# 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. """ XLNet configuration""" import warnings from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) from ..deprecated._archive_maps import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 class XLNetConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`XLNetModel`] or a [`TFXLNetModel`]. It is used to instantiate a XLNet 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 [xlnet/xlnet-large-cased](https://huggingface.co/xlnet/xlnet-large-cased) 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 32000): Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XLNetModel`] or [`TFXLNetModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. n_layer (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. d_inner (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. ff_activation (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. untie_r (`bool`, *optional*, defaults to `True`): Whether or not to untie relative position biases attn_type (`str`, *optional*, defaults to `"bi"`): The attention type used by the model. Set `"bi"` for XLNet, `"uni"` for Transformer-XL. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. mem_len (`int` or `None`, *optional*): The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous forward pass won't be re-computed. See the [quickstart](https://huggingface.co/transformers/quickstart.html#using-the-past) for more information. reuse_len (`int`, *optional*): The number of tokens in the current batch to be cached and reused in the future. bi_data (`bool`, *optional*, defaults to `False`): Whether or not to use bidirectional input pipeline. Usually set to `True` during pretraining and `False` during finetuning. clamp_len (`int`, *optional*, defaults to -1): Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping. same_length (`bool`, *optional*, defaults to `False`): Whether or not to use the same attention length for each token. summary_type (`str`, *optional*, defaults to "last"): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`boo`, *optional*, defaults to `True`): Used in the sequence classification and multiple choice models. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_last_dropout (`float`, *optional*, defaults to 0.1): Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. start_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. end_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. use_mems_eval (`bool`, *optional*, defaults to `True`): Whether or not the model should make use of the recurrent memory mechanism in evaluation mode. use_mems_train (`bool`, *optional*, defaults to `False`): Whether or not the model should make use of the recurrent memory mechanism in train mode. For pretraining, it is recommended to set `use_mems_train` to `True`. For fine-tuning, it is recommended to set `use_mems_train` to `False` as discussed [here](https://github.com/zihangdai/xlnet/issues/41#issuecomment-505102587). If `use_mems_train` is set to `True`, one has to make sure that the train batches are correctly pre-processed, *e.g.* `batch_1 = [[This line is], [This is the]]` and `batch_2 = [[ the first line], [ second line]]` and that all batches are of equal size. Examples: ```python >>> from transformers import XLNetConfig, XLNetModel >>> # Initializing a XLNet configuration >>> configuration = XLNetConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = XLNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xlnet" keys_to_ignore_at_inference = ["mems"] attribute_map = { "n_token": "vocab_size", # Backward compatibility "hidden_size": "d_model", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=32000, d_model=1024, n_layer=24, n_head=16, d_inner=4096, ff_activation="gelu", untie_r=True, attn_type="bi", initializer_range=0.02, layer_norm_eps=1e-12, dropout=0.1, mem_len=512, reuse_len=None, use_mems_eval=True, use_mems_train=False, bi_data=False, clamp_len=-1, same_length=False, summary_type="last", summary_use_proj=True, summary_activation="tanh", summary_last_dropout=0.1, start_n_top=5, end_n_top=5, pad_token_id=5, bos_token_id=1, eos_token_id=2, **kwargs, ): """Constructs XLNetConfig.""" self.vocab_size = vocab_size self.d_model = d_model self.n_layer = n_layer self.n_head = n_head if d_model % n_head != 0: raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0") if "d_head" in kwargs: if kwargs["d_head"] != d_model // n_head: raise ValueError( f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" ) self.d_head = d_model // n_head self.ff_activation = ff_activation self.d_inner = d_inner self.untie_r = untie_r self.attn_type = attn_type self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.dropout = dropout self.mem_len = mem_len self.reuse_len = reuse_len self.bi_data = bi_data self.clamp_len = clamp_len self.same_length = same_length self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_last_dropout = summary_last_dropout self.start_n_top = start_n_top self.end_n_top = end_n_top self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.eos_token_id = eos_token_id if "use_cache" in kwargs: warnings.warn( "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`" " instead.", FutureWarning, ) use_mems_eval = kwargs["use_cache"] self.use_mems_eval = use_mems_eval self.use_mems_train = use_mems_train super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @property def max_position_embeddings(self): 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." )