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# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team.
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""" ProphetNet model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/prophetnet-large-uncased": "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json",
}
[docs]class ProphetNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.ProphetNetModel`. It is used
to instantiate a ProphetNet model according to the specified arguments, defining the model 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:
activation_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
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.
vocab_size (:obj:`int`, `optional`, defaults to 30522):
Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.ProphetNetModel`.
hidden_size (:obj:`int`, `optional`, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
num_encoder_layers (:obj:`int`, `optional`, defaults to 12):
Number of encoder layers.
num_encoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the ``intermediate`` (often named feed-forward) layer in decoder.
num_decoder_layers (:obj:`int`, `optional`, defaults to 12):
Number of decoder layers.
num_decoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
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.
add_cross_attention (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether cross-attention layers should be added to the model.
is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether this is an encoder/decoder model.
pad_token_id (:obj:`int`, `optional`, defaults to 1)
Padding token id.
bos_token_id (:obj:`int`, `optional`, defaults to 0)
Beginning of stream token id.
eos_token_id (:obj:`int`, `optional`, defaults to 2)
End of stream token id.
ngram (:obj:`int`, `optional`, defaults to 2)
Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
token.
num_buckets (:obj:`int`, `optional`, defaults to 32)
The number of buckets to use for each attention layer. This is for relative position calculation. See the
`T5 paper <see https://arxiv.org/abs/1910.10683>`__ for more details.
relative_max_distance (:obj:`int`, `optional`, defaults to 128)
Relative distances greater than this number will be put into the last same bucket. This is for relative
position calculation. See the `T5 paper <see https://arxiv.org/abs/1910.10683>`__ for more details.
disable_ngram_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether be trained predicting only the next first token.
eps (:obj:`float`, `optional`, defaults to 0.0):
Controls the ``epsilon`` parameter value for label smoothing in the loss calculation. If set to 0, no label
smoothing is performed.
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).
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
"""
model_type = "prophetnet"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
activation_dropout=0.1,
activation_function="gelu",
vocab_size=30522,
hidden_size=1024,
encoder_ffn_dim=4096,
num_encoder_layers=12,
num_encoder_attention_heads=16,
decoder_ffn_dim=4096,
num_decoder_layers=12,
num_decoder_attention_heads=16,
attention_dropout=0.1,
dropout=0.1,
max_position_embeddings=512,
init_std=0.02,
is_encoder_decoder=True,
add_cross_attention=True,
decoder_start_token_id=0,
ngram=2,
num_buckets=32,
relative_max_distance=128,
disable_ngram_loss=False,
gradient_checkpointing=False,
eps=0.0,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
**kwargs
):
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,
add_cross_attention=add_cross_attention,
decoder_start_token_id=decoder_start_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.encoder_ffn_dim = encoder_ffn_dim
self.num_encoder_layers = num_encoder_layers
self.num_encoder_attention_heads = num_encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.num_decoder_layers = num_decoder_layers
self.num_decoder_attention_heads = num_decoder_attention_heads
self.max_position_embeddings = max_position_embeddings
self.init_std = init_std # Normal(0, this parameter)
self.activation_function = activation_function
# parameters for prophetnet
self.ngram = ngram
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.disable_ngram_loss = disable_ngram_loss
self.eps = eps
# 3 Types of Dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.dropout = dropout
self.use_cache = use_cache
# 4 Training Args (should be removed soon)
self.gradient_checkpointing = gradient_checkpointing
@property
def num_attention_heads(self) -> int:
return self.num_encoder_attention_heads
@property
def num_hidden_layers(self) -> int:
return self.num_encoder_layers + self.num_decoder_layers