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import copy |
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from transformers import 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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logger = logging.get_logger(__name__) |
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class InternVLConfig(PretrainedConfig): |
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r""" |
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[`InternVLConfig`] is the configuration class to store the configuration of a |
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[`InternVLModel`]. It is used to instantiate a InternVLModel according to the specified |
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arguments, defining the InternViT-6B and QLLaMA configs. Instantiating a configuration with |
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the defaults will yield a similar configuration to that of the InternVL architecture. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vision_config (`dict`, *optional*): |
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Dictionary of configuration options used to initialize [`InternVisionConfig`]. |
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qllama_config (`dict`, *optional*): |
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Dictionary of configuration options used to initialize [`LLaMAConfig`]. |
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clip_embed_dim (`int`, *optional*, defaults to 768): |
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Size of the embeddings from the CLIP model. |
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attn_pool_num_heads (`int`, *optional*, defaults to 16): |
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Number of attention heads used in the attention pooling layers. |
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num_query_token (`int`, *optional*, defaults to 96): |
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Number of query tokens used in the transformer. |
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label_smoothing (`float`, *optional*, defaults to 0.0): |
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The amount of label smoothing to apply. |
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cross_attention_frequency (`int`, *optional*, defaults to 2): |
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The frequency of cross-attention layers in the model. |
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use_backbone_lora (`int`, *optional*, defaults to 0): |
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If non-zero, indicates the use of LoRA in the backbone of the model. |
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use_qllama_lora (`int`, *optional*, defaults to 0): |
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If non-zero, indicates the use of LoRA in the QLLaMA of the model. |
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force_image_size (`int` or `None`, *optional*): |
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If not None, forces the model to use this specific image size. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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kwargs (*optional*): |
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Dictionary of additional keyword arguments. |
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""" |
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model_type = 'internvl' |
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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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qllama_config=None, |
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clip_embed_dim=768, |
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attn_pool_num_heads=16, |
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num_query_token=96, |
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label_smoothing=0.0, |
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cross_attention_frequency=2, |
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use_backbone_lora=0, |
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use_qllama_lora=0, |
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force_image_size=None, |
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initializer_range=0.02, |
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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 qllama_config is None: |
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qllama_config = {} |
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logger.info( |
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'qllama_config is None. Initializing the InternTextConfig config with default values (`LlamaConfig`).') |
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self.vision_config = InternVisionConfig(**vision_config) |
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self.qllama_config = LlamaConfig(**qllama_config) |
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self.qllama_config.num_query_token = num_query_token |
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self.qllama_config.cross_attention_frequency = cross_attention_frequency |
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self.hidden_size = self.qllama_config.hidden_size |
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self.clip_embed_dim = clip_embed_dim |
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self.attn_pool_num_heads = attn_pool_num_heads |
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self.num_query_token = num_query_token |
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self.label_smoothing = label_smoothing |
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self.use_backbone_lora = use_backbone_lora |
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self.use_qllama_lora = use_qllama_lora |
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self.force_image_size = force_image_size |
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self.initializer_range = initializer_range |
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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['qllama_config'] = self.qllama_config.to_dict() |
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output['model_type'] = self.__class__.model_type |
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return output |
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