# 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 # # 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. """ Transformer XL configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = { "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json", } class TransfoXLConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. 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 TransfoXL [transfo-xl-wt103](https://huggingface.co/transfo-xl-wt103) 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 267735): Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`TransfoXLModel`] or [`TFTransfoXLModel`]. cutoffs (`List[int]`, *optional*, defaults to `[20000, 40000, 200000]`): Cutoffs for the adaptive softmax. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the model's hidden states. d_embed (`int`, *optional*, defaults to 1024): Dimensionality of the embeddings n_head (`int`, *optional*, defaults to 16): 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 4096): Inner dimension in FF div_val (`int`, *optional*, defaults to 4): Divident value for adapative input and softmax pre_lnorm (`boolean`, *optional*, defaults to `False`): Whether or not to apply LayerNorm to the input instead of the output in the blocks. n_layer (`int`, *optional*, defaults to 18): Number of hidden layers in the Transformer encoder. mem_len (`int`, *optional*, defaults to 1600): Length of the retained previous heads. clamp_len (`int`, *optional*, defaults to 1000): Use the same pos embeddings after clamp_len. same_length (`boolean`, *optional*, defaults to `True`): Whether or not to use the same attn length for all tokens proj_share_all_but_first (`boolean`, *optional*, defaults to `True`): True to share all but first projs, False not to share. attn_type (`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 (`int`, *optional*, defaults to -1): Number of samples in the sampled softmax. adaptive (`boolean`, *optional*, defaults to `True`): Whether or not to use adaptive softmax. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. dropatt (`float`, *optional*, defaults to 0): The dropout ratio for the attention probabilities. untie_r (`boolean`, *optional*, defaults to `True`): Whether ot not to untie relative position biases. init (`str`, *optional*, defaults to `"normal"`): Parameter initializer to use. init_range (`float`, *optional*, defaults to 0.01): Parameters initialized by U(-init_range, init_range). proj_init_std (`float`, *optional*, defaults to 0.01): Parameters initialized by N(0, init_std) init_std (`float`, *optional*, defaults to 0.02): Parameters initialized by N(0, init_std) layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers Examples: ```python >>> from transformers import TransfoXLConfig, TransfoXLModel >>> # Initializing a Transformer XL configuration >>> configuration = TransfoXLConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = TransfoXLModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "transfo-xl" keys_to_ignore_at_inference = ["mems"] attribute_map = { "n_token": "vocab_size", "hidden_size": "d_model", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } 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, ): 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 super().__init__(eos_token_id=eos_token_id, **kwargs) @property def max_position_embeddings(self): # Message copied from Transformer-XL documentation logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.") return -1 @max_position_embeddings.setter def max_position_embeddings(self, value): # Message copied from Transformer-XL documentation raise NotImplementedError( f"The model {self.model_type} is one of the few models that has no sequence length limit." )