# coding=utf-8
# Copyright 2020, Hugging Face
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""" Funnel Transformer model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
"funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
"funnel-transformer/intermediate": "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json",
"funnel-transformer/intermediate-base": "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json",
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
"funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}
[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
<https://huggingface.co/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)