configuration_veld.py

# coding=utf-8
# Copyright 2022, The T5 Authors and HuggingFace Inc, san kim.
#
# 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.
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""" vision-encoder-language-decoder-t5 model configuration"""
import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from transformers.models.auto.configuration_auto import AutoConfig
from transformers import T5Config, ViTConfig


logger = logging.get_logger(__name__)

class VELDConfig(PretrainedConfig):
    r"""
    [`VELDConfig`] is the configuration class to store the configuration of a
    [`VELDConfig`]. It is used to instantiate a Vision-Encoder-Text-Decoder model according to the
    specified arguments, defining the encoder and decoder configs.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        kwargs (*optional*):
            Dictionary of keyword arguments. Notably:

                - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the encoder config.
                - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the decoder config.

    Examples:

    ```python
    >>> from transformers import T5Config, ViTConfig
    >>> from configuration_veld import VELDConfig
    >>> from modeling_veld import VELDModel

    >>> # Initializing a ViT & T5 style configuration
    >>> config_encoder = ViTConfig()
    >>> config_decoder = T5Config()

    >>> config = VELDConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

    >>> # Initializing a ViTBert model from a ViT & bert-base-uncased style configurations
    >>> model = VELDModel(config=config)

    >>> # Accessing the model configuration
    >>> config_encoder = model.config.encoder
    >>> config_decoder = model.config.decoder
    >>> # set decoder config to causal lm
    >>> config_decoder.is_decoder = True
    >>> config_decoder.add_cross_attention = True

    >>> # Saving the model, including its configuration
    >>> model.save_pretrained("my-model")

    >>> # loading model and config from pretrained folder
    >>> encoder_decoder_config = VELDConfig.from_pretrained("my-model")
    >>> model = VELDModel.from_pretrained("my-model", config=encoder_decoder_config)
    ```"""
    model_type = "veld"
    is_composition = True

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                f"A configuraton of type {self.model_type} cannot be instantiated because "
                f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"
            )

        encoder_config = kwargs.pop("encoder")
        encoder_model_type = encoder_config.pop("model_type")
        decoder_config = kwargs.pop("decoder")
        decoder_model_type = decoder_config.pop("model_type")

        self.encoder = ViTConfig(**encoder_config)
        self.decoder = T5Config(**decoder_config)
        self.is_encoder_decoder = True

        self.pad_token_id=self.decoder.pad_token_id
        self.eos_token_id=self.decoder.eos_token_id

        self.num_queries_global = getattr(kwargs, "num_queries_global", 1)
        self.num_queries_local = getattr(kwargs, "num_queries_local", 256)


    @classmethod
    def from_encoder_decoder_configs(
        cls, encoder_config: PretrainedConfig, decoder_config: T5Config, **kwargs
    ) -> PretrainedConfig:
        r"""
        Instantiate a [`VELDConfig`] (or a derived class) from a pre-trained encoder model
        configuration and decoder model configuration.

        Returns:
            [`VELDConfig`]: An instance of a configuration object
        """
        logger.info("Setting `config.is_decoder=True` and `config.is_encoder_decoder=False` for decoder_config")
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False

        return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)

    def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Override the default *to_dict()* from *PretrainedConfig*.

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output["encoder"] = self.encoder.to_dict()
        output["decoder"] = self.decoder.to_dict()
        output["model_type"] = self.__class__.model_type
        return output