136 lines
5.1 KiB
Python
136 lines
5.1 KiB
Python
# coding=utf-8
|
|
# Copyright 2020 The Fairseq Authors and The HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
""" BART configuration """
|
|
|
|
|
|
import logging
|
|
|
|
from .configuration_utils import PretrainedConfig
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
BART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
|
"bart-large": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large/config.json",
|
|
"bart-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-mnli/config.json",
|
|
"bart-large-cnn": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-cnn/config.json",
|
|
"bart-large-xsum": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-xsum/config.json",
|
|
"mbart-large-en-ro": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/config.json",
|
|
}
|
|
|
|
|
|
class BartConfig(PretrainedConfig):
|
|
r"""
|
|
Configuration class for Bart. Parameters are renamed from the fairseq implementation
|
|
"""
|
|
model_type = "bart"
|
|
pretrained_config_archive_map = BART_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
|
|
def __init__(
|
|
self,
|
|
activation_dropout=0.0,
|
|
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,
|
|
**common_kwargs
|
|
):
|
|
r"""
|
|
:class:`~transformers.BartConfig` is the configuration class for `BartModel`.
|
|
Examples:
|
|
config = BartConfig.from_pretrained('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
|
|
|
|
@property
|
|
def num_attention_heads(self) -> int:
|
|
return self.encoder_attention_heads
|
|
|
|
@property
|
|
def hidden_size(self) -> int:
|
|
return self.d_model
|
|
|
|
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
|