97 lines
3.8 KiB
Python
97 lines
3.8 KiB
Python
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# --------------------------------------------------------
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# InternVL
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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import copy
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from transformers import AutoConfig, LlamaConfig
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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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from .configuration_internlm2 import InternLM2Config
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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=None,
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llm_config=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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super().__init__(**kwargs)
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if vision_config is None:
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vision_config = {}
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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 = {}
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logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
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self.vision_config = InternVisionConfig(**vision_config)
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if llm_config['architectures'][0] == 'LlamaForCausalLM':
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self.llm_config = LlamaConfig(**llm_config)
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elif llm_config['architectures'][0] == 'InternLM2ForCausalLM':
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self.llm_config = InternLM2Config(**llm_config)
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else:
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raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0]))
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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 # pixel shuffle 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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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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