# 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
#
# 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.
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""" Salesforce CTRL configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
[docs]class CTRLConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a :class:`~transformers.CTRLModel` or a
:class:`~transformers.TFCTRLModel`. It is used to instantiate a 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 <https://huggingface.co/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 number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.CTRLModel` or
:class:`~transformers.TFCTRLModel`.
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 feed forward networks (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.
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 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"
keys_to_ignore_at_inference = ["past_key_values"]
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,
use_cache=True,
**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
self.use_cache = use_cache
@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