Source code for transformers.models.openai.configuration_openai

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# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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""" OpenAI GPT configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)


[docs]class OpenAIGPTConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a :class:`~transformers.OpenAIGPTModel` or a :class:`~transformers.TFOpenAIGPTModel`. It is used to instantiate a GPT 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 `GPT <>`__ architecture from OpenAI. 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 40478): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.OpenAIGPTModel` or :class:`~transformers.TFOpenAIGPTModel`. n_positions (:obj:`int`, `optional`, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_ctx (:obj:`int`, `optional`, defaults to 512): Dimensionality of the causal mask (usually same as n_positions). n_embd (:obj:`int`, `optional`, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. afn (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. resid_pdrop (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (:obj:`int`, `optional`, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5): The epsilon to use in the layer normalization layers initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. predict_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not special tokens should be predicted when the model has a language modeling head. summary_type (:obj:`str`, `optional`, defaults to :obj:`"cls_index"`): Argument used when doing sequence summary, used in the models :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`. Has to be one of the following options: - :obj:`"last"`: Take the last token hidden state (like XLNet). - :obj:`"first"`: Take the first token hidden state (like BERT). - :obj:`"mean"`: Take the mean of all tokens hidden states. - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - :obj:`"attn"`: Not implemented now, use multi-head attention. summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`): Argument used when doing sequence summary, used in the models :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`. Whether or not to add a projection after the vector extraction. summary_activation (:obj:`str`, `optional`): Argument used when doing sequence summary, used in the models :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`. Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (:obj:`bool`, `optional`, defaults to :obj:`True`): Argument used when doing sequence summary, used in the models :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`. Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes. summary_first_dropout (:obj:`float`, `optional`, defaults to 0.1): Argument used when doing sequence summary, used in the models :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`. The dropout ratio to be used after the projection and activation. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). Examples:: >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel >>> # Initializing a GPT configuration >>> configuration = OpenAIGPTConfig() >>> # Initializing a model from the configuration >>> model = OpenAIGPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "openai-gpt" attribute_map = { "max_position_embeddings": "n_positions", "hidden_size": "n_embd", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=40478, n_positions=512, n_ctx=512, n_embd=768, n_layer=12, n_head=12, afn="gelu", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, predict_special_tokens=True, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, **kwargs ): self.vocab_size = vocab_size self.n_ctx = n_ctx self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.afn = afn self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.predict_special_tokens = predict_special_tokens self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels super().__init__(**kwargs)