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186 lines
8.2 KiB
186 lines
8.2 KiB
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PreTrainedConfig
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from ..qwen2.configuration_qwen2 import Qwen2Config
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class Ovis2VisionConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Ovis2VisionModel`]. It is used to instantiate a
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Ovis2VisionModel model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of Ovis2.
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Args:
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 2816):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input images.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the RMSNorm layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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qkv_bias (`bool`, *optional*, defaults to `False`):
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Whether to add a learnable bias to the query, key, and value sequences at each attention head.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to add a learnable bias to the MLP layers.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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vocab_size (`int`, *optional*, defaults to 16384):
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Vocabulary size of the Vision Transformer.
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hidden_stride (`int`, *optional*, defaults to 1):
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The stride of the hidden layer in the Vision Transformer.
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num_visual_indicator_tokens (`int`, *optional*, defaults to 5):
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Number of visual indicator tokens.
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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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tokenize_function (`str`, *optional*, defaults to `"softmax"`):
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The function used to tokenize the visual indicator tokens.
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"""
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base_config_key = "vision_config"
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def __init__(
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self,
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hidden_size: int = 1024,
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intermediate_size: int = 2816,
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num_hidden_layers: int = 24,
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num_attention_heads: int = 8,
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num_channels: int = 3,
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image_size: int = 224,
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patch_size: int = 14,
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rms_norm_eps: float = 1e-5,
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attention_dropout: float = 0.0,
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qkv_bias: bool = False,
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mlp_bias: bool = False,
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hidden_act="silu",
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vocab_size=16384,
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hidden_stride=1,
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num_visual_indicator_tokens=5,
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initializer_range=0.02,
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tokenize_function="softmax",
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.attention_dropout = attention_dropout
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self.hidden_act = hidden_act
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self.qkv_bias = qkv_bias
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self.mlp_bias = mlp_bias
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self.rms_norm_eps = rms_norm_eps
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self.vocab_size = vocab_size
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self.hidden_stride = hidden_stride
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self.num_visual_indicator_tokens = num_visual_indicator_tokens
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self.tokenize_function = tokenize_function
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self.initializer_range = initializer_range
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class Ovis2Config(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Ovis2ForConditionalGeneration`]. It is used to instantiate a
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Ovis2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of Ovis2.
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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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e.g. [thisisiron/Ovis2-1B-hf](https://huggingface.co/thisisiron/Ovis2-1B-hf)
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Args:
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vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Ovis2VisionConfig`):
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The config object or dictionary of the vision backbone.
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text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`):
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The config object or dictionary of the text backbone.
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image_token_id (`int`, *optional*, defaults to 151665):
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The image token id to encode the image prompt.
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visual_indicator_token_ids (`List[int]`, *optional*, defaults to `[151666, 151667, 151668, 151669, 151670]`):
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The visual indicator token ids to encode the image prompt.
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vocab_size (`int`, *optional*, defaults to 151643):
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Vocabulary size of the text model.
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hidden_size (`int`, *optional*, defaults to 1536):
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Dimensionality of the encoder layers and the pooler layer.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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```python
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>>> from transformers import Ovis2ForConditionalGeneration, Ovis2Config
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>>> # Initializing a Ovis2 style configuration
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>>> configuration = Ovis2Config()
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>>> # Initializing a model from the Ovis2-2B style configuration
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>>> model = Ovis2ForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "ovis2"
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sub_configs = {"text_config": Qwen2Config, "vision_config": Ovis2VisionConfig}
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def __init__(
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self,
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vision_config=None,
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text_config=None,
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image_token_id=151665,
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visual_indicator_token_ids=[151666, 151667, 151668, 151669, 151670],
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vocab_size=151643,
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hidden_size=1536,
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tie_word_embeddings=True,
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**kwargs,
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):
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if isinstance(vision_config, dict):
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self.vision_config = Ovis2VisionConfig(**vision_config)
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elif isinstance(vision_config, Ovis2VisionConfig):
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self.vision_config = vision_config
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if vision_config is None:
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self.vision_config = Ovis2VisionConfig(num_visual_indicator_tokens=len(visual_indicator_token_ids))
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if isinstance(text_config, dict):
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self.text_config = Qwen2Config(**text_config)
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elif isinstance(text_config, Qwen2Config):
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self.text_config = text_config
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elif text_config is None:
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self.text_config = Qwen2Config()
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.image_token_id = image_token_id
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self.visual_indicator_token_ids = visual_indicator_token_ids
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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(**kwargs)
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__all__ = ["Ovis2VisionConfig", "Ovis2Config"]
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