Source code for transformers.configuration_transfo_xl

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" Transformer XL configuration """

import warnings

from .configuration_utils import PretrainedConfig
from .utils import logging

logger = logging.get_logger(__name__)

    "transfo-xl-wt103": "",

[docs]class TransfoXLConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a :class:`~transformers.TransfoXLModel`. It is used to instantiate a Transformer XL 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 `Transformer XL <>`__ 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 267735): Vocabulary size of the Transformer XL model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.TransfoXLModel`. cutoffs (:obj:`List[int]`, optional, defaults to :obj:`[20000, 40000, 200000]`): Cutoffs for the adaptive softmax d_model (:obj:`int`, optional, defaults to 1024): Dimensionality of the model's hidden states. d_embed (:obj:`int`, optional, defaults to 1024): Dimensionality of the embeddings n_head (:obj:`int`, optional, defaults to 16): 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 4096): Inner dimension in FF div_val (:obj:`int`, optional, defaults to 4): Divident value for adapative input and softmax pre_lnorm (:obj:`boolean`, optional, defaults to :obj:`False`): Apply LayerNorm to the input instead of the output n_layer (:obj:`int`, optional, defaults to 18): Number of hidden layers in the Transformer encoder. mem_len (:obj:`int`, optional, defaults to 1600): Length of the retained previous heads clamp_len (:obj:`int`, optional, defaults to 1000): use the same pos embeddings after clamp_len same_length (:obj:`boolean`, optional, defaults to :obj:`True`): Use the same attn length for all tokens proj_share_all_but_first (:obj:`boolean`, optional, defaults to :obj:`True`): True to share all but first projs, False not to share. attn_type (:obj:`int`, optional, defaults to 0): Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al. sample_softmax (:obj:`int`, optional, defaults to -1): number of samples in sampled softmax adaptive (:obj:`boolean`, optional, defaults to :obj:`True`): use adaptive softmax dropout (:obj:`float`, optional, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. dropatt (:obj:`float`, optional, defaults to 0): The dropout ratio for the attention probabilities. untie_r (:obj:`boolean`, optional, defaults to :obj:`True`): Untie relative position biases init (:obj:`string`, optional, defaults to `normal`): Parameter initializer to use init_range (:obj:`float`, optional, defaults to 0.01): Parameters initialized by U(-init_range, init_range). proj_init_std (:obj:`float`, optional, defaults to 0.01): Parameters initialized by N(0, init_std) init_std (:obj:`float`, optional, defaults to 0.02): Parameters initialized by N(0, init_std) layer_norm_epsilon (:obj:`float`, optional, defaults to 1e-5): The epsilon to use in the layer normalization layers Example:: >>> from transformers import TransfoXLConfig, TransfoXLModel >>> # Initializing a Transformer XL configuration >>> configuration = TransfoXLConfig() >>> # Initializing a model from the configuration >>> model = TransfoXLModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "transfo-xl" def __init__( self, vocab_size=267735, cutoffs=[20000, 40000, 200000], d_model=1024, d_embed=1024, n_head=16, d_head=64, d_inner=4096, div_val=4, pre_lnorm=False, n_layer=18, mem_len=1600, clamp_len=1000, same_length=True, proj_share_all_but_first=True, attn_type=0, sample_softmax=-1, adaptive=True, dropout=0.1, dropatt=0.0, untie_r=True, init="normal", init_range=0.01, proj_init_std=0.01, init_std=0.02, layer_norm_epsilon=1e-5, eos_token_id=0, **kwargs ): if "tie_weight" in kwargs: warnings.warn( "The config parameter `tie_weight` is deprecated. Please use `tie_word_embeddings` instead.", FutureWarning, ) kwargs["tie_word_embeddings"] = kwargs["tie_weight"] super().__init__(eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.cutoffs = [] self.cutoffs.extend(cutoffs) if proj_share_all_but_first: self.tie_projs = [False] + [True] * len(self.cutoffs) else: self.tie_projs = [False] + [False] * len(self.cutoffs) self.d_model = d_model self.d_embed = d_embed self.d_head = d_head self.d_inner = d_inner self.div_val = div_val self.pre_lnorm = pre_lnorm self.n_layer = n_layer self.n_head = n_head self.mem_len = mem_len self.same_length = same_length self.attn_type = attn_type self.clamp_len = clamp_len self.sample_softmax = sample_softmax self.adaptive = adaptive self.dropout = dropout self.dropatt = dropatt self.untie_r = untie_r self.init = init self.init_range = init_range self.proj_init_std = proj_init_std self.init_std = init_std self.layer_norm_epsilon = layer_norm_epsilon @property def max_position_embeddings(self): # Message copied from Transformer-XL documentation"The model {self.model_type} is one of the few models that has no sequence length limit.") return -1 @property def n_token(self): # Backward compatibility return self.vocab_size @n_token.setter def n_token(self, value): # Backward compatibility self.vocab_size = value @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 self.n_layer