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# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
""" 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
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