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""" BigBirdPegasus model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/bigbird-pegasus-large-arxiv": "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json",
"google/bigbird-pegasus-large-pubmed": "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json",
"google/bigbird-pegasus-large-bigpatent": "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json",
# See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}
[docs]class BigBirdPegasusConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.BigBirdPegasusModel`. It is
used to instantiate an BigBirdPegasus 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 BigBirdPegasus
`google/bigbird-pegasus-large-arxiv <https://huggingface.co/google/bigbird-pegasus-large-arxiv>`__ 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 96103):
Vocabulary size of the BigBirdPegasus model. Defines the number of different tokens that can be represented
by the :obj:`inputs_ids` passed when calling :class:`~transformers.BigBirdPegasusModel`.
d_model (:obj:`int`, `optional`, defaults to 1024):
Dimension of the layers and the pooler layer.
encoder_layers (:obj:`int`, `optional`, defaults to 16):
Number of encoder layers.
decoder_layers (:obj:`int`, `optional`, defaults to 16):
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):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_new"`):
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 4096):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 1024 or 2048 or 4096).
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.
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).
attention_type (:obj:`str`, `optional`, defaults to :obj:`"block_sparse"`)
Whether to use block sparse attention (with n complexity) as introduced in paper or original attention
layer (with n^2 complexity) in encoder. Possible values are :obj:`"original_full"` and
:obj:`"block_sparse"`.
use_bias (:obj:`bool`, `optional`, defaults to :obj:`False`)
Whether to use bias in query, key, value.
block_size (:obj:`int`, `optional`, defaults to 64)
Size of each block. Useful only when :obj:`attention_type == "block_sparse"`.
num_random_blocks (:obj:`int`, `optional`, defaults to 3)
Each query is going to attend these many number of random blocks. Useful only when :obj:`attention_type ==
"block_sparse"`.
scale_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`)
Whether to rescale embeddings with (hidden_size ** 0.5).
Example::
>>> from transformers import BigBirdPegasusModel, BigBirdPegasusConfig
>>> # Initializing a BigBirdPegasus bigbird-pegasus-base style configuration
>>> configuration = BigBirdPegasusConfig()
>>> # Initializing a model from the bigbird-pegasus-base style configuration
>>> model = BigBirdPegasusModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "bigbird_pegasus"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "encoder_attention_heads",
"hidden_size": "d_model",
"attention_probs_dropout_prob": "attention_dropout",
}
def __init__(
self,
vocab_size=96103,
max_position_embeddings=4096,
encoder_layers=16,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=16,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
use_cache=True,
is_encoder_decoder=True,
activation_function="gelu_new",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=2,
classifier_dropout=0.0,
scale_embedding=True,
pad_token_id=0,
bos_token_id=2,
eos_token_id=1,
attention_type="block_sparse", # only for encoder
block_size=64,
num_random_blocks=3,
use_bias=False,
**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.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
# extra config
self.attention_type = attention_type
self.block_size = block_size
self.num_random_blocks = num_random_blocks
self.use_bias = use_bias
super().__init__(
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,
**kwargs,
)