Source code for transformers.configuration_bart

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# Copyright 2020 The Fairseq Authors and The HuggingFace Inc. team.
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""" BART configuration """

from .configuration_utils import PretrainedConfig
from .file_utils import add_start_docstrings_to_callable
from .utils import logging

logger = logging.get_logger(__name__)

    "facebook/bart-base": "",
    "facebook/bart-large": "",
    "facebook/bart-large-mnli": "",
    "facebook/bart-large-cnn": "",
    "facebook/bart-large-xsum": "",
    "facebook/mbart-large-en-ro": "",
    "yjernite/bart_eli5": "",

        vocab_size (:obj:`int`, optional, defaults to 50265):
            defines the different tokens that can be represented by `inputs_ids` passed to the forward method.
        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, 16 for pegasus, 6 for bart-base and marian
        decoder_layers (:obj:`int`, optional, defaults to 12):
            Number of decoder layers, 16 for pegasus, 6 for bart-base and marian
        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" (i.e., feed-forward) layer in decoder.
        encoder_ffn_dim (:obj:`int`, optional, defaults to 4096):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in decoder.
        activation_function (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
            The non-linear activation function (function or string) in the encoder and pooler.
            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
        dropout (:obj:`float`, optional, defaults to 0.1):
            The dropout probabilitiy 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.
        add_bias_logits (:obj:`bool`, optional, defaults to :obj:`False`):
            True for marian only.
        normalize_before (:obj:`bool`, optional, defaults to :obj:`False`):
            Call layernorm before attention ops. True for pegasus, mbart. False for bart. FIXME: marian?
        normalize_embedding (:obj:`bool`, optional, defaults to :obj:`True`):
            Call layernorm after embeddings. Only True for Bart.
        static_position_embeddings (:obj:`bool`, optional, defaults to :obj:`False`):
            Don't learn positional embeddings, use sinusoidal. True for marian, pegasus.
        add_final_layer_norm (:obj:`bool`, optional, defaults to :obj:`False`):
            Why not add another layernorm?
        scale_embedding (:obj:`bool`, optional, defaults to :obj:`False`):
            Scale embeddings by diving by sqrt(d_model).
        eos_token_id (:obj:`int`, optional, defaults to 2)
            End of stream token id.
        pad_token_id (:obj:`int`, optional, defaults to 1)
            Padding token id.
        bos_token_id (:obj:`int`, optional, defaults to 0)
            Beginning of stream token id.
        encoder_layerdrop: (:obj:`float`, optional, defaults to 0.0):
            Google "layerdrop arxiv", as its not explainable in one line.
        decoder_layerdrop: (:obj:`float`, optional, defaults to 0.0):
            Google "layerdrop arxiv", as its not explainable in one line.
        extra_pos_embeddings: (:obj:`int`, optional, defaults to 2):
            How many extra learned positional embeddings to use. Should be pad_token_id+1 for bart.
        num_labels: (:obj:`int`, optional, defaults to 3):
            for SequenceClassification
        is_encoder_decoder (:obj:`bool`, optional, defaults to :obj:`True`):
            Whether this is an encoder/decoder model
        force_bos_token_to_be_generated (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to force BOS token to be generated at step 1 (after ``decoder_start_token_id``), only true for `bart-large-cnn`.


[docs]@add_start_docstrings_to_callable(BART_CONFIG_ARGS_DOC) class BartConfig(PretrainedConfig): r""" Configuration class for Bart. Parameters are renamed from the fairseq implementation """ model_type = "bart" def __init__( self, activation_dropout=0.0, extra_pos_embeddings=2, # FIXME(@sshleifer): delete? activation_function="gelu", vocab_size=50265, d_model=1024, encoder_ffn_dim=4096, encoder_layers=12, encoder_attention_heads=16, decoder_ffn_dim=4096, decoder_layers=12, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, attention_dropout=0.0, dropout=0.1, max_position_embeddings=1024, init_std=0.02, classifier_dropout=0.0, num_labels=3, is_encoder_decoder=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, normalize_before=False, add_final_layer_norm=False, scale_embedding=False, normalize_embedding=True, static_position_embeddings=False, add_bias_logits=False, force_bos_token_to_be_generated=False, **common_kwargs ): r""" :class:`~transformers.BartConfig` is the configuration class for `BartModel`. Examples:: >>> from transformers import BartConfig, BartModel >>> config = BartConfig.from_pretrained('facebook/bart-large') >>> model = BartModel(config) """ if "hidden_size" in common_kwargs: raise ValueError("hidden size is called d_model") 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, **common_kwargs, ) self.vocab_size = vocab_size self.d_model = d_model # encoder_embed_dim and decoder_embed_dim self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = self.num_hidden_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.max_position_embeddings = max_position_embeddings self.init_std = init_std # Normal(0, this parameter) self.activation_function = activation_function # Params introduced for Mbart self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.normalize_embedding = normalize_embedding # True for mbart, False otherwise self.normalize_before = normalize_before # combo of fairseq's encoder_ and decoder_normalize_before self.add_final_layer_norm = add_final_layer_norm # Params introduced for Marian self.add_bias_logits = add_bias_logits self.static_position_embeddings = static_position_embeddings # 3 Types of Dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.dropout = dropout # Classifier stuff self.classif_dropout = classifier_dropout # pos embedding offset self.extra_pos_embeddings = self.pad_token_id + 1 self.force_bos_token_to_be_generated = force_bos_token_to_be_generated @property def num_attention_heads(self) -> int: return self.encoder_attention_heads @property def hidden_size(self) -> int: return self.d_model
[docs] def is_valid_mbart(self) -> bool: """Is the configuration aligned with the MBART paper.""" if self.normalize_before and self.add_final_layer_norm and self.scale_embedding: return True if self.normalize_before or self.add_final_layer_norm or self.scale_embedding:"This configuration is a mixture of MBART and BART settings") return False