196 lines
9.2 KiB
Python
196 lines
9.2 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/smolvlm/modular_smolvlm.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_smolvlm.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 the HuggingFace Inc. team. All rights reserved.
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# Written by Orr Zohar
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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 ...utils import logging
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from ..auto import CONFIG_MAPPING, AutoConfig
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logger = logging.get_logger(__name__)
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class SmolVLMVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SmolVLMVisionModel`]. It is used to instantiate a
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SmolVLM vision encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint
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[google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) used in SmolVLM
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[HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct).
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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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Args:
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hidden_size (`int`, *optional*, defaults to 1152):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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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 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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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 32):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization 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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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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Example:
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```python
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>>> from transformers.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer
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>>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig
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>>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration
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>>> configuration = SmolVLMVisionConfig()
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>>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration
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>>> model = SmolVLMVisionTransformer(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "smolvlm_vision"
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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=1152,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=16,
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num_channels=3,
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image_size=224,
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patch_size=32,
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hidden_act="gelu_pytorch_tanh",
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layer_norm_eps=1e-6,
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attention_dropout=0.0,
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initializer_range=0.02,
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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.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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class SmolVLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a
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SmolVLM model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the model of the SmolVLM
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[HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture.
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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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Args:
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should cache the key/value pairs of the attention mechanism. Only
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relevant if `config.is_decoder=True`.
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image_token_id (`int`, *optional*, defaults to 128257):
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The id of the "image" token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether or not to tie the word embeddings with the token embeddings.
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vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`):
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Custom vision config or dict for the vision tower
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text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`):
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Custom text config or dict for the text model
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scale_factor (`int`, *optional*, defaults to 2):
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The scale factor for the image encoder.
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pad_token_id (`int`, *optional*, defaults to 128002):
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The id of the padding token.
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Example:
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```python
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>>> from transformers import SmolVLMModel, SmolVLMConfig
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>>> # Initializing configuration
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>>> configuration = SmolVLMConfig()
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>>> # Initializing a model from the configuration
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>>> model = SmolVLMModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "smolvlm"
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sub_configs = {"text_config": AutoConfig, "vision_config": SmolVLMVisionConfig}
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def __init__(
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self,
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use_cache=True,
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image_token_id=128257,
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tie_word_embeddings=False,
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vision_config=None,
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text_config=None,
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scale_factor=2,
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pad_token_id=128_002,
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**kwargs,
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):
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self.image_token_id = image_token_id
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self.use_cache = use_cache
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self.tie_word_embeddings = tie_word_embeddings
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if vision_config is None:
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self.vision_config = SmolVLMVisionConfig()
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logger.info("vision_config is None, using default vision config")
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elif isinstance(vision_config, dict):
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self.vision_config = SmolVLMVisionConfig(**vision_config)
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elif isinstance(vision_config, SmolVLMVisionConfig):
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self.vision_config = vision_config
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if isinstance(text_config, dict):
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text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
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text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
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elif text_config is None:
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logger.info("text_config is None, using default text config")
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text_config = CONFIG_MAPPING["llama"](
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rms_norm_eps=1e-5,
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pad_token_id=pad_token_id,
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tie_word_embeddings=False,
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)
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self.text_config = text_config
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self.scale_factor = scale_factor
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super().__init__(**kwargs, pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings)
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__all__ = ["SmolVLMVisionConfig", "SmolVLMConfig"]
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