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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
@classmethod
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
@classmethod
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)
@classmethod
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)