# coding=utf-8 # Copyright 2022 WenXiang ZhongzhiCheng LedellWu LiuGuang BoWenZhang and 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. """AltCLIP model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class AltCLIPTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`AltCLIPTextModel`]. It is used to instantiate a AltCLIP text 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 AltCLIP [BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) architecture. 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 250002): Vocabulary size of the AltCLIP model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`AltCLIPTextModel`]. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): 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. intermediate_size (`int`, *optional*, defaults to 4096): 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 514): 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 1): The vocabulary size of the `token_type_ids` passed when calling [`AltCLIPTextModel`] initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 0.02): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 1): The id of the *padding* token. bos_token_id (`int`, *optional*, defaults to 0): The id of the *beginning-of-sequence* token. eos_token_id (`Union[int, List[int]]`, *optional*, defaults to 2): The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens. 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`. project_dim (`int`, *optional*, defaults to 768): The dimensions of the teacher model before the mapping layer. Examples: ```python >>> from transformers import AltCLIPTextModel, AltCLIPTextConfig >>> # Initializing a AltCLIPTextConfig with BAAI/AltCLIP style configuration >>> configuration = AltCLIPTextConfig() >>> # Initializing a AltCLIPTextModel (with random weights) from the BAAI/AltCLIP style configuration >>> model = AltCLIPTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "altclip_text_model" def __init__( self, vocab_size=250002, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, intermediate_size=4096, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=514, type_vocab_size=1, initializer_range=0.02, initializer_factor=0.02, layer_norm_eps=1e-05, pad_token_id=1, bos_token_id=0, eos_token_id=2, position_embedding_type="absolute", use_cache=True, project_dim=768, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **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.initializer_factor = initializer_factor self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.use_cache = use_cache self.project_dim = project_dim class AltCLIPVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an AltCLIP 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 AltCLIP [BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. 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. num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): 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-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import AltCLIPVisionConfig, AltCLIPVisionModel >>> # Initializing a AltCLIPVisionConfig with BAAI/AltCLIP style configuration >>> configuration = AltCLIPVisionConfig() >>> # Initializing a AltCLIPVisionModel (with random weights) from the BAAI/AltCLIP style configuration >>> model = AltCLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "altclip_vision_model" base_config_key = "vision_config" def __init__( self, hidden_size=768, intermediate_size=3072, projection_dim=512, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=224, patch_size=32, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.projection_dim = projection_dim 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.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act class AltCLIPConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an AltCLIP 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 AltCLIP [BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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 [`AltCLIPTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`AltCLIPVisionConfig`]. projection_dim (`int`, *optional*, defaults to 768): Dimensionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The initial value of the *logit_scale* parameter. Default is used as per the original CLIP implementation. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import AltCLIPConfig, AltCLIPModel >>> # Initializing a AltCLIPConfig with BAAI/AltCLIP style configuration >>> configuration = AltCLIPConfig() >>> # Initializing a AltCLIPModel (with random weights) from the BAAI/AltCLIP style configuration >>> model = AltCLIPModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a AltCLIPConfig from a AltCLIPTextConfig and a AltCLIPVisionConfig >>> # Initializing a AltCLIPText and AltCLIPVision configuration >>> config_text = AltCLIPTextConfig() >>> config_vision = AltCLIPVisionConfig() >>> config = AltCLIPConfig.from_text_vision_configs(config_text, config_vision) ```""" model_type = "altclip" sub_configs = {"text_config": AltCLIPTextConfig, "vision_config": AltCLIPVisionConfig} def __init__( self, text_config=None, vision_config=None, projection_dim=768, logit_scale_init_value=2.6592, **kwargs ): # If `_config_dict` exist, we use them for the backward compatibility. # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot # of confusion!). text_config_dict = kwargs.pop("text_config_dict", None) vision_config_dict = kwargs.pop("vision_config_dict", None) super().__init__(**kwargs) # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`. if text_config_dict is not None: if text_config is None: text_config = {} # This is the complete result when using `text_config_dict`. _text_config_dict = AltCLIPTextConfig(**text_config_dict).to_dict() # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different. for key, value in _text_config_dict.items(): if key in text_config and value != text_config[key] and key not in ["transformers_version"]: # If specified in `text_config_dict` if key in text_config_dict: message = ( f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. " f'The value `text_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The " f'value `text_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `text_config` with the ones in `_text_config_dict`. text_config.update(_text_config_dict) if vision_config_dict is not None: if vision_config is None: vision_config = {} # This is the complete result when using `vision_config_dict`. _vision_config_dict = AltCLIPVisionConfig(**vision_config_dict).to_dict() # convert keys to string instead of integer if "id2label" in _vision_config_dict: _vision_config_dict["id2label"] = { str(key): value for key, value in _vision_config_dict["id2label"].items() } # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different. for key, value in _vision_config_dict.items(): if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]: # If specified in `vision_config_dict` if key in vision_config_dict: message = ( f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different " f'values. The value `vision_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. " f'The value `vision_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `vision_config` with the ones in `_vision_config_dict`. vision_config.update(_vision_config_dict) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.") self.text_config = AltCLIPTextConfig(**text_config) self.vision_config = AltCLIPVisionConfig(**vision_config) self.projection_dim = projection_dim self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = 1.0 @classmethod def from_text_vision_configs(cls, text_config: AltCLIPTextConfig, vision_config: AltCLIPVisionConfig, **kwargs): r""" Instantiate a [`AltCLIPConfig`] (or a derived class) from altclip text model configuration and altclip vision model configuration. Returns: [`AltCLIPConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) __all__ = ["AltCLIPTextConfig", "AltCLIPVisionConfig", "AltCLIPConfig"]