# coding=utf-8
# Copyright 2018 The OpenAI Team Authors 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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""" OpenAI GPT configuration """
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import sys
from io import open
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json"
}
[docs]class OpenAIGPTConfig(PretrainedConfig):
"""
Configuration class to store the configuration of a `OpenAIGPTModel`.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `OpenAIGPTModel` or a configuration json file.
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
n_embd: Dimensionality of the embeddings and hidden states.
n_layer: Number of hidden layers in the Transformer encoder.
n_head: Number of attention heads for each attention layer in
the Transformer encoder.
afn: The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
resid_pdrop: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attn_pdrop: The dropout ratio for the attention
probabilities.
embd_pdrop: The dropout ratio for the embeddings.
layer_norm_epsilon: epsilon to use in the layer norm layers
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
predict_special_tokens: should we predict special tokens (when the model has a LM head)
"""
pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size_or_config_json_file=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,
num_labels=1,
summary_type='cls_index',
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
**kwargs
):
"""Constructs OpenAIGPTConfig.
"""
super(OpenAIGPTConfig, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
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.num_labels = num_labels
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
else:
raise ValueError(
"First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)"
)
@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