Source code for transformers.configuration_ctrl

# coding=utf-8
# Copyright 2018 Salesforce and 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,
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""" Salesforce CTRL configuration """

from .configuration_utils import PretrainedConfig
from .utils import logging

logger = logging.get_logger(__name__)


[docs]class CTRLConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a :class:`~transformers.CTRLModel`. It is used to instantiate an CTRL 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 `ctrl <>`__ architecture from SalesForce. 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 246534): Vocabulary size of the CTRL model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.CTRLModel`. n_positions (:obj:`int`, optional, defaults to 256): 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 256): Dimensionality of the causal mask (usually same as n_positions). n_embd (:obj:`int`, optional, defaults to 1280): Dimensionality of the embeddings and hidden states. dff (:obj:`int`, optional, defaults to 8192): Dimensionality of the inner dimension of the FFN. n_layer (:obj:`int`, optional, defaults to 48): Number of hidden layers in the Transformer encoder. n_head (:obj:`int`, optional, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. 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-6): 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. Example:: >>> from transformers import CTRLModel, CTRLConfig >>> # Initializing a CTRL configuration >>> configuration = CTRLConfig() >>> # Initializing a model from the configuration >>> model = CTRLModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "ctrl" def __init__( self, vocab_size=246534, n_positions=256, n_ctx=256, n_embd=1280, dff=8192, n_layer=48, n_head=16, resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-6, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, **kwargs ): super().__init__(**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.dff = dff 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.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 @property def max_position_embeddings(self): return self.n_positions @property def hidden_size(self): return self.n_embd @property def num_attention_heads(self): return self.n_head @property def num_hidden_layers(self): return self.n_layer