src/transformers/configuration_xlm.py

# coding=utf-8
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""" XLM configuration """


import logging

from .configuration_utils import PretrainedConfig


logger = logging.getLogger(__name__)

XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "xlm-mlm-en-2048": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
    "xlm-mlm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
    "xlm-mlm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",
    "xlm-mlm-enro-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
    "xlm-mlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
    "xlm-clm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
    "xlm-clm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
    "xlm-mlm-17-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",
    "xlm-mlm-100-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",
}


class XLMConfig(PretrainedConfig):
    """
        This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`.
        It is used to instantiate an XLM 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 `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ 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 30145):
                Vocabulary size of the XLM model. Defines the different tokens that
                can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`.
            emb_dim (:obj:`int`, optional, defaults to 2048):
                Dimensionality of the encoder layers and the pooler layer.
            n_layer (:obj:`int`, optional, defaults to 12):
                Number of hidden layers in the Transformer encoder.
            n_head (:obj:`int`, optional, defaults to 16):
                Number of attention heads for each attention layer in the Transformer encoder.
            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 0.1):
                The dropout probability for the attention mechanism
            gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`):
                The non-linear activation function (function or string) in the
                encoder and pooler. If set to `True`, "gelu" will be used instead of "relu".
            sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`):
                Whether to use sinusoidal positional embeddings instead of absolute positional embeddings.
            causal (:obj:`boolean`, optional, defaults to :obj:`False`):
                Set this to `True` for the model to behave in a causal manner.
                Causal models use a triangular attention mask in order to only attend to the left-side context instead
                if a bidirectional context.
            asm (:obj:`boolean`, optional, defaults to :obj:`False`):
                Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction
                layer.
            n_langs (:obj:`int`, optional, defaults to 1):
                The number of languages the model handles. Set to 1 for monolingual models.
            use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`)
                Whether to use language embeddings. Some models use additional language embeddings, see
                `the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
                for information on how to use them.
            max_position_embeddings (:obj:`int`, optional, defaults to 512):
                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).
            embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5):
                The standard deviation of the truncated_normal_initializer for
                initializing the embedding matrices.
            init_std (:obj:`int`, optional, defaults to 50257):
                The standard deviation of the truncated_normal_initializer for
                initializing all weight matrices except the embedding matrices.
            layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
                The epsilon used by the layer normalization layers.
            bos_index (:obj:`int`, optional, defaults to 0):
                The index of the beginning of sentence token in the vocabulary.
            eos_index (:obj:`int`, optional, defaults to 1):
                The index of the end of sentence token in the vocabulary.
            pad_index (:obj:`int`, optional, defaults to 2):
                The index of the padding token in the vocabulary.
            unk_index (:obj:`int`, optional, defaults to 3):
                The index of the unknown token in the vocabulary.
            mask_index (:obj:`int`, optional, defaults to 5):
                The index of the masking token in the vocabulary.
            is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`):
                Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
            summary_type (:obj:`string`, optional, defaults to "first"):
                Argument used when doing sequence summary. Used in for the multiple choice head in
                :class:`~transformers.XLMForSequenceClassification`.
                Is one of the following options:
                    - 'last' => take the last token hidden state (like XLNet)
                    - 'first' => take the first token hidden state (like Bert)
                    - 'mean' => take the mean of all tokens hidden states
                    - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
                    - 'attn' => Not implemented now, use multi-head attention
            summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
                Argument used when doing sequence summary. Used in for the multiple choice head in
                :class:`~transformers.XLMForSequenceClassification`.
                Add a projection after the vector extraction
            summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
                Argument used when doing sequence summary. Used in for the multiple choice head in
                :class:`~transformers.XLMForSequenceClassification`.
                'tanh' => add a tanh activation to the output, Other => no activation.
            summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
                Argument used when doing sequence summary. Used in for the multiple choice head in
                :class:`~transformers.XLMForSequenceClassification`.
                If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
            summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
                Argument used when doing sequence summary. Used in for the multiple choice head in
                :class:`~transformers.XLMForSequenceClassification`.
                Add a dropout before the projection and activation
            start_n_top (:obj:`int`, optional, defaults to 5):
                Used in the SQuAD evaluation script for XLM and XLNet.
            end_n_top (:obj:`int`, optional, defaults to 5):
                Used in the SQuAD evaluation script for XLM and XLNet.
            mask_token_id (:obj:`int`, optional, defaults to 0):
                Model agnostic parameter to identify masked tokens when generating text in an MLM context.
            lang_id (:obj:`int`, optional, defaults to 1):
                The ID of the language used by the model. This parameter is used when generating
                text in a given language.

        Example::

            from transformers import XLMConfig, XLMModel

            # Initializing a XLM configuration
            configuration = XLMConfig()

            # Initializing a model from the configuration
            model = XLMModel(configuration)

            # Accessing the model configuration
            configuration = model.config

        Attributes:
            pretrained_config_archive_map (Dict[str, str]):
                A dictionary containing all the available pre-trained checkpoints.
    """

    pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
    model_type = "xlm"

    def __init__(
        self,
        vocab_size=30145,
        emb_dim=2048,
        n_layers=12,
        n_heads=16,
        dropout=0.1,
        attention_dropout=0.1,
        gelu_activation=True,
        sinusoidal_embeddings=False,
        causal=False,
        asm=False,
        n_langs=1,
        use_lang_emb=True,
        max_position_embeddings=512,
        embed_init_std=2048 ** -0.5,
        layer_norm_eps=1e-12,
        init_std=0.02,
        bos_index=0,
        eos_index=1,
        pad_index=2,
        unk_index=3,
        mask_index=5,
        is_encoder=True,
        summary_type="first",
        summary_use_proj=True,
        summary_activation=None,
        summary_proj_to_labels=True,
        summary_first_dropout=0.1,
        start_n_top=5,
        end_n_top=5,
        mask_token_id=0,
        lang_id=0,
        **kwargs
    ):
        """Constructs XLMConfig.
        """
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.emb_dim = emb_dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.gelu_activation = gelu_activation
        self.sinusoidal_embeddings = sinusoidal_embeddings
        self.causal = causal
        self.asm = asm
        self.n_langs = n_langs
        self.use_lang_emb = use_lang_emb
        self.layer_norm_eps = layer_norm_eps
        self.bos_index = bos_index
        self.eos_index = eos_index
        self.pad_index = pad_index
        self.unk_index = unk_index
        self.mask_index = mask_index
        self.is_encoder = is_encoder
        self.max_position_embeddings = max_position_embeddings
        self.embed_init_std = embed_init_std
        self.init_std = init_std
        self.summary_type = summary_type
        self.summary_use_proj = summary_use_proj
        self.summary_activation = summary_activation
        self.summary_proj_to_labels = summary_proj_to_labels
        self.summary_first_dropout = summary_first_dropout
        self.start_n_top = start_n_top
        self.end_n_top = end_n_top
        self.mask_token_id = mask_token_id
        self.lang_id = lang_id

        if "n_words" in kwargs:
            self.n_words = kwargs["n_words"]

    @property
    def n_words(self):  # For backward compatibility
        return self.vocab_size

    @n_words.setter
    def n_words(self, value):  # For backward compatibility
        self.vocab_size = value

    @property
    def hidden_size(self):
        return self.emb_dim

    @property
    def num_attention_heads(self):
        return self.n_heads

    @property
    def num_hidden_layers(self):
        return self.n_layers