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configuration_align.py
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# 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"""
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
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.
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`.
pad_token_id (`int`, *optional*, defaults to 0)
Padding token id.
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"
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
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type") == "align":
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 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.
hiddem_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"
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
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type") == "align":
config_dict = config_dict["vision_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 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):
Dimentionality of text and vision projection layers.
temperature_init_value (`float`, *optional*, defaults to 1.0):
The inital value of the *temperature* paramter. 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"
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)