# coding=utf-8
# Copyright 2020 Google and The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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""" PEGASUS model configuration """
from ...utils import logging
from ..bart.configuration_bart import BartConfig
logger = logging.get_logger(__name__)
# These config values do not vary between checkpoints
DEFAULTS = dict(
vocab_size=96103,
max_position_embeddings=512,
d_model=1024,
encoder_ffn_dim=4096,
decoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_attention_heads=16,
encoder_layers=16,
decoder_layers=16,
dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.1,
pad_token_id=0,
eos_token_id=1,
is_encoder_decoder=True,
normalize_before=True,
scale_embedding=True,
normalize_embedding=False,
add_final_layer_norm=True,
static_position_embeddings=True,
num_beams=8,
activation_function="relu",
)
# Config values that vary between checkpoints: for testing and conversion
task_specific_params = {
# These are task specific params for pegasus-large and normal params for finetuned checkpoints
"summarization_xsum": {"length_penalty": 0.6, "max_length": 64, "max_position_embeddings": 512},
"summarization_cnn_dailymail": {"length_penalty": 0.8, "max_length": 128, "max_position_embeddings": 1024},
"summarization_newsroom": {"length_penalty": 0.8, "max_length": 128, "max_position_embeddings": 512},
"summarization_wikihow": {"length_penalty": 0.6, "max_length": 256, "max_position_embeddings": 512},
"summarization_multi_news": {"length_penalty": 0.8, "max_length": 256, "max_position_embeddings": 1024},
"summarization_reddit_tifu": {"length_penalty": 0.6, "max_length": 128, "max_position_embeddings": 512},
"summarization_big_patent": {"length_penalty": 0.7, "max_length": 256, "max_position_embeddings": 1024},
"summarization_arxiv": {"length_penalty": 0.8, "max_length": 256, "max_position_embeddings": 1024},
"summarization_pubmed": {"length_penalty": 0.8, "max_length": 256, "max_position_embeddings": 1024},
"summarization_gigaword": {"length_penalty": 0.6, "max_length": 32, "max_position_embeddings": 128},
"summarization_aeslc": {"length_penalty": 0.6, "max_length": 32, "max_position_embeddings": 512},
"summarization_billsum": {"length_penalty": 0.6, "max_length": 256, "max_position_embeddings": 1024},
# this last entry is useless -- just for consistency
"summarization_large": {"length_penalty": 0.8, "max_length": 256, "max_position_embeddings": 1024},
}
[docs]class PegasusConfig(BartConfig):
"""
This is the configuration class to store the configuration of a
:class:`~transformers.PegasusForConditionalGeneration`. It is used to instantiate a Pegasus 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:
vocab_size (:obj:`int`, `optional`, defaults to 96103):
Vocabulary size of the Pegasus model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.PegasusForConditionalGeneration`.
d_model (:obj:`int`, `optional`, defaults to 1024):
Dimensionality 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):
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 :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.
add_bias_logits (:obj:`bool`, `optional`, defaults to :obj:`False`):
This should be completed, specific to marian.
normalize_before (:obj:`bool`, `optional`, defaults to :obj:`True`):
Call layernorm before attention ops.
normalize_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`):
Call layernorm after embeddings.
static_position_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`):
Don't learn positional embeddings, use sinusoidal.
add_final_layer_norm (:obj:`bool`, `optional`, defaults to :obj:`True`):
Why not add another layernorm?
scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`True`):
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):
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.
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.
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``).
"""
model_type = "pegasus"
keys_to_ignore_at_inference = ["past_key_values"]
# The implementation of the config object is in BartConfig