# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_paddleocr_vl.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters class PaddleOCRVisionConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`PaddleOCRVisionModel`]. It is used to instantiate a PaddleOCRVL vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the PaddleOCRVL [PaddlePaddle/PaddleOCRVL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL) architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1152): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 4304): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 27): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input images. image_size (`int`, *optional*, defaults to 384): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. spatial_merge_size (`int`, *optional*, defaults to 2): The size used for merging spatial dimensions. Example: ```python >>> from transformers import PaddleOCRVisionConfig, PaddleOCRVisionModel >>> # Initializing a PaddleOCRVisionConfig with PaddlePaddle/PaddleOCR-VL style configuration >>> configuration = PaddleOCRVisionConfig() >>> # Initializing a PaddleOCRVisionModel (with random weights) from the PaddlePaddle/PaddleOCR-VL style configuration >>> model = PaddleOCRVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "paddleocr_vl_vision" base_config_key = "vision_config" def __init__( self, hidden_size=1152, intermediate_size=4304, num_hidden_layers=27, num_attention_heads=16, num_channels=3, image_size=384, patch_size=14, hidden_act="gelu_pytorch_tanh", layer_norm_eps=1e-6, attention_dropout=0.0, spatial_merge_size=2, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_channels = num_channels self.patch_size = patch_size self.image_size = image_size self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.spatial_merge_size = spatial_merge_size class PaddleOCRTextConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`PaddleOCRTextModel`]. It is used to instantiate an Ernie 4.5 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 Ernie 4.5 0.3B. e.g. [baidu/ERNIE-4.5-0.3B-PT](https://huggingface.co/baidu/ERNIE-4.5-0.3B-PT) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 103424): Vocabulary size of the Ernie 4.5 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PaddleOCRTextModel`] hidden_size (`int`, *optional*, defaults to 1024): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 18): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 2): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 131072): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. use_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in any of the projections including mlp and attention for example. head_dim (`int`, *optional*, defaults to 128): The attention head dimension. If None, it will default to hidden_size // num_attention_heads ```python >>> from transformers import PaddleOCRTextModel, PaddleOCRTextConfig >>> # Initializing a PaddleOCRText 0.3B style configuration >>> configuration = PaddleOCRTextConfig() >>> # Initializing a model from the 0.3B style configuration >>> model = PaddleOCRTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "paddleocr_vl_text" keys_to_ignore_at_inference = ["past_key_values"] default_theta = 500000.0 # Default tensor parallel plan for base model `PaddleOCRTextModel` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size: int | None = 103424, hidden_size: int | None = 1024, intermediate_size: int | None = 3072, num_hidden_layers: int | None = 18, num_attention_heads: int | None = 16, num_key_value_heads: int | None = 2, hidden_act: str | None = "silu", max_position_embeddings: int | None = 131072, initializer_range: float | None = 0.02, rms_norm_eps: int | None = 1e-05, use_cache: int | None = True, pad_token_id: int | None = 0, bos_token_id: int | None = 1, eos_token_id: int | None = 2, tie_word_embeddings: bool | None = True, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, use_bias: bool | None = False, head_dim: int | None = 128, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.use_bias = use_bias self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads self.rope_parameters = rope_parameters self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) class PaddleOCRVLConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`PaddleOCRVLForConditionalGeneration`]. It is used to instantiate a PaddleOCRVL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of PaddleOCRVL [PaddlePaddle/PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `PaddleOCRTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `PaddleOCRVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 100295): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 100296): The video token index to encode the image prompt. vision_start_token_id (`int`, *optional*, defaults to 101305): The token index to denote start of vision input. vision_end_token_id (`int`, *optional*, defaults to 101306): The token index to denote end of vision input. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether the model's input and output word embeddings should be tied. ```python >>> from transformers import PaddleOCRVLForConditionalGeneration, PaddleOCRVLConfig >>> # Initializing a PaddleOCRVL style configuration >>> configuration = PaddleOCRVLConfig() >>> # Initializing a model from the PaddleOCRVL style configuration >>> model = PaddleOCRVLForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "paddleocr_vl" sub_configs = {"vision_config": PaddleOCRVisionConfig, "text_config": PaddleOCRTextConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, image_token_id=100295, video_token_id=100296, vision_start_token_id=101305, vision_end_token_id=101306, tie_word_embeddings=True, **kwargs, ): if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() if isinstance(text_config, dict): self.text_config = self.sub_configs["text_config"](**text_config) elif text_config is None: # Hub configs are saved as flat dicts so we pop some of kwargs to init `TextConfig` text_params = inspect.signature(self.sub_configs["text_config"].__init__).parameters.keys() text_params = list(text_params) + ["rope_scaling", "rope_theta"] text_config = {key: kwargs.pop(key) for key in text_params if key in kwargs} text_config["dtype"] = kwargs.get("torch_dtype", kwargs.get("dtype")) # don't pop the dtype self.text_config = self.sub_configs["text_config"](**text_config) self.image_token_id = image_token_id self.video_token_id = video_token_id self.vision_start_token_id = vision_start_token_id self.vision_end_token_id = vision_end_token_id self.tie_word_embeddings = tie_word_embeddings super().__init__(**kwargs) __all__ = ["PaddleOCRVLConfig", "PaddleOCRVisionConfig", "PaddleOCRTextConfig"]