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from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from transformers.models.auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
class GeckoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`]. It is used to instantiate an
Llava model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Llava-9B.
e.g. [llava-hf/llava-9b](https://huggingface.co/llava-hf/llava-9b)
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 (`LlavaVisionConfig`, *optional*):
Custom vision config or dict
text_config (`Union[AutoConfig, dict]`, *optional*):
The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
image_token_index (`int`, *optional*, defaults to 32000):
The image token index to encode the image prompt.
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The activation function used by the multimodal projector.
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
The feature selection strategy used to select the vision feature from the CLIP backbone.
vision_feature_layer (`int`, *optional*, defaults to -2):
The index of the layer to select the vision feature.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Llava model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~LlavaForConditionalGeneration`]
"""
model_type = "gecko"
is_composition = False
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=32000,
projector_hidden_act="gelu",
vision_feature_select_strategy="cls",
patch_picking_strategy="across_layers",
vision_feature_layer=-2,
vocab_size=32000,
topk=4,
keyword_criteria="template",
positional_information="explicit",
visualize_patches=False,
visualize_topk_patches=False,
print_keyword=False,
print_topk_patches=False,
**kwargs,
):
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.vision_feature_layer = vision_feature_layer
self.vision_feature_select_strategy = vision_feature_select_strategy
self.patch_picking_strategy = patch_picking_strategy
self.vocab_size = vocab_size
self.topk = topk
self.vision_config = vision_config
self.text_config = text_config
self.keyword_criteria = keyword_criteria
self.positional_information = positional_information
self.visualize_patches = visualize_patches
self.visualize_topk_patches = visualize_topk_patches
self.print_keyword = print_keyword
self.print_topk_patches = print_topk_patches
if isinstance(self.vision_config, dict):
vision_config["model_type"] = (
vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
)
self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is None:
self.vision_config = CONFIG_MAPPING["clip_vision_model"](
intermediate_size=4096,
hidden_size=1024,
patch_size=14,
image_size=336,
num_hidden_layers=24,
num_attention_heads=16,
vocab_size=32000,
projection_dim=768,
)
self.vocab_size = self.vocab_size
self.text_config = text_config
if isinstance(self.text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
self.vocab_size = self.text_config.vocab_size
elif text_config is None:
self.text_config = CONFIG_MAPPING["llama"]()
super().__init__(**kwargs) |