# MIT License # Copyright (c) 2025 IPEC at Shanghai AI Laboratory # Permission is hereby granted, free of charge, to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND. # Based on code licensed under the Apache License, Version 2.0 by Google Inc. and HuggingFace Inc. team (Copyright 2024). # coding=utf-8 """PaliGemmamodel configuration""" import warnings from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from transformers import CONFIG_MAPPING, AutoConfig logger = logging.get_logger(__name__) class SpatialVLAConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PaliGemmaForConditionalGeneration`]. It is used to instantiate an PaliGemmamodel according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the PaliGemma-2B. e.g. [paligemma-hf/paligemma-2b](https://huggingface.co/paligemma-hf/paligemma-2b) 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 (`PaliGemmaVisionConfig`, *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 256000): The image token index to encode the image prompt. vocab_size (`int`, *optional*, defaults to 257152): Vocabulary size of the PaliGemmamodel. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`~PaliGemmaForConditionalGeneration`] projection_dim (`int`, *optional*, defaults to 2048): Dimension of the multimodal projection space. hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden layer of the Language model. Example: ```python >>> from transformers import PaliGemmaForConditionalGeneration, PaliGemmaConfig, SiglipVisionConfig, GemmaConfig >>> # Initializing a Siglip-like vision config >>> vision_config = SiglipVisionConfig() >>> # Initializing a PaliGemma config >>> text_config = GemmaConfig() >>> # Initializing a PaliGemma paligemma-3b-224 style configuration >>> configuration = PaliGemmaConfig(vision_config, text_config) >>> # Initializing a model from the paligemma-3b-224 style configuration >>> model = PaliGemmaForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "spatialvla" sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig, "vision_zoe_config": AutoConfig} def __init__( self, vision_config=None, text_config=None, ignore_index=-100, image_token_index=256000, vocab_size=257152, projection_dim=2048, hidden_size=2048, vision_zoe_config=None, action_token_begin_idx=None, spatial_token_num=259, use_spatial_token=False, ego3d_patch_reso=4, n_freqs=8, use_vision_zoe=True, # wrap_lora=False, **kwargs, ): self._ignore_index = ignore_index self.image_token_index = image_token_index self._vocab_size = vocab_size self.projection_dim = projection_dim self.hidden_size = hidden_size self.vision_config = vision_config self.is_encoder_decoder = False if isinstance(self.vision_config, dict): vision_config["model_type"] = ( vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model" ) self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) elif vision_config is None: self.vision_config = CONFIG_MAPPING["siglip_vision_model"]( intermediate_size=4096, hidden_size=1152, patch_size=14, image_size=224, num_hidden_layers=27, num_attention_heads=16, vocab_size=257152, vision_use_head=False, ) 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 "gemma2" self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) elif text_config is None: self.text_config = CONFIG_MAPPING["gemma2"]( hidden_size=2048, num_hidden_layers=18, intermediate_size=16384, num_attention_heads=8, num_key_value_heads=1, is_encoder_decoder=False, vocab_size=vocab_size, ) self.text_config.num_image_tokens = (self.vision_config.image_size // self.vision_config.patch_size) ** 2 self.vision_config.projection_dim = projection_dim # vision zoe config self.vision_zoe_config = vision_zoe_config if isinstance(self.vision_zoe_config, dict): vision_zoe_config["model_type"] = vision_zoe_config["model_type"] if "model_type" in vision_zoe_config else "zoedepth" self.vision_zoe_config = CONFIG_MAPPING[vision_zoe_config["model_type"]](**vision_zoe_config) else: print(f"🔥 init from default configurations ... {self.vision_zoe_config}") # BUG: initializing zoe in default cause key error # self.vision_zoe_config = CONFIG_MAPPING["zoedepth"]() pass # NOTE: additional attributes self.action_token_begin_idx = action_token_begin_idx self.spatial_token_num = spatial_token_num self.use_spatial_token = use_spatial_token self.ego3d_patch_reso = ego3d_patch_reso self.n_freqs = n_freqs self.use_vision_zoe = use_vision_zoe # self.wrap_lora = wrap_lora super().__init__(**kwargs) @property def ignore_index(self): warnings.warn( "The `ignore_index` attribute is deprecated and will be removed in v4.47.", FutureWarning, ) return self._ignore_index @ignore_index.setter def ignore_index(self, value): self._ignore_index = value def to_dict(self): output = super().to_dict() output.pop("_ignore_index", None) return output