167 lines
7.6 KiB
Python
167 lines
7.6 KiB
Python
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# coding=utf-8
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# Copyright 2020, Hugging Face
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Funnel Transformer model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class FunnelConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FunnelModel`] or a [`TFBertModel`]. It is used to
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instantiate a Funnel Transformer model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the Funnel
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Transformer [funnel-transformer/small](https://huggingface.co/funnel-transformer/small) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented
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by the `inputs_ids` passed when calling [`FunnelModel`] or [`TFFunnelModel`].
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block_sizes (`List[int]`, *optional*, defaults to `[4, 4, 4]`):
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The sizes of the blocks used in the model.
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block_repeats (`List[int]`, *optional*):
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If passed along, each layer of each block is repeated the number of times indicated.
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num_decoder_layers (`int`, *optional*, defaults to 2):
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The number of layers in the decoder (when not using the base model).
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d_model (`int`, *optional*, defaults to 768):
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Dimensionality of the model's hidden states.
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n_head (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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d_head (`int`, *optional*, defaults to 64):
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Dimensionality of the model's heads.
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d_inner (`int`, *optional*, defaults to 3072):
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Inner dimension in the feed-forward blocks.
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hidden_act (`str` or `callable`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability used between the two layers of the feed-forward blocks.
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initializer_range (`float`, *optional*, defaults to 0.1):
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The upper bound of the *uniform initializer* for initializing all weight matrices in attention layers.
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initializer_std (`float`, *optional*):
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The standard deviation of the *normal initializer* for initializing the embedding matrix and the weight of
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linear layers. Will default to 1 for the embedding matrix and the value given by Xavier initialization for
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linear layers.
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layer_norm_eps (`float`, *optional*, defaults to 1e-09):
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The epsilon used by the layer normalization layers.
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pooling_type (`str`, *optional*, defaults to `"mean"`):
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Possible values are `"mean"` or `"max"`. The way pooling is performed at the beginning of each block.
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attention_type (`str`, *optional*, defaults to `"relative_shift"`):
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Possible values are `"relative_shift"` or `"factorized"`. The former is faster on CPU/GPU while the latter
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is faster on TPU.
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separate_cls (`bool`, *optional*, defaults to `True`):
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Whether or not to separate the cls token when applying pooling.
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truncate_seq (`bool`, *optional*, defaults to `True`):
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When using `separate_cls`, whether or not to truncate the last token when pooling, to avoid getting a
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sequence length that is not a multiple of 2.
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pool_q_only (`bool`, *optional*, defaults to `True`):
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Whether or not to apply the pooling only to the query or to query, key and values for the attention layers.
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"""
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model_type = "funnel"
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attribute_map = {
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"hidden_size": "d_model",
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"num_attention_heads": "n_head",
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}
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def __init__(
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self,
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vocab_size=30522,
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block_sizes=[4, 4, 4],
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block_repeats=None,
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num_decoder_layers=2,
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d_model=768,
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n_head=12,
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d_head=64,
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d_inner=3072,
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hidden_act="gelu_new",
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hidden_dropout=0.1,
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attention_dropout=0.1,
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activation_dropout=0.0,
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initializer_range=0.1,
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initializer_std=None,
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layer_norm_eps=1e-9,
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pooling_type="mean",
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attention_type="relative_shift",
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separate_cls=True,
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truncate_seq=True,
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pool_q_only=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.block_sizes = block_sizes
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self.block_repeats = [1] * len(block_sizes) if block_repeats is None else block_repeats
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assert len(block_sizes) == len(
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self.block_repeats
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), "`block_sizes` and `block_repeats` should have the same length."
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self.num_decoder_layers = num_decoder_layers
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self.d_model = d_model
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self.n_head = n_head
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self.d_head = d_head
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self.d_inner = d_inner
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self.hidden_act = hidden_act
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.initializer_range = initializer_range
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self.initializer_std = initializer_std
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self.layer_norm_eps = layer_norm_eps
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assert pooling_type in [
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"mean",
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"max",
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], f"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
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self.pooling_type = pooling_type
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assert attention_type in [
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"relative_shift",
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"factorized",
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], f"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
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self.attention_type = attention_type
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self.separate_cls = separate_cls
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self.truncate_seq = truncate_seq
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self.pool_q_only = pool_q_only
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super().__init__(**kwargs)
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@property
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def num_hidden_layers(self):
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return sum(self.block_sizes)
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@num_hidden_layers.setter
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def num_hidden_layers(self, value):
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raise NotImplementedError(
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"This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."
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)
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@property
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def num_blocks(self):
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return len(self.block_sizes)
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@num_blocks.setter
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def num_blocks(self, value):
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raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`.")
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