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