# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # 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. """ALIGN model configuration""" from typing import TYPE_CHECKING, List if TYPE_CHECKING: pass from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class AlignTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`AlignTextModel`]. It is used to instantiate a ALIGN text 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 text encoder of the ALIGN [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values here are copied from BERT. 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 30522): Vocabulary size of the Align Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`AlignTextModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`AlignTextModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. Example: ```python >>> from transformers import AlignTextConfig, AlignTextModel >>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration >>> configuration = AlignTextConfig() >>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration >>> model = AlignTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "align_text_model" base_config_key = "text_config" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, **kwargs, ): super().__init__(**kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.use_cache = use_cache self.pad_token_id = pad_token_id class AlignVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`AlignVisionModel`]. It is used to instantiate a ALIGN 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 ALIGN [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values are copied from EfficientNet (efficientnet-b7) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 600): The input image size. width_coefficient (`float`, *optional*, defaults to 2.0): Scaling coefficient for network width at each stage. depth_coefficient (`float`, *optional*, defaults to 3.1): Scaling coefficient for network depth at each stage. depth_divisor `int`, *optional*, defaults to 8): A unit of network width. kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`): List of kernel sizes to be used in each block. in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`): List of input channel sizes to be used in each block for convolutional layers. out_channels (`List[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`): List of output channel sizes to be used in each block for convolutional layers. depthwise_padding (`List[int]`, *optional*, defaults to `[]`): List of block indices with square padding. strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`): List of stride sizes to be used in each block for convolutional layers. num_block_repeats (`List[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`): List of the number of times each block is to repeated. expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`): List of scaling coefficient of each block. squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25): Squeeze expansion ratio. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`, `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported. hidden_dim (`int`, *optional*, defaults to 1280): The hidden dimension of the layer before the classification head. pooling_type (`str` or `function`, *optional*, defaults to `"mean"`): Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`, `"max"`] initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. batch_norm_eps (`float`, *optional*, defaults to 1e-3): The epsilon used by the batch normalization layers. batch_norm_momentum (`float`, *optional*, defaults to 0.99): The momentum used by the batch normalization layers. drop_connect_rate (`float`, *optional*, defaults to 0.2): The drop rate for skip connections. Example: ```python >>> from transformers import AlignVisionConfig, AlignVisionModel >>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration >>> configuration = AlignVisionConfig() >>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration >>> model = AlignVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "align_vision_model" base_config_key = "vision_config" def __init__( self, num_channels: int = 3, image_size: int = 600, width_coefficient: float = 2.0, depth_coefficient: float = 3.1, depth_divisor: int = 8, kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3], in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192], out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320], depthwise_padding: List[int] = [], strides: List[int] = [1, 2, 2, 2, 1, 2, 1], num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1], expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6], squeeze_expansion_ratio: float = 0.25, hidden_act: str = "swish", hidden_dim: int = 2560, pooling_type: str = "mean", initializer_range: float = 0.02, batch_norm_eps: float = 0.001, batch_norm_momentum: float = 0.99, drop_connect_rate: float = 0.2, **kwargs, ): super().__init__(**kwargs) self.num_channels = num_channels self.image_size = image_size self.width_coefficient = width_coefficient self.depth_coefficient = depth_coefficient self.depth_divisor = depth_divisor self.kernel_sizes = kernel_sizes self.in_channels = in_channels self.out_channels = out_channels self.depthwise_padding = depthwise_padding self.strides = strides self.num_block_repeats = num_block_repeats self.expand_ratios = expand_ratios self.squeeze_expansion_ratio = squeeze_expansion_ratio self.hidden_act = hidden_act self.hidden_dim = hidden_dim self.pooling_type = pooling_type self.initializer_range = initializer_range self.batch_norm_eps = batch_norm_eps self.batch_norm_momentum = batch_norm_momentum self.drop_connect_rate = drop_connect_rate self.num_hidden_layers = sum(num_block_repeats) * 4 class AlignConfig(PretrainedConfig): r""" [`AlignConfig`] is the configuration class to store the configuration of a [`AlignModel`]. It is used to instantiate a ALIGN model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the ALIGN [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. 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 (`dict`, *optional*): Dictionary of configuration options used to initialize [`AlignTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`AlignVisionConfig`]. projection_dim (`int`, *optional*, defaults to 640): Dimensionality of text and vision projection layers. temperature_init_value (`float`, *optional*, defaults to 1.0): The initial value of the *temperature* parameter. Default is used as per the original ALIGN implementation. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import AlignConfig, AlignModel >>> # Initializing a AlignConfig with kakaobrain/align-base style configuration >>> configuration = AlignConfig() >>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration >>> model = AlignModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig >>> from transformers import AlignTextConfig, AlignVisionConfig >>> # Initializing ALIGN Text and Vision configurations >>> config_text = AlignTextConfig() >>> config_vision = AlignVisionConfig() >>> config = AlignConfig.from_text_vision_configs(config_text, config_vision) ```""" model_type = "align" sub_configs = {"text_config": AlignTextConfig, "vision_config": AlignVisionConfig} def __init__( self, text_config=None, vision_config=None, projection_dim=640, temperature_init_value=1.0, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("text_config is None. Initializing the AlignTextConfig with default values.") if vision_config is None: vision_config = {} logger.info("vision_config is None. Initializing the AlignVisionConfig with default values.") self.text_config = AlignTextConfig(**text_config) self.vision_config = AlignVisionConfig(**vision_config) self.projection_dim = projection_dim self.temperature_init_value = temperature_init_value self.initializer_range = initializer_range @classmethod def from_text_vision_configs(cls, text_config: AlignTextConfig, vision_config: AlignVisionConfig, **kwargs): r""" Instantiate a [`AlignConfig`] (or a derived class) from align text model configuration and align vision model configuration. Returns: [`AlignConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) __all__ = ["AlignTextConfig", "AlignVisionConfig", "AlignConfig"]