|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" 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 |
|
|
