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self.max_dynamic_patch = max_dynamic_patch |
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self.tie_word_embeddings = self.llm_config.tie_word_embeddings |
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logger.info(f'vision_select_layer: {self.select_layer}') |
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logger.info(f'ps_version: {self.ps_version}') |
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logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}') |
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logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}') |
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def to_dict(self): |
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""" |
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. |
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Returns: |
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, |
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""" |
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output = copy.deepcopy(self.__dict__) |
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output['vision_config'] = self.vision_config.to_dict() |
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output['llm_config'] = self.llm_config.to_dict() |
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output['model_type'] = self.__class__.model_type |
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output['use_backbone_lora'] = self.use_backbone_lora |
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output['use_llm_lora'] = self.use_llm_lora |
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output['select_layer'] = self.select_layer |
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output['force_image_size'] = self.force_image_size |
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output['downsample_ratio'] = self.downsample_ratio |
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output['template'] = self.template |
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output['dynamic_image_size'] = self.dynamic_image_size |
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output['use_thumbnail'] = self.use_thumbnail |
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output['ps_version'] = self.ps_version |
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output['min_dynamic_patch'] = self.min_dynamic_patch |
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output['max_dynamic_patch'] = self.max_dynamic_patch |
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return output |
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