Spaces:
Sleeping
Sleeping
File size: 7,994 Bytes
6fc683c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
""" OpenAI GPT-2 configuration """
import logging
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
"gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json",
"gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-config.json",
"distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-config.json",
}
class GPT2Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a :class:`~transformers.GPT2Model`.
It is used to instantiate an GPT-2 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-2 `small <https://huggingface.co/gpt2>`__ architecture.
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 50257):
Vocabulary size of the GPT-2 model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.GPT2Model`.
n_positions (:obj:`int`, optional, defaults to 1024):
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 1024):
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.
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 16):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
summary_type (:obj:`string`, optional, defaults to "cls_index"):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.GPT2DoubleHeadsModel`.
Is 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 (GPT/GPT-2)
- 'attn' => Not implemented now, use multi-head attention
summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.GPT2DoubleHeadsModel`.
Add a projection after the vector extraction
summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.GPT2DoubleHeadsModel`.
'tanh' => add a tanh activation to the output, Other => no activation.
summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.GPT2DoubleHeadsModel`.
If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.GPT2DoubleHeadsModel`.
Add a dropout before the projection and activation
Example::
from transformers import GPT2Model, GPT2Config
# Initializing a GPT2 configuration
configuration = GPT2Config()
# Initializing a model from the configuration
model = GPT2Model(configuration)
# Accessing the model configuration
configuration = model.config
Attributes:
pretrained_config_archive_map (Dict[str, str]):
A dictionary containing all the available pre-trained checkpoints.
"""
pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "gpt2"
def __init__(
self,
vocab_size=50257,
n_positions=1024,
n_ctx=1024,
n_embd=768,
n_layer=12,
n_head=12,
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
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.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
|