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| # coding=utf-8 | |
| # Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and 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. | |
| """ BridgeTower model configuration""" | |
| import os | |
| from typing import Union | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json", | |
| "BridgeTower/bridgetower-base-itm-mlm": ( | |
| "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json" | |
| ), | |
| } | |
| class BridgeTowerVisionConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the vision configuration of a [`BridgeTowerModel`]. Instantiating a | |
| configuration with the defaults will yield a similar configuration to that of the bridgetower-base | |
| [BridgeTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-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: | |
| 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 visual encoder model. | |
| patch_size (`int`, *optional*, defaults to 16): | |
| The size (resolution) of each patch. | |
| image_size (`int`, *optional*, defaults to 288): | |
| The size (resolution) of each image. | |
| initializer_factor (`float``, *optional*, defaults to 1): | |
| 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. | |
| stop_gradient (`bool`, *optional*, defaults to `False`): | |
| Whether to stop gradient for training. | |
| share_layernorm (`bool`, *optional*, defaults to `True`): | |
| Whether LayerNorm layers are shared. | |
| remove_last_layer (`bool`, *optional*, defaults to `False`): | |
| Whether to remove the last layer from the vision encoder. | |
| Example: | |
| ```python | |
| >>> from transformers import BridgeTowerVisionConfig | |
| >>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration for the vision model | |
| >>> configuration = BridgeTowerVisionConfig() | |
| >>> # Accessing the configuration | |
| >>> configuration | |
| ```""" | |
| model_type = "bridgetower_vision_model" | |
| def __init__( | |
| self, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_channels=3, | |
| patch_size=16, | |
| image_size=288, | |
| initializer_factor=1, | |
| layer_norm_eps=1e-05, | |
| stop_gradient=False, | |
| share_layernorm=True, | |
| remove_last_layer=False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_channels = num_channels | |
| self.patch_size = patch_size | |
| self.image_size = image_size | |
| self.initializer_factor = initializer_factor | |
| self.layer_norm_eps = layer_norm_eps | |
| self.stop_gradient = stop_gradient | |
| self.share_layernorm = share_layernorm | |
| self.remove_last_layer = remove_last_layer | |
| def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": | |
| config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) | |
| if config_dict.get("model_type") == "bridgetower": | |
| config_dict = config_dict["text_config"] | |
| if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: | |
| logger.warning( | |
| f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " | |
| f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." | |
| ) | |
| return cls.from_dict(config_dict, **kwargs) | |
| class BridgeTowerTextConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the text configuration of a [`BridgeTowerModel`]. The default values here | |
| are copied from RoBERTa. Instantiating a configuration with the defaults will yield a similar configuration to that | |
| of the bridgetower-base [BridegTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-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: | |
| vocab_size (`int`, *optional*, defaults to 50265): | |
| Vocabulary size of the text part of the model. Defines the number of different tokens that can be | |
| represented by the `inputs_ids` passed when calling [`BridgeTowerModel`]. | |
| 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 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 2): | |
| The vocabulary size of the `token_type_ids`. | |
| initializer_factor (`float``, *optional*, defaults to 1): | |
| 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. | |
| 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). | |
| is_decoder (`bool`, *optional*, defaults to `False`): | |
| Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. | |
| 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 BridgeTowerTextConfig | |
| >>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration for the text model | |
| >>> configuration = BridgeTowerTextConfig() | |
| >>> # Accessing the configuration | |
| >>> configuration | |
| ```""" | |
| model_type = "bridgetower_text_model" | |
| def __init__( | |
| self, | |
| vocab_size=50265, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| initializer_factor=1, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=514, | |
| type_vocab_size=1, | |
| layer_norm_eps=1e-05, | |
| pad_token_id=1, | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| 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.initializer_factor = initializer_factor | |
| 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.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 | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": | |
| config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) | |
| if config_dict.get("model_type") == "bridgetower": | |
| config_dict = config_dict["text_config"] | |
| if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: | |
| logger.warning( | |
| f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " | |
| f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." | |
| ) | |
| return cls.from_dict(config_dict, **kwargs) | |
| class BridgeTowerConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`BridgeTowerModel`]. It is used to instantiate a | |
| BridgeTower 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 bridgetower-base | |
| [BridgeTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-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: | |
| share_cross_modal_transformer_layers (`bool`, *optional*, defaults to `True`): | |
| Whether cross modal transformer layers are shared. | |
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| initializer_factor (`float``, *optional*, defaults to 1): | |
| 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. | |
| share_link_tower_layers (`bool`, *optional*, defaults to `False`): | |
| Whether the bride/link tower layers are shared. | |
| link_tower_type (`str`, *optional*, defaults to `"add"`): | |
| Type of the bridge/link layer. | |
| num_attention_heads (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| num_hidden_layers (`int`, *optional*, defaults to 6): | |
| Number of hidden layers in the Transformer encoder. | |
| tie_word_embeddings (`bool`, *optional*, defaults to `False`): | |
| Whether to tie input and output embeddings. | |
| init_layernorm_from_vision_encoder (`bool`, *optional*, defaults to `False`): | |
| Whether to init LayerNorm from the vision encoder. | |
| text_config (`dict`, *optional*): | |
| Dictionary of configuration options used to initialize [`BridgeTowerTextConfig`]. | |
| vision_config (`dict`, *optional*): | |
| Dictionary of configuration options used to initialize [`BridgeTowerVisionConfig`]. | |
| Example: | |
| ```python | |
| >>> from transformers import BridgeTowerModel, BridgeTowerConfig | |
| >>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration | |
| >>> configuration = BridgeTowerConfig() | |
| >>> # Initializing a model from the BridgeTower/bridgetower-base style configuration | |
| >>> model = BridgeTowerModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "bridgetower" | |
| def __init__( | |
| self, | |
| share_cross_modal_transformer_layers=True, | |
| hidden_act="gelu", | |
| hidden_size=768, | |
| initializer_factor=1, | |
| layer_norm_eps=1e-05, | |
| share_link_tower_layers=False, | |
| link_tower_type="add", | |
| num_attention_heads=12, | |
| num_hidden_layers=6, | |
| tie_word_embeddings=False, | |
| init_layernorm_from_vision_encoder=False, | |
| text_config=None, | |
| vision_config=None, | |
| **kwargs, | |
| ): | |
| # TODO: remove this once the Hub files are updated. | |
| _ = kwargs.pop("text_config_dict", None) | |
| _ = kwargs.pop("vision_config_dict", None) | |
| super().__init__(**kwargs) | |
| self.share_cross_modal_transformer_layers = share_cross_modal_transformer_layers | |
| self.hidden_act = hidden_act | |
| self.hidden_size = hidden_size | |
| self.initializer_factor = initializer_factor | |
| self.layer_norm_eps = layer_norm_eps | |
| self.share_link_tower_layers = share_link_tower_layers | |
| self.link_tower_type = link_tower_type | |
| self.num_attention_heads = num_attention_heads | |
| self.num_hidden_layers = num_hidden_layers | |
| self.tie_word_embeddings = tie_word_embeddings | |
| self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder | |
| if text_config is None: | |
| text_config = {} | |
| logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.") | |
| if vision_config is None: | |
| vision_config = {} | |
| logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.") | |
| self.text_config = BridgeTowerTextConfig(**text_config) | |
| self.vision_config = BridgeTowerVisionConfig(**vision_config) | |
| def from_text_vision_configs( | |
| cls, text_config: BridgeTowerTextConfig, vision_config: BridgeTowerVisionConfig, **kwargs | |
| ): | |
| r""" | |
| Instantiate a [`BridgeTowerConfig`] (or a derived class) from BridgeTower text model configuration. Returns: | |
| [`BridgeTowerConfig`]: An instance of a configuration object | |
| """ | |
| return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) | |