Source code for transformers.models.pegasus.configuration_pegasus

# coding=utf-8
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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