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| # 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__) | |
| LED_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json", | |
| # See all LED models at https://huggingface.co/models?filter=led | |
| } | |
| class LEDConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a :class:`~transformers.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 :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 50265): | |
| Vocabulary size of the LED model. Defines the number of different tokens that can be represented by the | |
| :obj:`inputs_ids` passed when calling :class:`~transformers.LEDModel` or :class:`~transformers.TFLEDModel`. | |
| d_model (:obj:`int`, `optional`, defaults to 1024): | |
| Dimensionality of the layers and the pooler layer. | |
| encoder_layers (:obj:`int`, `optional`, defaults to 12): | |
| Number of encoder layers. | |
| decoder_layers (:obj:`int`, `optional`, defaults to 12): | |
| 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" (often named feed-forward) layer in decoder. | |
| encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): | |
| Dimensionality of the "intermediate" (often named 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_encoder_position_embeddings (:obj:`int`, `optional`, defaults to 16384): | |
| The maximum sequence length that the encoder might ever be used with. | |
| max_decoder_position_embeddings (:obj:`int`, `optional`, defaults to 16384): | |
| The maximum sequence length that the decoder might ever be used with. | |
| 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 | |
| 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. | |
| 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) | |
| gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| If True, use gradient checkpointing to save memory at the expense of slower backward pass. | |
| Example:: | |
| >>> 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" | |
| 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, | |
| gradient_checkpointing=False, | |
| attention_window: Union[List[int], int] = 512, | |
| **kwargs | |
| ): | |
| 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, | |
| ) | |
| 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 | |
| self.gradient_checkpointing = gradient_checkpointing | |
| def num_attention_heads(self) -> int: | |
| return self.encoder_attention_heads | |
| def hidden_size(self) -> int: | |
| return self.d_model | |
| def attention_probs_dropout_prob(self) -> float: | |
| return self.attention_dropout | |
| def initializer_range(self) -> float: | |
| return self.init_std | |