350 lines
16 KiB
Python
350 lines
16 KiB
Python
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# coding=utf-8
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# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and 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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""" BridgeTower model configuration"""
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import os
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from typing import Union
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class BridgeTowerVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the vision configuration of a [`BridgeTowerModel`]. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the bridgetower-base
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[BridgeTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-base/) 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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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in visual encoder model.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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image_size (`int`, *optional*, defaults to 288):
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The size (resolution) of each image.
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initializer_factor (`float`, *optional*, defaults to 1):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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stop_gradient (`bool`, *optional*, defaults to `False`):
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Whether to stop gradient for training.
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share_layernorm (`bool`, *optional*, defaults to `True`):
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Whether LayerNorm layers are shared.
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remove_last_layer (`bool`, *optional*, defaults to `False`):
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Whether to remove the last layer from the vision encoder.
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Example:
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```python
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>>> from transformers import BridgeTowerVisionConfig
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>>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration for the vision model
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>>> configuration = BridgeTowerVisionConfig()
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>>> # Accessing the configuration
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>>> configuration
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```"""
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model_type = "bridgetower_vision_model"
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def __init__(
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self,
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hidden_size=768,
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num_hidden_layers=12,
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num_channels=3,
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patch_size=16,
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image_size=288,
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initializer_factor=1,
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layer_norm_eps=1e-05,
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stop_gradient=False,
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share_layernorm=True,
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remove_last_layer=False,
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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.num_hidden_layers = num_hidden_layers
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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.initializer_factor = initializer_factor
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self.layer_norm_eps = layer_norm_eps
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self.stop_gradient = stop_gradient
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self.share_layernorm = share_layernorm
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self.remove_last_layer = remove_last_layer
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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if config_dict.get("model_type") == "bridgetower":
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config_dict = config_dict["text_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class BridgeTowerTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the text configuration of a [`BridgeTowerModel`]. The default values here
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are copied from RoBERTa. Instantiating a configuration with the defaults will yield a similar configuration to that
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of the bridgetower-base [BridegTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-base/)
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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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vocab_size (`int`, *optional*, defaults to 50265):
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Vocabulary size of the text part of the model. Defines the number of different tokens that can be
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represented by the `inputs_ids` passed when calling [`BridgeTowerModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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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 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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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"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 514):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids`.
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initializer_factor (`float`, *optional*, defaults to 1):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
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positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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is_decoder (`bool`, *optional*, defaults to `False`):
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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Example:
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```python
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>>> from transformers import BridgeTowerTextConfig
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>>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration for the text model
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>>> configuration = BridgeTowerTextConfig()
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>>> # Accessing the configuration
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>>> configuration
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```"""
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model_type = "bridgetower_text_model"
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def __init__(
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self,
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vocab_size=50265,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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initializer_factor=1,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=514,
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type_vocab_size=1,
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layer_norm_eps=1e-05,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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position_embedding_type="absolute",
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use_cache=True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_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.hidden_act = hidden_act
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self.initializer_factor = initializer_factor
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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if config_dict.get("model_type") == "bridgetower":
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config_dict = config_dict["text_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class BridgeTowerConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BridgeTowerModel`]. It is used to instantiate a
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BridgeTower 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 bridgetower-base
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[BridgeTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-base/) 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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share_cross_modal_transformer_layers (`bool`, *optional*, defaults to `True`):
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Whether cross modal transformer layers are shared.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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initializer_factor (`float`, *optional*, defaults to 1):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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share_link_tower_layers (`bool`, *optional*, defaults to `False`):
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Whether the bride/link tower layers are shared.
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link_tower_type (`str`, *optional*, defaults to `"add"`):
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Type of the bridge/link layer.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer encoder.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie input and output embeddings.
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init_layernorm_from_vision_encoder (`bool`, *optional*, defaults to `False`):
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Whether to init LayerNorm from the vision encoder.
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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`BridgeTowerTextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`BridgeTowerVisionConfig`].
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Example:
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```python
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>>> from transformers import BridgeTowerModel, BridgeTowerConfig
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>>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration
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>>> configuration = BridgeTowerConfig()
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>>> # Initializing a model from the BridgeTower/bridgetower-base style configuration
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>>> model = BridgeTowerModel(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 = "bridgetower"
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def __init__(
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self,
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share_cross_modal_transformer_layers=True,
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hidden_act="gelu",
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hidden_size=768,
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initializer_factor=1,
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layer_norm_eps=1e-05,
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share_link_tower_layers=False,
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link_tower_type="add",
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num_attention_heads=12,
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num_hidden_layers=6,
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tie_word_embeddings=False,
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init_layernorm_from_vision_encoder=False,
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text_config=None,
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vision_config=None,
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**kwargs,
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):
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# TODO: remove this once the Hub files are updated.
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_ = kwargs.pop("text_config_dict", None)
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_ = kwargs.pop("vision_config_dict", None)
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super().__init__(**kwargs)
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self.share_cross_modal_transformer_layers = share_cross_modal_transformer_layers
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self.hidden_act = hidden_act
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self.hidden_size = hidden_size
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self.initializer_factor = initializer_factor
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self.layer_norm_eps = layer_norm_eps
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self.share_link_tower_layers = share_link_tower_layers
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self.link_tower_type = link_tower_type
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self.num_attention_heads = num_attention_heads
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self.num_hidden_layers = num_hidden_layers
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self.tie_word_embeddings = tie_word_embeddings
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self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder
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if text_config is None:
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text_config = {}
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logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.")
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if vision_config is None:
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vision_config = {}
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logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.")
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self.text_config = BridgeTowerTextConfig(**text_config)
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self.vision_config = BridgeTowerVisionConfig(**vision_config)
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@classmethod
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def from_text_vision_configs(
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cls, text_config: BridgeTowerTextConfig, vision_config: BridgeTowerVisionConfig, **kwargs
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):
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r"""
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|
Instantiate a [`BridgeTowerConfig`] (or a derived class) from BridgeTower text model configuration. Returns:
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|
|
[`BridgeTowerConfig`]: An instance of a configuration object
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"""
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||
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return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
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