|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" RotoBART model configuration """ |
|
import warnings |
|
|
|
from transformers.configuration_utils import PretrainedConfig |
|
from transformers.utils import logging |
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
RotoBART_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
|
"facebook/RotoBART-large": "https://huggingface.co/facebook/RotoBART-large/resolve/main/config.json", |
|
|
|
} |
|
|
|
|
|
class RotoBARTConfig(PretrainedConfig): |
|
r""" |
|
This is the configuration class to store the configuration of a :class:`~transformers.RotoBARTModel`. It is used to |
|
instantiate a RotoBART 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 RotoBART `facebook/RotoBART-large |
|
<https://huggingface.co/facebook/RotoBART-large>`__ architecture. |
|
|
|
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model |
|
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. |
|
|
|
|
|
Args: |
|
vocab_size (:obj:`int`, `optional`, defaults to 50265): |
|
Vocabulary size of the RotoBART model. Defines the number of different tokens that can be represented by the |
|
:obj:`inputs_ids` passed when calling :class:`~transformers.RotoBARTModel` or |
|
:class:`~transformers.TFRotoBARTModel`. |
|
d_model (:obj:`int`, `optional`, defaults to 1024): |
|
Dimensionality of the layers and the pooler layer. |
|
encoder_layers (:obj:`int`, `optional`, defaults to 12): |
|
Number of encoder layers. |
|
decoder_layers (:obj:`int`, `optional`, defaults to 12): |
|
Number of decoder layers. |
|
encoder_attention_heads (:obj:`int`, `optional`, defaults to 16): |
|
Number of attention heads for each attention layer in the Transformer encoder. |
|
decoder_attention_heads (:obj:`int`, `optional`, defaults to 16): |
|
Number of attention heads for each attention layer in the Transformer decoder. |
|
decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): |
|
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. |
|
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): |
|
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. |
|
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`): |
|
The non-linear activation function (function or string) in the encoder and pooler. If string, |
|
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. |
|
dropout (:obj:`float`, `optional`, defaults to 0.1): |
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
|
attention_dropout (:obj:`float`, `optional`, defaults to 0.0): |
|
The dropout ratio for the attention probabilities. |
|
activation_dropout (:obj:`float`, `optional`, defaults to 0.0): |
|
The dropout ratio for activations inside the fully connected layer. |
|
classifier_dropout (:obj:`float`, `optional`, defaults to 0.0): |
|
The dropout ratio for classifier. |
|
max_position_embeddings (:obj:`int`, `optional`, defaults to 1024): |
|
The maximum sequence length that this model might ever be used with. Typically set this to something large |
|
just in case (e.g., 512 or 1024 or 2048). |
|
init_std (:obj:`float`, `optional`, defaults to 0.02): |
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
|
encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): |
|
The LayerDrop probability for the encoder. See the `LayerDrop paper <see |
|
https://arxiv.org/abs/1909.11556>`__ for more details. |
|
decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): |
|
The LayerDrop probability for the decoder. See the `LayerDrop paper <see |
|
https://arxiv.org/abs/1909.11556>`__ for more details. |
|
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): |
|
If True, use gradient checkpointing to save memory at the expense of slower backward pass. |
|
scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`): |
|
Scale embeddings by diving by sqrt(d_model). |
|
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): |
|
Whether or not the model should return the last key/values attentions (not used by all models). |
|
num_labels: (:obj:`int`, `optional`, defaults to 3): |
|
The number of labels to use in :class:`~transformers.RotoBARTForSequenceClassification`. |
|
forced_eos_token_id (:obj:`int`, `optional`, defaults to 2): |
|
The id of the token to force as the last generated token when :obj:`max_length` is reached. Usually set to |
|
:obj:`eos_token_id`. |
|
|
|
Example:: |
|
|
|
>>> from transformers import RotoBARTModel, RotoBARTConfig |
|
|
|
>>> # Initializing a RotoBART facebook/RotoBART-large style configuration |
|
>>> configuration = RotoBARTConfig() |
|
|
|
>>> # Initializing a model from the facebook/RotoBART-large style configuration |
|
>>> model = RotoBARTModel(configuration) |
|
|
|
>>> # Accessing the model configuration |
|
>>> configuration = model.config |
|
""" |
|
model_type = "RotoBART" |
|
keys_to_ignore_at_inference = ["past_key_values"] |
|
|
|
def __init__( |
|
self, |
|
vocab_size=50265, |
|
max_position_embeddings=1024, |
|
encoder_layers=12, |
|
encoder_ffn_dim=4096, |
|
encoder_attention_heads=16, |
|
decoder_layers=12, |
|
decoder_ffn_dim=4096, |
|
decoder_attention_heads=16, |
|
encoder_layerdrop=0.0, |
|
decoder_layerdrop=0.0, |
|
activation_function="gelu", |
|
d_model=1024, |
|
dropout=0.1, |
|
attention_dropout=0.0, |
|
activation_dropout=0.0, |
|
init_std=0.02, |
|
classifier_dropout=0.0, |
|
scale_embedding=False, |
|
gradient_checkpointing=False, |
|
use_cache=True, |
|
num_labels=3, |
|
pad_token_id=1, |
|
bos_token_id=0, |
|
eos_token_id=2, |
|
is_encoder_decoder=True, |
|
decoder_start_token_id=2, |
|
forced_eos_token_id=2, |
|
**kwargs, |
|
): |
|
super().__init__( |
|
num_labels=num_labels, |
|
pad_token_id=pad_token_id, |
|
bos_token_id=bos_token_id, |
|
eos_token_id=eos_token_id, |
|
is_encoder_decoder=is_encoder_decoder, |
|
decoder_start_token_id=decoder_start_token_id, |
|
forced_eos_token_id=forced_eos_token_id, |
|
**kwargs, |
|
) |
|
|
|
self.vocab_size = vocab_size |
|
self.max_position_embeddings = max_position_embeddings |
|
self.d_model = d_model |
|
self.encoder_ffn_dim = encoder_ffn_dim |
|
self.encoder_layers = encoder_layers |
|
self.encoder_attention_heads = encoder_attention_heads |
|
self.decoder_ffn_dim = decoder_ffn_dim |
|
self.decoder_layers = decoder_layers |
|
self.decoder_attention_heads = decoder_attention_heads |
|
self.dropout = dropout |
|
self.attention_dropout = attention_dropout |
|
self.activation_dropout = activation_dropout |
|
self.activation_function = activation_function |
|
self.init_std = init_std |
|
self.encoder_layerdrop = encoder_layerdrop |
|
self.decoder_layerdrop = decoder_layerdrop |
|
self.classifier_dropout = classifier_dropout |
|
self.use_cache = use_cache |
|
self.num_hidden_layers = encoder_layers |
|
self.gradient_checkpointing = gradient_checkpointing |
|
self.scale_embedding = scale_embedding |
|
|
|
|
|
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): |
|
self.forced_bos_token_id = self.bos_token_id |
|
warnings.warn( |
|
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions." |
|
"The config can simply be saved and uploaded again to be fixed." |
|
) |
|
|
|
@property |
|
def num_attention_heads(self) -> int: |
|
return self.encoder_attention_heads |
|
|
|
@property |
|
def hidden_size(self) -> int: |
|
return self.d_model |
|
|