Source code for transformers.configuration_funnel

# coding=utf-8
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""" Funnel Transformer model configuration """

from .configuration_utils import PretrainedConfig
from .utils import logging

logger = logging.get_logger(__name__)

    "funnel-transformer/small": "",
    "funnel-transformer/small-base": "",
    "funnel-transformer/medium": "",
    "funnel-transformer/medium-base": "",
    "funnel-transformer/intermediate": "",
    "funnel-transformer/intermediate-base": "",
    "funnel-transformer/large": "",
    "funnel-transformer/large-base": "",
    "funnel-transformer/xlarge": "",
    "funnel-transformer/xlarge-base": "",

[docs]class FunnelConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.FunnelModel` or a :class:`~transformers.TFBertModel`. It is used to instantiate a Funnel Transformer 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 Funnel Transformer `funnel-transformer/small <>`__ 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 30522): Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.FunnelModel` or :class:`~transformers.TFFunnelModel`. block_sizes (:obj:`List[int]`, `optional`, defaults to :obj:`[4, 4, 4]`): The sizes of the blocks used in the model. block_repeats (:obj:`List[int]`, `optional`): If passed along, each layer of each block is repeated the number of times indicated. num_decoder_layers (:obj:`int`, `optional`, defaults to 2): The number of layers in the decoder (when not using the base model). d_model (:obj:`int`, `optional`, defaults to 768): Dimensionality of the model's hidden states. n_head (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. d_head (:obj:`int`, `optional`, defaults to 64): Dimensionality of the model's heads. d_inner (:obj:`int`, `optional`, defaults to 3072): Inner dimension in the feed-forward blocks. hidden_act (:obj:`str` or :obj:`callable`, `optional`, defaults to :obj:`"gelu_new"`): 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. hidden_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 probabilities. activation_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probability used between the two layers of the feed-forward blocks. 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). type_vocab_size (:obj:`int`, `optional`, defaults to 3): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.FunnelModel` or :class:`~transformers.TFFunnelModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.1): The standard deviation of the `uniform initializer` for initializing all weight matrices in attention layers. initializer_std (:obj:`float`, `optional`): The standard deviation of the `normal initializer` for initializing the embedding matrix and the weight of linear layers. Will default to 1 for the embedding matrix and the value given by Xavier initialization for linear layers. layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-9): The epsilon used by the layer normalization layers. pooling_type (:obj:`str`, `optional`, defaults to :obj:`"mean"`): Possible values are ``"mean"`` or ``"max"``. The way pooling is performed at the beginning of each block. attention_type (:obj:`str`, `optional`, defaults to :obj:`"relative_shift"`): Possible values are ``"relative_shift"`` or ``"factorized"``. The former is faster on CPU/GPU while the latter is faster on TPU. separate_cls (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to separate the cls token when applying pooling. truncate_seq (:obj:`bool`, `optional`, defaults to :obj:`False`): When using ``separate_cls``, whether or not to truncate the last token when pooling, to avoid getting a sequence length that is not a multiple of 2. pool_q_only (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to apply the pooling only to the query or to query, key and values for the attention layers. """ model_type = "funnel" def __init__( self, vocab_size=30522, block_sizes=[4, 4, 4], block_repeats=None, num_decoder_layers=2, d_model=768, n_head=12, d_head=64, d_inner=3072, hidden_act="gelu_new", hidden_dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, max_position_embeddings=512, type_vocab_size=3, initializer_range=0.1, initializer_std=None, layer_norm_eps=1e-9, pooling_type="mean", attention_type="relative_shift", separate_cls=True, truncate_seq=True, pool_q_only=True, **kwargs ): super().__init__(**kwargs) self.vocab_size = vocab_size self.block_sizes = block_sizes self.block_repeats = [1] * len(block_sizes) if block_repeats is None else block_repeats assert len(block_sizes) == len( self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length." self.num_decoder_layers = num_decoder_layers self.d_model = d_model self.n_head = n_head self.d_head = d_head self.d_inner = d_inner self.hidden_act = hidden_act self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.initializer_std = initializer_std self.layer_norm_eps = layer_norm_eps assert pooling_type in [ "mean", "max", ], f"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported." self.pooling_type = pooling_type assert attention_type in [ "relative_shift", "factorized", ], f"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported." self.attention_type = attention_type self.separate_cls = separate_cls self.truncate_seq = truncate_seq self.pool_q_only = pool_q_only @property def hidden_size(self): return self.d_model @property def num_attention_heads(self): return self.n_head @property def num_hidden_layers(self): return sum(self.block_sizes) @property def num_blocks(self): return len(self.block_sizes)