# coding=utf-8 # Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan 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. """LED model configuration""" from typing import List, Union from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class LEDConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LEDModel`]. It is used to instantiate an LED 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 LED [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) 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 50265): Vocabulary size of the LED model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LEDModel`] or [`TFLEDModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): 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): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): 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_encoder_position_embeddings (`int`, *optional*, defaults to 16384): The maximum sequence length that the encoder might ever be used with. max_decoder_position_embeddings (`int`, *optional*, defaults to 16384): The maximum sequence length that the decoder might ever be used with. 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://arxiv.org/abs/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://arxiv.org/abs/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) Example: ```python >>> from transformers import LEDModel, LEDConfig >>> # Initializing a LED allenai/led-base-16384 style configuration >>> configuration = LEDConfig() >>> # Initializing a model from the allenai/led-base-16384 style configuration >>> model = LEDModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "led" attribute_map = { "num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model", "attention_probs_dropout_prob": "attention_dropout", "initializer_range": "init_std", } def __init__( self, vocab_size=50265, max_encoder_position_embeddings=16384, max_decoder_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, 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", 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, pad_token_id=1, bos_token_id=0, eos_token_id=2, attention_window: Union[List[int], int] = 512, **kwargs, ): self.vocab_size = vocab_size self.max_encoder_position_embeddings = max_encoder_position_embeddings self.max_decoder_position_embeddings = max_decoder_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.attention_window = attention_window 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, ) __all__ = ["LEDConfig"]