765 lines
36 KiB
Python
765 lines
36 KiB
Python
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# coding=utf-8
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# Copyright 2022 Meta Platforms authors and The HuggingFace 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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""" FLAVA model configurations"""
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import os
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from typing import Any, Dict, 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 FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class FlavaImageConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FlavaImageModel`]. It is used to instantiate an
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FLAVA model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
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[facebook/flava-full](https://huggingface.co/facebook/flava-full) 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 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" (i.e., feed-forward) layer in the Transformer encoder.
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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. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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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 0.0):
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The dropout ratio for the attention probabilities.
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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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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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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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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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mask_token (`bool`, *optional*, defaults to `True`):
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Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA.
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vocab_size (`int`, *optional*, defaults to 8192):
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Vocabulary size of the [`FlavaImageCodebook`] used in conjunction with [`FlavaImageModel`] for MIM (Masked
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Image Modeling) loss for FLAVA.
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Example:
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```python
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>>> from transformers import FlavaImageConfig, FlavaImageModel
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>>> # Initializing a FlavaImageModel with style configuration
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>>> configuration = FlavaImageConfig()
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>>> # Initializing a FlavaImageModel model (with random weights) from the style configuration
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>>> model = FlavaImageModel(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 = "flava_image_model"
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def __init__(
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self,
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hidden_size: int = 768,
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num_hidden_layers: int = 12,
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num_attention_heads: int = 12,
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intermediate_size: int = 3072,
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hidden_act: int = "gelu",
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hidden_dropout_prob: float = 0.0,
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attention_probs_dropout_prob: float = 0.0,
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initializer_range: float = 0.02,
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layer_norm_eps: float = 1e-12,
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image_size: int = 224,
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patch_size: int = 16,
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num_channels: int = 3,
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qkv_bias: bool = True,
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mask_token: bool = True,
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vocab_size: int = 8192,
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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_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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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.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.qkv_bias = qkv_bias
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self.mask_token = mask_token
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self.vocab_size = vocab_size
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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 image config dict if we are loading from FlavaConfig
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if config_dict.get("model_type") == "flava":
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config_dict = config_dict["image_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 FlavaTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FlavaTextModel`]. It is used to instantiate an
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FLAVA model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
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[facebook/flava-full](https://huggingface.co/facebook/flava-full) 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 30522):
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Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`FlavaTextModel`].
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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 [`FlavaTextModel`]. Note that even though
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text encoder allows `token_type_ids`'s value as 2, for text-only pretraining and fine-tuning, only 1 is
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used similar to RoBERTa.
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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). For VL, max_length passed to model is 77.
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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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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" (i.e., feed-forward) layer in the Transformer encoder.
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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. If string, `"gelu"`,
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`"relu"`, `"selu"` 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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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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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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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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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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Example:
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```python
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>>> from transformers import FlavaTextConfig, FlavaTextModel
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>>> # Initializing a FlavaTextModel with style configuration
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>>> configuration = FlavaTextConfig()
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>>> # Initializing a FlavaTextModel model (with random weights) from the style configuration
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>>> model = FlavaTextModel(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 = "flava_text_model"
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def __init__(
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self,
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vocab_size: int = 30522,
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type_vocab_size: int = 2,
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max_position_embeddings: int = 512,
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position_embedding_type: str = "absolute",
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hidden_size: int = 768,
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num_hidden_layers: int = 12,
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num_attention_heads: int = 12,
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intermediate_size: int = 3072,
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hidden_act: str = "gelu",
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hidden_dropout_prob: float = 0.0,
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attention_probs_dropout_prob: float = 0.0,
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initializer_range: float = 0.02,
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layer_norm_eps: float = 1e-12,
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pad_token_id: int = 0,
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qkv_bias: bool = 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.type_vocab_size = type_vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.position_embedding_type = position_embedding_type
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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.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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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.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.qkv_bias = qkv_bias
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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 FlavaConfig
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if config_dict.get("model_type") == "flava":
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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 FlavaMultimodalConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FlavaMultimodalModel`]. It is used to instantiate
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an FLAVA model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
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[facebook/flava-full](https://huggingface.co/facebook/flava-full) 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 6):
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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" (i.e., feed-forward) layer in the Transformer encoder.
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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. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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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 0.0):
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The dropout ratio for the attention probabilities.
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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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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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use_cls_token (`bool`, *optional*, defaults to `True`):
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Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model.
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Example:
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```python
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>>> from transformers import FlavaMultimodalConfig, FlavaMultimodalModel
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>>> # Initializing a FlavaMultimodalModel with style configuration
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>>> configuration = FlavaMultimodalConfig()
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>>> # Initializing a FlavaMultimodalModel model (with random weights) from the style configuration
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||
|
|
>>> model = FlavaMultimodalModel(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "flava_multimodal_model"
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
hidden_size: int = 768,
|
||
|
|
num_hidden_layers: int = 6,
|
||
|
|
num_attention_heads: int = 12,
|
||
|
|
intermediate_size: int = 3072,
|
||
|
|
hidden_act: int = "gelu",
|
||
|
|
hidden_dropout_prob: int = 0.0,
|
||
|
|
attention_probs_dropout_prob: int = 0.0,
|
||
|
|
initializer_range: float = 0.02,
|
||
|
|
layer_norm_eps: float = 1e-12,
|
||
|
|
qkv_bias: bool = True,
|
||
|
|
use_cls_token: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.num_hidden_layers = num_hidden_layers
|
||
|
|
self.num_attention_heads = num_attention_heads
|
||
|
|
self.intermediate_size = intermediate_size
|
||
|
|
self.hidden_act = hidden_act
|
||
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
||
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
self.layer_norm_eps = layer_norm_eps
|
||
|
|
self.qkv_bias = qkv_bias
|
||
|
|
self.use_cls_token = use_cls_token
|
||
|
|
|
||
|
|
@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 multimodal config dict if we are loading from FlavaConfig
|
||
|
|
if config_dict.get("model_type") == "flava":
|
||
|
|
config_dict = config_dict["multimodal_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 FlavaImageCodebookConfig(PretrainedConfig):
|
||
|
|
model_type = "flava_image_codebook"
|
||
|
|
|
||
|
|
r"""
|
||
|
|
[`FlavaImageCodebookConfig`] is the configuration class to store the configuration of a [`FlavaImageCodebook`]. It
|
||
|
|
is used to instantiate an FLAVA 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 FLAVA
|
||
|
|
[facebook/flava-image-codebook](https://huggingface.co/facebook/flava-image-codebook) architecture.
|
||
|
|
|
||
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||
|
|
documentation from [`PretrainedConfig`] for more information.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
num_groups (`int`, defaults to 4):
|
||
|
|
Number of groups to be created. This parameter as of now doesn't affect the model and is used for some
|
||
|
|
internal calculation and estimations.
|
||
|
|
input_channels (`int`, defaults to 3):
|
||
|
|
Number of channels in the image to be passed.
|
||
|
|
num_blocks_per_group (`int`, defaults to 2):
|
||
|
|
Number of conv-based blocks per group.
|
||
|
|
hidden_size (`int`, defaults to 256):
|
||
|
|
Size of hidden dim for the blocks.
|
||
|
|
vocab_size (`int`, defaults to 8192):
|
||
|
|
Size of the output vocabulary for the codebook.
|
||
|
|
freeze (`bool`, defaults to `True`):
|
||
|
|
Whether to freeze the weights of the model.
|
||
|
|
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 FlavaImageCodebookConfig, FlavaImageCodebook
|
||
|
|
|
||
|
|
>>> # Initializing a FlavaImageCodebook with style configuration
|
||
|
|
>>> configuration = FlavaImageCodebookConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a FlavaImageCodebook model (with random weights) from the style configuration
|
||
|
|
>>> model = FlavaImageCodebook(configuration)
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_groups: int = 4,
|
||
|
|
input_channels: int = 3,
|
||
|
|
num_blocks_per_group: int = 2,
|
||
|
|
hidden_size: int = 256,
|
||
|
|
vocab_size: int = 8192,
|
||
|
|
freeze: int = True,
|
||
|
|
initializer_range: float = 0.02,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
self.num_groups = num_groups
|
||
|
|
self.input_channels = input_channels
|
||
|
|
self.num_blocks_per_group = num_blocks_per_group
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.vocab_size = vocab_size
|
||
|
|
self.freeze = freeze
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
|
||
|
|
@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 image codebook config dict if we are loading from FlavaConfig
|
||
|
|
if config_dict.get("model_type") == "flava":
|
||
|
|
config_dict = config_dict["image_codebook_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 FlavaConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
[`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to
|
||
|
|
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
|
||
|
|
and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to
|
||
|
|
that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) 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 [`FlavaTextConfig`].
|
||
|
|
image_config (`dict`, *optional*):
|
||
|
|
Dictionary of configuration options used to initialize [`FlavaImageConfig`].
|
||
|
|
multimodal_config (`dict`, *optional*):
|
||
|
|
Dictionary of configuration options used to initialize [`FlavaMultimodalConfig`].
|
||
|
|
hidden_size (`int`, *optional*, defaults to 768):
|
||
|
|
Dimensionality of the encoder layers and the pooler layer.
|
||
|
|
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
|
||
|
|
The epsilon used by the layer normalization layers.
|
||
|
|
projection_dim (`int`, *optional*, defaults to 512):
|
||
|
|
Dimentionality of text and image projection layers.
|
||
|
|
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
|
||
|
|
The inital value of the *logit_scale* paramter. Default is used as per the original FLAVA/CLIP
|
||
|
|
implementation.
|
||
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
||
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||
|
|
ce_ignore_index (`int`, *optional*, defaults to -100):
|
||
|
|
Cross entropy index to ignore.
|
||
|
|
mim_weight (`float`, *optional*, defaults to 1.0):
|
||
|
|
Weight to be assigned to MIM (Masked Image Modeling) unimodal loss
|
||
|
|
mlm_weight (`float`, *optional*, defaults to 1.0):
|
||
|
|
Weight to be assigned to MLM (Masked Language Modeling) unimodal loss
|
||
|
|
global_contrastive_weight (`float`, *optional*, defaults to 1.0):
|
||
|
|
Weight to be assigned to global contrastive cross-alignment loss.
|
||
|
|
itm_weight (`float`, *optional*, defaults to 1.0):
|
||
|
|
Weight to be assigned to image-text matching multimodal loss.
|
||
|
|
mmm_image_weight (`float`, *optional*, defaults to 1.0):
|
||
|
|
Weight to be assigned to MMM loss's image part.
|
||
|
|
mmm_text_weight (`float`, *optional*, defaults to 1.0):
|
||
|
|
Weight to be assigned to MMM loss's text part.
|
||
|
|
global_backprop_contrastive (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether to use global backpropgation through all workers in contrastive loss.
|
||
|
|
skip_unmasked_multimodal_encoder (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses.
|
||
|
|
return_loss (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether to return loss or not
|
||
|
|
|
||
|
|
kwargs (*optional*):
|
||
|
|
Dictionary of keyword arguments.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import FlavaConfig, FlavaModel, FlavaForPreTraining
|
||
|
|
|
||
|
|
>>> # Initializing a FlavaConfig with style configuration
|
||
|
|
>>> configuration = FlavaConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a FlavaModel and FlavaForPreTraining model (with random weights) from the style configuration
|
||
|
|
>>> model = FlavaModel(configuration)
|
||
|
|
>>> model_pre = FlavaForPreTraining(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
>>> configuration_pre = model_pre.config
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
model_type = "flava"
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
image_config: Dict[str, Any] = None,
|
||
|
|
text_config: Dict[str, Any] = None,
|
||
|
|
multimodal_config: Dict[str, Any] = None,
|
||
|
|
image_codebook_config: Dict[str, Any] = None,
|
||
|
|
hidden_size: int = 768,
|
||
|
|
layer_norm_eps: float = 1e-12,
|
||
|
|
projection_dim: int = 768,
|
||
|
|
init_codebook: bool = True,
|
||
|
|
logit_scale_init_value: float = 2.6592,
|
||
|
|
initializer_range: float = 0.02,
|
||
|
|
ce_ignore_index: int = -100,
|
||
|
|
mim_weight: float = 1.0,
|
||
|
|
mlm_weight: float = 1.0,
|
||
|
|
global_contrastive_weight: float = 1.0,
|
||
|
|
itm_weight: float = 1.0,
|
||
|
|
mmm_image_weight: float = 1.0,
|
||
|
|
mmm_text_weight: float = 1.0,
|
||
|
|
global_backprop_contrastive: bool = True,
|
||
|
|
skip_unmasked_multimodal_encoder: bool = True,
|
||
|
|
return_loss: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
# If `_config_dict` exist, we use them for the backward compatibility.
|
||
|
|
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
|
||
|
|
# of confusion!).
|
||
|
|
text_config_dict = kwargs.pop("text_config_dict", None)
|
||
|
|
image_config_dict = kwargs.pop("image_config_dict", None)
|
||
|
|
multimodal_config_dict = kwargs.pop("multimodal_config_dict", None)
|
||
|
|
image_codebook_config_dict = kwargs.pop("image_codebook_config_dict", None)
|
||
|
|
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
|
||
|
|
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
|
||
|
|
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
|
||
|
|
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
|
||
|
|
if text_config_dict is not None:
|
||
|
|
if text_config is None:
|
||
|
|
text_config = {}
|
||
|
|
|
||
|
|
# This is the complete result when using `text_config_dict`.
|
||
|
|
_text_config_dict = FlavaTextConfig(**text_config_dict).to_dict()
|
||
|
|
|
||
|
|
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
|
||
|
|
for key, value in _text_config_dict.items():
|
||
|
|
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
|
||
|
|
# If specified in `text_config_dict`
|
||
|
|
if key in text_config_dict:
|
||
|
|
message = (
|
||
|
|
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
|
||
|
|
f'The value `text_config_dict["{key}"]` will be used instead.'
|
||
|
|
)
|
||
|
|
# If inferred from default argument values (just to be super careful)
|
||
|
|
else:
|
||
|
|
message = (
|
||
|
|
f"`text_config_dict` is provided which will be used to initialize `FlavaTextConfig`. The "
|
||
|
|
f'value `text_config["{key}"]` will be overriden.'
|
||
|
|
)
|
||
|
|
logger.info(message)
|
||
|
|
|
||
|
|
# Update all values in `text_config` with the ones in `_text_config_dict`.
|
||
|
|
text_config.update(_text_config_dict)
|
||
|
|
|
||
|
|
if image_config_dict is not None:
|
||
|
|
if image_config is None:
|
||
|
|
image_config = {}
|
||
|
|
|
||
|
|
# This is the complete result when using `image_config_dict`.
|
||
|
|
_image_config_dict = FlavaImageConfig(**image_config_dict).to_dict()
|
||
|
|
# convert keys to string instead of integer
|
||
|
|
if "id2label" in _image_config_dict:
|
||
|
|
_image_config_dict["id2label"] = {
|
||
|
|
str(key): value for key, value in _image_config_dict["id2label"].items()
|
||
|
|
}
|
||
|
|
|
||
|
|
# Give a warning if the values exist in both `_image_config_dict` and `image_config` but being different.
|
||
|
|
for key, value in _image_config_dict.items():
|
||
|
|
if key in image_config and value != image_config[key] and key not in ["transformers_version"]:
|
||
|
|
# If specified in `image_config_dict`
|
||
|
|
if key in image_config_dict:
|
||
|
|
message = (
|
||
|
|
f"`{key}` is found in both `image_config_dict` and `image_config` but with different "
|
||
|
|
f'values. The value `image_config_dict["{key}"]` will be used instead.'
|
||
|
|
)
|
||
|
|
# If inferred from default argument values (just to be super careful)
|
||
|
|
else:
|
||
|
|
message = (
|
||
|
|
f"`image_config_dict` is provided which will be used to initialize `FlavaImageConfig`. "
|
||
|
|
f'The value `image_config["{key}"]` will be overriden.'
|
||
|
|
)
|
||
|
|
logger.info(message)
|
||
|
|
|
||
|
|
# Update all values in `image_config` with the ones in `_image_config_dict`.
|
||
|
|
image_config.update(_image_config_dict)
|
||
|
|
|
||
|
|
if multimodal_config_dict is not None:
|
||
|
|
if multimodal_config is None:
|
||
|
|
multimodal_config = {}
|
||
|
|
|
||
|
|
# This is the complete result when using `multimodal_config_dict`.
|
||
|
|
_multimodal_config_dict = FlavaMultimodalConfig(**multimodal_config_dict).to_dict()
|
||
|
|
|
||
|
|
# Give a warning if the values exist in both `_multimodal_config_dict` and `multimodal_config` but being
|
||
|
|
# different.
|
||
|
|
for key, value in _multimodal_config_dict.items():
|
||
|
|
if (
|
||
|
|
key in multimodal_config
|
||
|
|
and value != multimodal_config[key]
|
||
|
|
and key not in ["transformers_version"]
|
||
|
|
):
|
||
|
|
# If specified in `multimodal_config_dict`
|
||
|
|
if key in multimodal_config_dict:
|
||
|
|
message = (
|
||
|
|
f"`{key}` is found in both `multimodal_config_dict` and `multimodal_config` but with "
|
||
|
|
f'different values. The value `multimodal_config_dict["{key}"]` will be used instead.'
|
||
|
|
)
|
||
|
|
# If inferred from default argument values (just to be super careful)
|
||
|
|
else:
|
||
|
|
message = (
|
||
|
|
f"`multimodal_config_dict` is provided which will be used to initialize "
|
||
|
|
f'`FlavaMultimodalConfig`. The value `multimodal_config["{key}"]` will be overriden.'
|
||
|
|
)
|
||
|
|
logger.info(message)
|
||
|
|
|
||
|
|
# Update all values in `multimodal_config` with the ones in `_multimodal_config_dict`.
|
||
|
|
multimodal_config.update(_multimodal_config_dict)
|
||
|
|
|
||
|
|
if image_codebook_config_dict is not None:
|
||
|
|
if image_codebook_config is None:
|
||
|
|
image_codebook_config = {}
|
||
|
|
|
||
|
|
# This is the complete result when using `image_codebook_config_dict`.
|
||
|
|
_image_codebook_config_dict = FlavaImageCodebookConfig(**image_codebook_config_dict).to_dict()
|
||
|
|
|
||
|
|
# Give a warning if the values exist in both `_image_codebook_config_dict` and `image_codebook_config` but
|
||
|
|
# being different.
|
||
|
|
for key, value in _image_codebook_config_dict.items():
|
||
|
|
if (
|
||
|
|
key in image_codebook_config
|
||
|
|
and value != image_codebook_config[key]
|
||
|
|
and key not in ["transformers_version"]
|
||
|
|
):
|
||
|
|
# If specified in `image_codebook_config_dict`
|
||
|
|
if key in image_codebook_config_dict:
|
||
|
|
message = (
|
||
|
|
f"`{key}` is found in both `image_codebook_config_dict` and `image_codebook_config` but "
|
||
|
|
f'with different values. The value `image_codebook_config_dict["{key}"]` will be used '
|
||
|
|
"instead."
|
||
|
|
)
|
||
|
|
# If inferred from default argument values (just to be super careful)
|
||
|
|
else:
|
||
|
|
message = (
|
||
|
|
f"`image_codebook_config_dict` is provided which will be used to initialize "
|
||
|
|
f'`FlavaImageCodebookConfig`. The value `image_codebook_config["{key}"]` will be overriden.'
|
||
|
|
)
|
||
|
|
logger.info(message)
|
||
|
|
|
||
|
|
# Update all values in `image_codebook_config` with the ones in `_image_codebook_config_dict`.
|
||
|
|
image_codebook_config.update(_image_codebook_config_dict)
|
||
|
|
|
||
|
|
if image_config is None:
|
||
|
|
image_config = {}
|
||
|
|
logger.info("`image_config` is `None`. initializing the `FlavaImageConfig` with default values.")
|
||
|
|
|
||
|
|
if text_config is None:
|
||
|
|
text_config = {}
|
||
|
|
logger.info("`text_config` is `None`. Initializing the `FlavaTextConfig` with default values.")
|
||
|
|
|
||
|
|
if multimodal_config is None:
|
||
|
|
multimodal_config = {}
|
||
|
|
logger.info("`multimodal_config` is `None`. initializing the `FlavaMultimodalConfig` with default values.")
|
||
|
|
|
||
|
|
if image_codebook_config is None:
|
||
|
|
image_codebook_config = {}
|
||
|
|
logger.info(
|
||
|
|
"`image_codebook_config` is `None`. initializing the `FlavaImageCodebookConfig` with default values."
|
||
|
|
)
|
||
|
|
|
||
|
|
self.image_config = FlavaImageConfig(**image_config)
|
||
|
|
self.text_config = FlavaTextConfig(**text_config)
|
||
|
|
self.multimodal_config = FlavaMultimodalConfig(**multimodal_config)
|
||
|
|
self.image_codebook_config = FlavaImageCodebookConfig(**image_codebook_config)
|
||
|
|
self.projection_dim = projection_dim
|
||
|
|
self.init_codebook = init_codebook
|
||
|
|
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.layer_norm_eps = layer_norm_eps
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
self.logit_scale_init_value = logit_scale_init_value
|
||
|
|
self.initializer_factor = 1.0
|
||
|
|
self.ce_ignore_index = ce_ignore_index
|
||
|
|
self.mim_weight = mim_weight
|
||
|
|
self.mlm_weight = mlm_weight
|
||
|
|
self.global_contrastive_weight = global_contrastive_weight
|
||
|
|
self.itm_weight = itm_weight
|
||
|
|
self.mmm_image_weight = mmm_image_weight
|
||
|
|
self.mmm_text_weight = mmm_text_weight
|
||
|
|
self.global_backprop_contrastive = global_backprop_contrastive
|
||
|
|
self.skip_unmasked_multimodal_encoder = skip_unmasked_multimodal_encoder
|
||
|
|
self.return_loss = return_loss
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_configs(
|
||
|
|
cls,
|
||
|
|
image_config: FlavaImageConfig,
|
||
|
|
text_config: FlavaTextConfig,
|
||
|
|
multimodal_config: FlavaMultimodalConfig,
|
||
|
|
image_codebook_config: FlavaImageCodebookConfig,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
r"""
|
||
|
|
Instantiate a [`FlavaConfig`] (or a derived class) from flava text model configuration, flava image model
|
||
|
|
configuration, flava multimodal model and flava codebook model configuration.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
[`FlavaConfig`]: An instance of a configuration object
|
||
|
|
"""
|
||
|
|
|
||
|
|
return cls(
|
||
|
|
image_config=image_config.to_dict(),
|
||
|
|
text_config=text_config.to_dict(),
|
||
|
|
multimodal_config=multimodal_config.to_dict(),
|
||
|
|
image_codebook_config=image_codebook_config.to_dict(),
|
||
|
|
**kwargs,
|
||
|
|
)
|