# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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""" Transformer XL configuration """
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import sys
from io import open
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-config.json",
}
[docs]class TransfoXLConfig(PretrainedConfig):
"""Configuration class to store the configuration of a `TransfoXLModel`.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `TransfoXLModel` or a configuration json file.
cutoffs: cutoffs for the adaptive softmax
d_model: Dimensionality of the model's hidden states.
d_embed: Dimensionality of the embeddings
d_head: Dimensionality of the model's heads.
div_val: divident value for adapative input and softmax
pre_lnorm: apply LayerNorm to the input instead of the output
d_inner: Inner dimension in FF
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
tgt_len: number of tokens to predict
ext_len: length of the extended context
mem_len: length of the retained previous heads
same_length: use the same attn length for all tokens
proj_share_all_but_first: True to share all but first projs, False not to share.
attn_type: attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
clamp_len: use the same pos embeddings after clamp_len
sample_softmax: number of samples in sampled softmax
adaptive: use adaptive softmax
tie_weight: tie the word embedding and softmax weights
dropout: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
dropatt: The dropout ratio for the attention probabilities.
untie_r: untie relative position biases
embd_pdrop: The dropout ratio for the embeddings.
init: parameter initializer to use
init_range: parameters initialized by U(-init_range, init_range).
proj_init_std: parameters initialized by N(0, init_std)
init_std: parameters initialized by N(0, init_std)
"""
pretrained_config_archive_map = TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file=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,
tgt_len=128,
ext_len=0,
mem_len=1600,
clamp_len=1000,
same_length=True,
proj_share_all_but_first=True,
attn_type=0,
sample_softmax=-1,
adaptive=True,
tie_weight=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,
**kwargs):
"""Constructs TransfoXLConfig.
"""
super(TransfoXLConfig, self).__init__(**kwargs)
self.n_token = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
self.cutoffs = []
self.cutoffs.extend(cutoffs)
self.tie_weight = tie_weight
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.tgt_len = tgt_len
self.ext_len = ext_len
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
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif not isinstance(vocab_size_or_config_json_file, int):
raise ValueError("First argument must be either a vocabulary size (int)"
" or the path to a pretrained model config file (str)")
@property
def max_position_embeddings(self):
return self.tgt_len + self.ext_len + self.mem_len
@property
def vocab_size(self):
return self.n_token
@vocab_size.setter
def vocab_size(self, value):
self.n_token = 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