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| # coding=utf-8 | |
| # Copyright 2021 The Fairseq Authors 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. | |
| """ BART model configuration """ | |
| import warnings | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| BART_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json", | |
| # See all BART models at https://huggingface.co/models?filter=bart | |
| } | |
| class BartConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a :class:`~transformers.BartModel`. It is used to | |
| instantiate a BART 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 BART `facebook/bart-large | |
| <https://huggingface.co/facebook/bart-large>`__ 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 BART model. Defines the number of different tokens that can be represented by the | |
| :obj:`inputs_ids` passed when calling :class:`~transformers.BartModel` or | |
| :class:`~transformers.TFBartModel`. | |
| 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_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. | |
| 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. | |
| gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| If True, use gradient checkpointing to save memory at the expense of slower backward pass. | |
| scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Scale embeddings by diving by sqrt(d_model). | |
| 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). | |
| num_labels: (:obj:`int`, `optional`, defaults to 3): | |
| The number of labels to use in :class:`~transformers.BartForSequenceClassification`. | |
| forced_eos_token_id (:obj:`int`, `optional`, defaults to 2): | |
| The id of the token to force as the last generated token when :obj:`max_length` is reached. Usually set to | |
| :obj:`eos_token_id`. | |
| Example:: | |
| >>> from transformers import BartModel, BartConfig | |
| >>> # Initializing a BART facebook/bart-large style configuration | |
| >>> configuration = BartConfig() | |
| >>> # Initializing a model from the facebook/bart-large style configuration | |
| >>> model = BartModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| """ | |
| model_type = "bart" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} | |
| def __init__( | |
| self, | |
| vocab_size=50265, | |
| decoder_vocab_size=16384 + 1, # encoded image token space + 1 for bos | |
| max_position_embeddings=1024, | |
| decoder_max_position_embeddings=256 + 1, # number of encoded tokens + 1 for bos, | |
| 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, | |
| activation_function="gelu", | |
| d_model=1024, | |
| dropout=0.1, | |
| attention_dropout=0.0, | |
| activation_dropout=0.0, | |
| init_std=0.02, | |
| classifier_dropout=0.0, | |
| scale_embedding=False, | |
| gradient_checkpointing=False, | |
| use_cache=True, | |
| num_labels=3, | |
| pad_token_id=1, | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| is_encoder_decoder=True, | |
| decoder_start_token_id=16384, | |
| forced_eos_token_id=2, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.decoder_vocab_size = decoder_vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.decoder_max_position_embeddings = decoder_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.gradient_checkpointing = gradient_checkpointing | |
| self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True | |
| super().__init__( | |
| num_labels=num_labels, | |
| 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, | |
| forced_eos_token_id=forced_eos_token_id, | |
| **kwargs, | |
| ) | |
| # ensure backward compatibility for BART CNN models | |
| if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): | |
| self.forced_bos_token_id = self.bos_token_id | |
| warnings.warn( | |
| f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions." | |
| "The config can simply be saved and uploaded again to be fixed." | |
| ) | |