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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. | |
pad_token_id (`int`, *optional*, defaults to 0): | |
Padding token id. | |
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`. | |
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 | |
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 | |
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 | |
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) | |