# coding=utf-8 # Copyright 2020, Hugging Face # # 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. """Funnel Transformer model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class FunnelConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FunnelModel`] or a [`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 [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FunnelModel`] or [`TFFunnelModel`]. block_sizes (`List[int]`, *optional*, defaults to `[4, 4, 4]`): The sizes of the blocks used in the model. block_repeats (`List[int]`, *optional*): If passed along, each layer of each block is repeated the number of times indicated. num_decoder_layers (`int`, *optional*, defaults to 2): The number of layers in the decoder (when not using the base model). d_model (`int`, *optional*, defaults to 768): Dimensionality of the model's hidden states. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. d_head (`int`, *optional*, defaults to 64): Dimensionality of the model's heads. d_inner (`int`, *optional*, defaults to 3072): Inner dimension in the feed-forward blocks. hidden_act (`str` or `callable`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout probability used between the two layers of the feed-forward blocks. initializer_range (`float`, *optional*, defaults to 0.1): The upper bound of the *uniform initializer* for initializing all weight matrices in attention layers. initializer_std (`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 (`float`, *optional*, defaults to 1e-09): The epsilon used by the layer normalization layers. pooling_type (`str`, *optional*, defaults to `"mean"`): Possible values are `"mean"` or `"max"`. The way pooling is performed at the beginning of each block. attention_type (`str`, *optional*, defaults to `"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 (`bool`, *optional*, defaults to `True`): Whether or not to separate the cls token when applying pooling. truncate_seq (`bool`, *optional*, defaults to `True`): 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 (`bool`, *optional*, defaults to `True`): Whether or not to apply the pooling only to the query or to query, key and values for the attention layers. """ model_type = "funnel" attribute_map = { "hidden_size": "d_model", "num_attention_heads": "n_head", } 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, 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, ): 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.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 super().__init__(**kwargs) @property def num_hidden_layers(self): return sum(self.block_sizes) @num_hidden_layers.setter def num_hidden_layers(self, value): raise NotImplementedError( "This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`." ) @property def num_blocks(self): return len(self.block_sizes) @num_blocks.setter def num_blocks(self, value): raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`.") __all__ = ["FunnelConfig"]