# coding=utf-8
# Copyright 2020 The Fairseq Authors and The HuggingFace Inc. team.
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""" BART configuration """
from .configuration_utils import PretrainedConfig
from .file_utils import add_start_docstrings_to_callable
from .utils import logging
logger = logging.get_logger(__name__)
BART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/bart-base": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-base/config.json",
"facebook/bart-large": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large/config.json",
"facebook/bart-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-mnli/config.json",
"facebook/bart-large-cnn": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-cnn/config.json",
"facebook/bart-large-xsum": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-xsum/config.json",
"facebook/mbart-large-en-ro": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/config.json",
"yjernite/bart_eli5": "https://s3.amazonaws.com/models.huggingface.co/bert/yjernite/bart_eli5/config.json",
}
BART_CONFIG_ARGS_DOC = r"""
Args:
vocab_size (:obj:`int`, optional, defaults to 50265):
defines the different tokens that can be represented by `inputs_ids` passed to the forward method.
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, 16 for pegasus, 6 for bart-base and marian
decoder_layers (:obj:`int`, optional, defaults to 12):
Number of decoder layers, 16 for pegasus, 6 for bart-base and marian
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" (i.e., feed-forward) layer in decoder.
encoder_ffn_dim (:obj:`int`, optional, defaults to 4096):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in decoder.
activation_function (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the encoder and pooler.
If string, "gelu", "relu", "swish" and "gelu_new" are supported.
dropout (:obj:`float`, optional, defaults to 0.1):
The dropout probabilitiy 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.
add_bias_logits (:obj:`bool`, optional, defaults to :obj:`False`):
True for marian only.
normalize_before (:obj:`bool`, optional, defaults to :obj:`False`):
Call layernorm before attention ops. True for pegasus, mbart. False for bart. FIXME: marian?
normalize_embedding (:obj:`bool`, optional, defaults to :obj:`True`):
Call layernorm after embeddings. Only True for Bart.
static_position_embeddings (:obj:`bool`, optional, defaults to :obj:`False`):
Don't learn positional embeddings, use sinusoidal. True for marian, pegasus.
add_final_layer_norm (:obj:`bool`, optional, defaults to :obj:`False`):
Why not add another layernorm?
scale_embedding (:obj:`bool`, optional, defaults to :obj:`False`):
Scale embeddings by diving by sqrt(d_model).
eos_token_id (:obj:`int`, optional, defaults to 2)
End of stream token id.
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.
encoder_layerdrop: (:obj:`float`, optional, defaults to 0.0):
Google "layerdrop arxiv", as its not explainable in one line.
decoder_layerdrop: (:obj:`float`, optional, defaults to 0.0):
Google "layerdrop arxiv", as its not explainable in one line.
extra_pos_embeddings: (:obj:`int`, optional, defaults to 2):
How many extra learned positional embeddings to use. Should be pad_token_id+1 for bart.
num_labels: (:obj:`int`, optional, defaults to 3):
for SequenceClassification
is_encoder_decoder (:obj:`bool`, optional, defaults to :obj:`True`):
Whether this is an encoder/decoder model
force_bos_token_to_be_generated (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force BOS token to be generated at step 1 (after ``decoder_start_token_id``), only true for `bart-large-cnn`.
"""
[docs]@add_start_docstrings_to_callable(BART_CONFIG_ARGS_DOC)
class BartConfig(PretrainedConfig):
r"""
Configuration class for Bart. Parameters are renamed from the fairseq implementation
"""
model_type = "bart"
def __init__(
self,
activation_dropout=0.0,
extra_pos_embeddings=2, # FIXME(@sshleifer): delete?
activation_function="gelu",
vocab_size=50265,
d_model=1024,
encoder_ffn_dim=4096,
encoder_layers=12,
encoder_attention_heads=16,
decoder_ffn_dim=4096,
decoder_layers=12,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
attention_dropout=0.0,
dropout=0.1,
max_position_embeddings=1024,
init_std=0.02,
classifier_dropout=0.0,
num_labels=3,
is_encoder_decoder=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
normalize_before=False,
add_final_layer_norm=False,
scale_embedding=False,
normalize_embedding=True,
static_position_embeddings=False,
add_bias_logits=False,
force_bos_token_to_be_generated=False,
**common_kwargs
):
r"""
:class:`~transformers.BartConfig` is the configuration class for `BartModel`.
Examples::
>>> from transformers import BartConfig, BartModel
>>> config = BartConfig.from_pretrained('facebook/bart-large')
>>> model = BartModel(config)
"""
if "hidden_size" in common_kwargs:
raise ValueError("hidden size is called d_model")
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,
**common_kwargs,
)
self.vocab_size = vocab_size
self.d_model = d_model # encoder_embed_dim and decoder_embed_dim
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = self.num_hidden_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.max_position_embeddings = max_position_embeddings
self.init_std = init_std # Normal(0, this parameter)
self.activation_function = activation_function
# Params introduced for Mbart
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.normalize_embedding = normalize_embedding # True for mbart, False otherwise
self.normalize_before = normalize_before # combo of fairseq's encoder_ and decoder_normalize_before
self.add_final_layer_norm = add_final_layer_norm
# Params introduced for Marian
self.add_bias_logits = add_bias_logits
self.static_position_embeddings = static_position_embeddings
# 3 Types of Dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.dropout = dropout
# Classifier stuff
self.classif_dropout = classifier_dropout
# pos embedding offset
self.extra_pos_embeddings = self.pad_token_id + 1
self.force_bos_token_to_be_generated = force_bos_token_to_be_generated
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model
[docs] def is_valid_mbart(self) -> bool:
"""Is the configuration aligned with the MBART paper."""
if self.normalize_before and self.add_final_layer_norm and self.scale_embedding:
return True
if self.normalize_before or self.add_final_layer_norm or self.scale_embedding:
logger.info("This configuration is a mixture of MBART and BART settings")
return False