Source code for transformers.models.bigbird_pegasus.configuration_bigbird_pegasus

# coding=utf-8
# Copyright Google Research and The HuggingFace Inc. team. All rights reserved.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" BigBirdPegasus model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

    "google/bigbird-pegasus-large-arxiv": "",
    "google/bigbird-pegasus-large-pubmed": "",
    "google/bigbird-pegasus-large-bigpatent": "",
    # See all BigBirdPegasus models at

[docs]class BigBirdPegasusConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.BigBirdPegasusModel`. It is used to instantiate an BigBirdPegasus model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the BigBirdPegasus `google/bigbird-pegasus-large-arxiv <>`__ 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 BigBirdPegasus model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.BigBirdPegasusModel`. d_model (:obj:`int`, `optional`, defaults to 1024): Dimension 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): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_new"`): 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 4096): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 1024 or 2048 or 4096). init_std (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the encoder. See the `LayerDrop paper <see>`__ for more details. decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the decoder. See the `LayerDrop paper <see>`__ for more details. 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). attention_type (:obj:`str`, `optional`, defaults to :obj:`"block_sparse"`) Whether to use block sparse attention (with n complexity) as introduced in paper or original attention layer (with n^2 complexity) in encoder. Possible values are :obj:`"original_full"` and :obj:`"block_sparse"`. use_bias (:obj:`bool`, `optional`, defaults to :obj:`False`) Whether to use bias in query, key, value. block_size (:obj:`int`, `optional`, defaults to 64) Size of each block. Useful only when :obj:`attention_type == "block_sparse"`. num_random_blocks (:obj:`int`, `optional`, defaults to 3) Each query is going to attend these many number of random blocks. Useful only when :obj:`attention_type == "block_sparse"`. scale_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`) Whether to rescale embeddings with (hidden_size ** 0.5). Example:: >>> from transformers import BigBirdPegasusModel, BigBirdPegasusConfig >>> # Initializing a BigBirdPegasus bigbird-pegasus-base style configuration >>> configuration = BigBirdPegasusConfig() >>> # Initializing a model from the bigbird-pegasus-base style configuration >>> model = BigBirdPegasusModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "bigbird_pegasus" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model", "attention_probs_dropout_prob": "attention_dropout", } def __init__( self, vocab_size=96103, max_position_embeddings=4096, encoder_layers=16, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=16, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="gelu_new", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, classifier_dropout=0.0, scale_embedding=True, pad_token_id=0, bos_token_id=2, eos_token_id=1, attention_type="block_sparse", # only for encoder block_size=64, num_random_blocks=3, use_bias=False, **kwargs ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True # extra config self.attention_type = attention_type self.block_size = block_size self.num_random_blocks = num_random_blocks self.use_bias = use_bias 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, decoder_start_token_id=decoder_start_token_id, **kwargs, )