384 lines
18 KiB
Python
384 lines
18 KiB
Python
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# coding=utf-8
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# Copyright 2023 The 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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""" ALIGN model configuration"""
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import os
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from typing import TYPE_CHECKING, List, Union
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if TYPE_CHECKING:
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pass
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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 ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class AlignTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`AlignTextModel`]. It is used to instantiate a
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ALIGN text encoder 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 text encoder of the ALIGN
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[kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values here are
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copied from BERT.
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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 30522):
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Vocabulary size of the Align Text model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`AlignTextModel`].
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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 512):
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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` passed when calling [`AlignTextModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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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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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 AlignTextConfig, AlignTextModel
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>>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration
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>>> configuration = AlignTextConfig()
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>>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration
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>>> model = AlignTextModel(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 = "align_text_model"
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def __init__(
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self,
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vocab_size=30522,
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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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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=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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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.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.initializer_range = initializer_range
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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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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the text config dict if we are loading from AlignConfig
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if config_dict.get("model_type") == "align":
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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 AlignVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`AlignVisionModel`]. It is used to instantiate a
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ALIGN vision encoder 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 vision encoder of the ALIGN
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[kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values are copied
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from EfficientNet (efficientnet-b7)
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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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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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image_size (`int`, *optional*, defaults to 600):
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The input image size.
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width_coefficient (`float`, *optional*, defaults to 2.0):
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Scaling coefficient for network width at each stage.
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depth_coefficient (`float`, *optional*, defaults to 3.1):
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Scaling coefficient for network depth at each stage.
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depth_divisor `int`, *optional*, defaults to 8):
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A unit of network width.
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kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
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List of kernel sizes to be used in each block.
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in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
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List of input channel sizes to be used in each block for convolutional layers.
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out_channels (`List[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`):
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List of output channel sizes to be used in each block for convolutional layers.
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depthwise_padding (`List[int]`, *optional*, defaults to `[]`):
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List of block indices with square padding.
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strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
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List of stride sizes to be used in each block for convolutional layers.
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num_block_repeats (`List[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`):
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List of the number of times each block is to repeated.
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expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
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List of scaling coefficient of each block.
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squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25):
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Squeeze expansion ratio.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
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`"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
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hiddem_dim (`int`, *optional*, defaults to 1280):
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The hidden dimension of the layer before the classification head.
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pooling_type (`str` or `function`, *optional*, defaults to `"mean"`):
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Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`,
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`"max"`]
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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batch_norm_eps (`float`, *optional*, defaults to 1e-3):
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The epsilon used by the batch normalization layers.
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batch_norm_momentum (`float`, *optional*, defaults to 0.99):
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The momentum used by the batch normalization layers.
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drop_connect_rate (`float`, *optional*, defaults to 0.2):
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The drop rate for skip connections.
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Example:
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```python
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>>> from transformers import AlignVisionConfig, AlignVisionModel
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>>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration
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>>> configuration = AlignVisionConfig()
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>>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration
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>>> model = AlignVisionModel(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 = "align_vision_model"
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def __init__(
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self,
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num_channels: int = 3,
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image_size: int = 600,
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width_coefficient: float = 2.0,
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depth_coefficient: float = 3.1,
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depth_divisor: int = 8,
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kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
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in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
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out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
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depthwise_padding: List[int] = [],
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strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
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num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
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expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
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squeeze_expansion_ratio: float = 0.25,
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hidden_act: str = "swish",
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hidden_dim: int = 2560,
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pooling_type: str = "mean",
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initializer_range: float = 0.02,
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batch_norm_eps: float = 0.001,
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batch_norm_momentum: float = 0.99,
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drop_connect_rate: float = 0.2,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.num_channels = num_channels
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self.image_size = image_size
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self.width_coefficient = width_coefficient
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self.depth_coefficient = depth_coefficient
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self.depth_divisor = depth_divisor
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self.kernel_sizes = kernel_sizes
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.depthwise_padding = depthwise_padding
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self.strides = strides
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self.num_block_repeats = num_block_repeats
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self.expand_ratios = expand_ratios
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self.squeeze_expansion_ratio = squeeze_expansion_ratio
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self.hidden_act = hidden_act
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self.hidden_dim = hidden_dim
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self.pooling_type = pooling_type
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self.initializer_range = initializer_range
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self.batch_norm_eps = batch_norm_eps
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self.batch_norm_momentum = batch_norm_momentum
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self.drop_connect_rate = drop_connect_rate
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self.num_hidden_layers = sum(num_block_repeats) * 4
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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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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the vision config dict if we are loading from AlignConfig
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if config_dict.get("model_type") == "align":
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config_dict = config_dict["vision_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 AlignConfig(PretrainedConfig):
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r"""
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[`AlignConfig`] is the configuration class to store the configuration of a [`AlignModel`]. It is used to
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instantiate a ALIGN model according to the specified arguments, defining the text model and vision model configs.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the ALIGN
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[kakaobrain/align-base](https://huggingface.co/kakaobrain/align-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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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`AlignTextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`AlignVisionConfig`].
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projection_dim (`int`, *optional*, defaults to 640):
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Dimentionality of text and vision projection layers.
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temperature_init_value (`float`, *optional*, defaults to 1.0):
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The inital value of the *temperature* paramter. Default is used as per the original ALIGN implementation.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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kwargs (*optional*):
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Dictionary of keyword arguments.
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Example:
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```python
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>>> from transformers import AlignConfig, AlignModel
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>>> # Initializing a AlignConfig with kakaobrain/align-base style configuration
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>>> configuration = AlignConfig()
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>>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration
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||
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>>> model = AlignModel(configuration)
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|
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||
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>>> # Accessing the model configuration
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||
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>>> configuration = model.config
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|
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>>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig
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>>> from transformers import AlignTextConfig, AlignVisionConfig
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|
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||
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>>> # Initializing ALIGN Text and Vision configurations
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||
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|
>>> config_text = AlignTextConfig()
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>>> config_vision = AlignVisionConfig()
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|
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>>> config = AlignConfig.from_text_vision_configs(config_text, config_vision)
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```"""
|
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|
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||
|
|
model_type = "align"
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|
|
|
||
|
|
def __init__(
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|
|
self,
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|
|
text_config=None,
|
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|
|
vision_config=None,
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||
|
|
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)
|