# 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 # # 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. """BigBirdPegasus model configuration""" from ...configuration_utils import PreTrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class BigBirdPegasusConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`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](https://huggingface.co/google/bigbird-pegasus-large-arxiv) architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 96103): Vocabulary size of the BigBirdPegasus model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BigBirdPegasusModel`]. d_model (`int`, *optional*, defaults to 1024): Dimension of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 16): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 16): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`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 (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). attention_type (`str`, *optional*, defaults to `"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 `"original_full"` and `"block_sparse"`. use_bias (`bool`, *optional*, defaults to `False`) Whether to use bias in query, key, value. block_size (`int`, *optional*, defaults to 64) Size of each block. Useful only when `attention_type == "block_sparse"`. num_random_blocks (`int`, *optional*, defaults to 3) Each query is going to attend these many number of random blocks. Useful only when `attention_type == "block_sparse"`. scale_embeddings (`bool`, *optional*, defaults to `True`) Whether to rescale embeddings with (hidden_size ** 0.5). Example: ```python >>> from transformers import BigBirdPegasusConfig, BigBirdPegasusModel >>> # Initializing a BigBirdPegasus bigbird-pegasus-base style configuration >>> configuration = BigBirdPegasusConfig() >>> # Initializing a model (with random weights) 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, is_decoder=False, tie_word_embeddings=True, **kwargs, ): self.is_decoder = is_decoder self.tie_word_embeddings = tie_word_embeddings 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 self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.decoder_start_token_id = decoder_start_token_id super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) __all__ = ["BigBirdPegasusConfig"]